1236 results about "Topological optimization" patented technology

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 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 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 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 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 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.

The invention relates to a network topology dynamic optimization method and equipment for large-scale power supply and distribution equipment networking. The method comprises the following steps: step S101, real-time data acquisition and state sensing; step S102, carrying out network topology modeling and performance evaluation; step S103, dynamic risk assessment and optimization target generation; s104, carrying out topological optimization decision making based on a feasibility maintenance type genetic algorithm; step S105, carrying out optimal strategy verification and seamless switching; selecting an optimal network topology reconstruction scheme from the Pareto optimal solution set according to a preset decision strategy; and after the reconstruction scheme is verified on a control level, generating an equipment cascade and open circuit control instruction sequence, and guiding related nodes to complete undisturbed switching of the network topology in a preset time window through a distributed cooperative control mechanism. According to the method, generation of invalid solutions can be avoided, and the convergence efficiency of a large-scale network topology optimization algorithm is remarkably improved.

The invention provides a microwave network topology optimization method and system based on a graph neural network, and the method comprises the steps: firstly obtaining a real-time operation data set of a plurality of microwave nodes in a microwave communication network, covering a link quality index, a node load state parameter and an environment interference coefficient, and then carrying out the dynamic feature extraction of the real-time operation data set, the method comprises the steps of obtaining a dynamic propagation feature set containing inter-node signal attenuation features and the like, calling a preset graph neural network model to carry out topological relation modeling on the dynamic propagation feature set, generating an association weight matrix of microwave nodes, and carrying out iterative matching calculation based on the association weight matrix and a preset topological optimization constraint condition, generating a microwave network topology optimization strategy; and finally, the microwave network topology optimization strategy is sent to a microwave network management system to trigger link reconstruction operation, and the microwave network topology can be effectively optimized.

The invention relates to the technical field of Internet of Things equipment, in particular to an Internet of Things equipment management method, computing platform and system, and the method comprises the steps: obtaining the state data of the Internet of Things equipment, carrying out the health scoring of the equipment periodically through a neural network model, and detecting the potential fault risk; constructing a task allocation strategy based on the equipment health score, and dynamically transferring a task to health equipment when the equipment has a potential fault risk; detecting the communication state of the potential fault risk equipment; potential fault risk equipment in a normal communication state is degraded into temporary communication equipment, and the health equipment is assisted to complete communication; and optimizing network topology and communication load by using temporary communication equipment based on a dynamic topology optimization algorithm driven by a graph model. According to the method, the reliability, the task completion rate and the network communication performance of the Internet of Things equipment can be effectively improved.

The invention discloses a lightweight design method for a thin-wall structure containing a flow channel based on additive manufacturing, and belongs to the technical field of additive manufacturing, and the lightweight design method comprises the following steps: S100, analyzing functional requirements, and constructing a functional requirement model containing the flow channel; s200, a printing direction and additive manufacturing constraint conditions are set, topological optimization is executed on the function demand model, and a preliminary topological structure is obtained; s300, carrying out geometric reconstruction on a topological optimization result to obtain a three-dimensional model suitable for additive manufacturing; s400, carrying out parameter optimization design; s500, the section shape and size of the runner are optimized based on fluid performance requirements; and S600, additive manufacturing manufacturability inspection is conducted on the structure with the section shape and size optimized. And S700, the model obtained after additive manufacturing manufacturability optimization is analyzed and checked. The technical problems that in the prior art, the design requirement of a thin-wall structure containing a runner cannot be met, and the forming quality and the section shape of the inner surface of the runner are difficult to accurately control can be solved.

The invention belongs to the field of router protection, and particularly provides an energy router protection system and method based on deep reinforcement learning and virtual impedance collaboration, and the method comprises the steps that a high-speed sensing and monitoring unit collects and preprocesses the data of each power branch; the main control unit operates deep Q network fault reasoning, and a fault criterion threshold value is finely adjusted in combination with online Q-learning; calling a virtual impedance injection module, and superposing compensation voltage on the PWM reference signal to realize injection; a topology self-reconfiguration module is triggered synchronously, a fault branch is isolated, and a standby branch is switched to maintain power supply of a key load; the thermal management regulation and control module deploys load shedding through PID load shedding control and a temperature-current permission curve based on the thermal RC equivalent model and the real-time temperature; and synchronously issuing a related instruction by means of the CAN-FD bus. According to the method, the deep reinforcement learning algorithm and the virtual impedance technology are combined, so that adaptive protection and topological optimization of the energy router under complex working conditions are realized.

The invention relates to the technical field of structural design, and discloses a topology and size joint optimization design method and system for a hierarchical composite structure, and the method comprises the following steps: dividing a structural domain into a plurality of hierarchical sub-domains, defining the size constraint range of each sub-domain, and distributing a multi-phase material for each sub-domain; grid discretization processing is carried out on the structural domain, a topological design variable is given to each discrete unit, acoustic unit modeling is carried out on the sound field domain, sound pressure boundary conditions are defined, and coupling boundary conditions are applied to the interface of the sound field domain and the structural domain; solving an acoustic-structure coupling equation based on a finite element method, calculating an objective function and constraint conditions, analyzing the sensitivity of the objective function to topological variables by adopting an adjoint method, and iteratively updating the topological design variables through an optimization algorithm until a convergence criterion is met; and on the basis of a topological optimization result, constructing a neural network agent model of an input layer, a hidden layer and an output layer, and carrying out global optimization on sub-domain size parameters by adopting a genetic algorithm so as to minimize a target function.

The invention discloses an unmanned aerial vehicle cluster topology dynamic optimization method and system under a complex terrain, and the method comprises the steps: collecting the terrain three-dimensional modeling data and real-time environment parameters of a canyon or an urban agglomeration, and combining the position of an unmanned aerial vehicle cluster and communication link state data to construct a dynamically updated twin scene model; inputting the twin scene model into a distributed reinforcement learning model, and generating a topological optimization strategy of unmanned aerial vehicle relay node site selection and link priority allocation through multi-agent asynchronous decision; according to the relay node position and the link direction in the topological optimization strategy, generating an antenna parameter-topological structure collaborative adaptation scheme; based on the collaborative adaptation scheme, cluster communication quality and navigation continuity indexes are collected in real time, and a cluster topology dynamic optimization result adaptive to complex terrain changes is generated. By utilizing the embodiment of the invention, the cluster communication and navigation performance under the complex terrain can be dynamically improved, and the stable execution of the cluster task is ensured.

The invention discloses an automobile chassis lightweight structure design method based on topological optimization, and relates to the technical field of lightweight design. Constructing a macro-micro coupling model, and simulating and quantifying material parameter fluctuation by using Monte Carlo; establishing a rigid-flexible coupling multi-body dynamic model, simulating working conditions such as braking and turning, and generating a load spectrum by using a rain flow counting method; constructing a multi-objective function containing light weight, rigidity and modality, and solving manufacturing constraints such as pattern draft and the like by using an NSGA-II (Non-dominated Sorting Genetic Algorithm-II) algorithm; dividing steel, aluminum and carbon fiber material domains; and fusing bench test data to correct the model. According to the method, multi-scale collaborative optimization is realized, dynamic loads are accurately mapped, and light weight and performance are balanced; the connection reliability is improved through the multi-material gradient design; manufacturability is ensured through feature recognition and process verification; the batch consistency is guaranteed by digital twinning and robustness optimization; the chassis design efficiency and quality are integrally improved, and the service life is prolonged.

The invention provides a multi-arm drill jumbo self-adaptive drilling track planning method based on visual navigation, which relates to the technical field of visual identification, and comprises the following steps: acquiring image data, identifying a working surface change area, generating a drilling grid with topological optimization, determining a target drilling position set and a drilling direction set, and performing drilling grouping. Generating a space-time conflict constraint for each group of drill holes, and iteratively optimizing to determine a multi-arm collaborative operation sequence; in the process of executing the multi-arm operation sequence, a structured light projection technology and a visual phase shift method are adopted to obtain a deformation field of the surface of a rock mass, rock mass response characteristics including elastic deformation, plastic deformation and abnormal deformation information are established, and according to the rock mass response characteristics, a progressive B-spline track adjustment algorithm is adopted to adjust the drilling track in real time.

The invention discloses a power distribution network topology optimization method and system, and relates to the technical field of power distribution network topology optimization, and the system comprises a dynamic health degree obtaining module, an operation risk cost module, a topology optimization module and a maintenance scheme generation module. The dynamic health degree index and the operation risk cost of the key electrical equipment are incorporated into the topological optimization objective function, and the weight is dynamically adjusted according to the health state of the equipment, so that the problem that in the prior art, the actual health state of the equipment is ignored, so that the degradation of the equipment is possibly accelerated by the optimization scheme is effectively solved; therefore, the real-time health condition evaluation information of the key equipment can be effectively integrated, the short-term operation economic index and the long-term operation reliability of the equipment are dynamically balanced in the optimization process, the unexpected fault risk of the equipment is reduced, and the overall reliability and safety of operation of the power distribution network are improved.

The invention relates to the technical field of intelligent mobile robot path planning, in particular to a dynamic sensing path planning method for a mobile robot in an unknown environment. According to the method, an environment complexity model is established based on the obstacle density and the dynamic activity, and map partition and obstacle avoidance judgment are adaptively adjusted according to the environment complexity model; carrying out incremental updating on the near, middle and far sub-regions based on the point cloud similarity; extracting contours of the obstacle point clouds and carrying out topological optimization; dynamic candidate points are identified through double marking of rays and scanning points, and collision time is predicted and estimated in combination with spatial clustering and trajectory; and according to the collision time, hierarchical obstacle avoidance and local or global re-planning are implemented, and a repaired path is smoothed. According to the method, the path planning efficiency and the real-time obstacle avoidance capability of the mobile robot in a dynamic complex environment are remarkably improved, the consumption of computing resources is reduced, and the method is suitable for application scenes needing high dynamic response, such as warehouse logistics and unmanned driving.

A method, computer program product, and computer system for network topology optimization. A graph convolutional network (GCN) model is trained using training network topology datasets as input. Each training network topology dataset includes: (i) a specified network topology and an associated optimal network topology and an optimality function value for the optimal network topology, (ii) relative weights of optimality parameters including performance, availability, and scalability, and (iii) an identification of an optimality function of the optimality parameters weighted by the relative weights. The specified network topology includes components, relationships between the components, and metadata pertaining to the components. Each output node in an output layer of the GCN model includes an optimality function value for a different candidate network topology. One of the output nodes identifies an optimum network topology relative to the specified network topology as being the candidate network topology having a highest optimality function value.

The invention belongs to the related technical field of heat dissipation of electronic equipment, and discloses a liquid cooling device and a preparation method and application thereof, and the method comprises the steps: S1, enabling a control equation of the liquid cooling device to be equivalent to a continuity equation, an N-S equation and a heat transfer equation based on equivalent parameters; s2, taking the minimum average temperature and energy dissipation of the whole design domain of the liquid cooling device as a target function, taking the volume fraction of the fluid domain as a constraint condition, and constructing a topological optimization model based on a continuity equation, an N-S equation and a heat transfer equation; s3, an optimization solver is adopted to carry out iterative solution on the topological optimization model, whether a result meets a convergence condition or not is judged, if yes, a topological structure is output, and the step S4 is executed; otherwise, updating the design variable of the topological optimization model, and turning to the step S1; and S4, the three-dimensional model of the liquid cooling device is corrected based on the obtained topological structure, and then blowing forming is carried out based on the corrected three-dimensional model to obtain the liquid cooling device. According to the invention, the cost is reduced.