1011 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 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 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.

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 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 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 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 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 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.

The invention provides an aluminum alloy frame multistage collaborative topological optimization method based on Internet of Vehicles big data, which is applied to the technical field of new energy vehicle lightweight and comprises the following steps: fusing subjective and objective weights to generate a comprehensive weight; a compromise planning method is adopted to construct a multiplex topological optimization objective function; performing first-stage frame overall topological optimization through an SIMP variable density method to determine the number of cross beams and spatial layout; and constructing a rigid-flexible coupling virtual prototype to extract an equivalent static load at the joint of the longitudinal / cross beam, and carrying out second-stage section topological optimization in combination with extrusion process constraint to obtain an optimal section configuration of the longitudinal / cross beam. Through a multi-stage optimization framework of data driving weight distribution, overall layout optimization and local section collaborative design, the lightweight rate of the frame is improved by 15%-22%, the bending rigidity and the torsional rigidity are improved by 12%-18% and 10%-15% respectively, and meanwhile the technological feasibility of aluminum alloy extrusion forming is guaranteed.

The invention relates to a production workshop carbon flow twinborn mapping method, and belongs to the technical field of carbon emission optimization. The method comprises the steps of collecting production workshop data in real time to perform multi-modal data fusion; constructing a carbon flow dynamic accounting mechanism model, and calculating the real-time carbon emission intensity of the process; constructing a carbon flow intensity tensor model, extracting multi-granularity carbon flow features based on the carbon flow intensity tensor model, and realizing real-time digital twinborn deduction of carbon flow propagation by using an intelligent prediction algorithm; establishing a differential equation to describe double-flow real-time interaction of the carbon flow and the value flow, constructing a carbon value incidence matrix, and performing carbon flow value analysis; and on the basis of the carbon value incidence matrix, a space-time carbon chain-oriented cooperative adjustment strategy is dynamically generated through a multi-objective optimization algorithm, a double-layer topological optimization model is constructed, and a closed-loop feedback mechanism is introduced to drive the economic sustainability of the control strategy in an industrial environment. The workshop carbon footprint can be displayed in real time, the workshop carbon emission condition can be truly reflected, and the carbon emission data can be analyzed and optimized in time.

The invention relates to a radial frequency-doubled sound filtering metamaterial and a topological optimization design method thereof.The radial frequency-doubled sound filtering metamaterial comprises scatterers which are arranged on a base body and distributed in a concentric circle mode, and the topological optimization design method comprises the following steps that a two-dimensional axial symmetry model is established, the two-dimensional axisymmetric model corresponds to the cross section of the radial frequency-doubled sound filtering metamaterial in the radius direction and comprises periodically arranged unit structures, and each unit structure comprises a main board and a square design domain; a Floquet periodic boundary condition is applied to parallel boundaries of a unit structure in the radius direction, other boundaries are set as free boundaries, a genetic algorithm is adopted to carry out iterative optimization on the material distribution topology of a square design domain, the optimal material distribution topology of the square design domain is obtained, and then the final optimized shape of the radial double-frequency sound filtering metamaterial is obtained. Compared with the prior art, the invention has the advantages of capability of realizing omni-directional sound wave frequency doubling filtering, high design reliability and the like.

To create high-performance metasurface devices (110) in an inverse design process over a large design space (100), a deep neural network may be used as a surrogate model in lieu of a full physics simulation to more efficiently predict figures of merit for given input metasurface topologies during the iterative topology optimization. The neural network may also serve to efficiently compute, via backpropagation, gradients of the figures of merit with respect to design parameters, as are used to update the topology in each iteration.

The invention provides a finite element analysis method for a competition-level site cross-country motorcycle frame, and belongs to the technical field of crossing of computer-aided engineering and vehicle structure design. The method comprises the following steps: performing parametric modeling on a frame main bearing structure based on CATIA software, and simplifying non-functional characteristics; importing the model into ANSYS Workbench, generating a high-precision finite element model by adopting a grid division strategy of mixing tetrahedron and hexahedron units, and considering the calculation efficiency and precision; defining material characteristics; a limit load working condition is simulated through statics analysis, a displacement cloud picture and a stress cloud picture are combined to be compared with the yield strength of a material, and topological optimization is conducted on a weak area; meanwhile, the inherent frequency of the frame is extracted through modal analysis, and the resonance risk is avoided. According to the method, statics analysis and modal analysis are carried out on the frame through cross-platform co-simulation, the structural reliability is evaluated, and the method is suitable for efficient development and performance optimization of competition-level cross-country motorcycle frames.

The invention discloses a method and a system for designing a fluid flow channel of a calcium-based thermochemical heat storage fixed bed reactor based on topological optimization and a storage medium. The method comprises the following steps: S1, constructing a geometric model of a design domain of the reactor; s2, constructing an objective function and establishing a two-dimensional topological optimization mathematical model based on a control equation of conjugate heat transfer and fluid flow; s3, solving the mathematical model to obtain an optimal fluid flow channel two-dimensional topological structure; s4, performing geometric reconstruction on the two-dimensional topological structure obtained in the step S3, converting the two-dimensional topological structure into a Bezier curve, and integrating the Bezier curve into a geometric model corresponding to the original design domain; s5, establishing a finite element model of the reactor; s6, temperature field and pressure field distribution is calculated based on CFD simulation, whether the heat storage performance target is met or not is judged, and if not, corresponding control parameters are adjusted to return to the step S3 until the heat storage performance target is met; the device can optimize the heat transfer and mass transfer performance of the reactor at the same time, and the optimization design efficiency is high.

The invention relates to the technical field of operation machinery, and provides a topological optimization method applied to a suspension arm, the suspension arm, a hoisting arm frame device and a self-loading and self-unloading transport vehicle, and the method comprises the following steps: establishing an initial model of the suspension arm, dividing grids based on the initial model, and defining a design domain; applying constraint conditions to the initial model of the suspension arm in a corresponding design domain; determining a topological optimization method of the suspension arm according to the constraint conditions, and setting corresponding initial topological optimization parameters; based on the initial topological optimization parameters, sensitivity analysis and optimization iterative calculation are carried out on the topological optimization finite element model, and target topological optimization parameters are obtained; obtaining a model of the target suspension arm based on the target topological optimization parameters; and extracting the optimized density distribution for checking, and carrying out finite element analysis on the sealing distribution which does not meet the requirement again until a manufacturable geometric model is generated. On the premise that the mechanical property is guaranteed, the balance of light weight and high strength of the suspension arm can be achieved through topological optimization design.

The invention discloses a hydrogen fuel cell ship power switching system, which relates to the technical field of hydrogen fuel cell ship hybrid power, and comprises a topological optimization module for optimizing a series-parallel topological structure of a fuel cell stack through a quantum annealing algorithm based on a multi-modal data set, the reconfigurable bipolar plate magnetic control switch is triggered to execute millisecond topology switching, and a topology state coding signal is synchronously generated; and the compensation module is used for inputting the topological state coding signal into the LSTM model, predicting a fault risk value, correcting the time step length of a leakage diffusion equation according to the real-time switching frequency of a magnetic control switch, and calculating a ship inclination angle correction term according to the corrected time step length. According to the invention, through an accurate fault prediction and compensation mechanism, operation interruption and maintenance cost caused by unpredictable faults are greatly reduced.

The invention relates to the field of trajectory tracking, and discloses a gun trajectory tracking system and method fused with multi-source localization, the system comprises a satellite positioning module, an RFID node network, an inertial measurement unit (IMU) and a processing unit, the processing unit determines an open area, a semi-shielded area or a full-shielded area in real time through an environment classification sub-module, based on a dynamic weight distribution strategy, the fusion proportion of the satellite data, the RFID data and the I MU data is adjusted; synthesizing multi-source positioning data by adopting a weighted fusion algorithm, and outputting a real-time gun trajectory; and when the RFID node is located in a full-shielding area, the topological optimization calibration sub-module is combined with the RFID node topological relation to correct the accumulated error of the I MU, and weight distribution is dynamically fed back and adjusted based on the calibration error. According to the method, the problems of track interruption and drifting caused by failure of a single positioning source or inertial navigation accumulative errors in a complex shielding scene are solved, and high-robustness continuous tracking of gun tracks in multiple scenes such as cities, fields and indoors is realized.

The invention relates to the technical field of engineering mechanical structure optimization, in particular to an excavator structure topological optimization method and system based on real-time stress field reconstruction. The method comprises the following steps of: designing finite element analysis software, modeling and simulating in the finite element analysis software to obtain virtual data, designing a Kalman filter to carry out error correction on the virtual data and actual test data, training the Kalman filter by utilizing the corrected data to obtain prediction of real-time stress field distribution, and further, establishing a topological optimization model of an excavator structure, according to the method, material attributes are coded into a weight matrix in a neural network, and a stress constraint partial differential equation is used as a constraint condition, so that high-precision reconstruction of the stress field of the movable arm of the excavator under an actual working condition is realized, and an accurate boundary condition is provided for topological optimization.

The invention provides an intelligent space sensing dynamic topology optimization method and system based on electromagnetic detection, and the method comprises the steps: firstly obtaining an electromagnetic detection data set composed of electromagnetic signal intensity and propagation delay information of different position nodes in an intelligent space, and then carrying out the sensing equipment state analysis of the electromagnetic detection data set; the method comprises the steps of obtaining a state feature set containing equipment connection stability and signal coverage range features, generating a dynamic topology optimization strategy containing an equipment connection relation adjustment rule and a signal coverage area distribution scheme according to the state feature set, and adjusting a sensor network topology structure according to the dynamic topology optimization strategy. And finally, performing optimization effect evaluation on the adjusted sensor network topological structure, and feeding back an evaluation result to trigger strategy updating, thereby dynamically optimizing the sensor network topological structure and improving the stability and reliability of the intelligent space sensor network.