20results about How to "Improve optimization efficiency" patented technology

This invention relates to a method and system for automated generation of TSN network configurations based on a reflective agent. The system comprises a network state perception and intent reflection module, a YANG model configuration parameter and structure decision module, and a YANG model intelligent generation module. The network state perception and intent reflection module is responsible for structurally perceiving network state and resource information. The YANG model configuration parameter and structure decision module integrates multi-source knowledge and contextual information, intelligently generating deployable TSN scheduling parameters and YANG configuration structures based on business needs. The YANG model intelligent generation module automatically generates the corresponding YANG configuration model based on the optimized results, performs syntax and semantic verification, and then distributes it to the target device, achieving automated deployment of network configurations. This invention significantly improves the intelligence level and deployment automation capability of TSN network configuration by introducing reflective reasoning and feedback optimization techniques.

The application discloses a large-interval wide-angle scanning phased array antenna design method and system, and belongs to the technical field of array antenna design. The application adopts parameterized setting of block number to improve the degree of freedom of optimization. Since each block condition may exist a better solution, the block type of the region is substituted into the optimization variable, so that the final optimization result can be prevented from falling into a local optimum, and the optimal solution can be more likely obtained. The dense arrangement form of the divided region can effectively control the interval of adjacent array elements, avoid the overlapping of adjacent array elements, and improve the optimization efficiency. In addition, the regular layout is beneficial to the back-end network design, and reduces the complexity of the feed network. The dense arrangement and array element selection combination can effectively enlarge the interval, and can make the interval not appear grating lobe, thereby reducing the number of channels and reducing the cost. The array element position micro-adjustment provides more possibilities for large-angle scanning optimization, improves the optimization efficiency, and breaks the periodic arrangement form.

This invention discloses a knowledge-enhanced multi-agent decision-making method for welding deformation control in ship assembly subgroups. The method includes: constructing a welding process knowledge graph using a large language model based on welding process data; building a multi-agent collaborative framework for ship assembly subgroups; a process planning agent generating a process plan including welding sequence and welding parameter combinations; a deformation simulation agent performing welding deformation simulation calculations and outputting deformation prediction results; and a central coordinating agent triggering multi-agent collaborative iterative optimization based on the comparison of deformation prediction results with tolerance constraints, outputting the optimal welding process plan that meets deformation control requirements. This invention, by constructing a welding process knowledge graph, structurally represents the welding deformation mechanism, process parameter rules, and historical cases, and achieves knowledge-driven process decision-making based on a multi-agent collaborative mechanism, effectively solving the problems of lagging welding deformation control and reliance on experience in process parameter selection in traditional methods.

The application discloses an optimization method for a video transmission network CDR. The method comprises the following steps: acquiring an environment characteristic and a control parameter vector, wherein the control parameter vector is constructed based on a fuzzy controller of the video transmission network CDR; generating an initial value of a spore population based on the control parameter vector; iteratively optimizing the spore population by using an optimization method with an adaptive exploration and utilization strategy, so that the spore population is adapted to optimal video transmission quality under the environment characteristic; each spore of the spore population corresponds to a value of the control parameter vector; and after the iterative optimization is completed, the spore with the highest fitness in the spore population is taken as a value of a target control parameter vector and is issued to the fuzzy controller. The application can realize adaptive optimization of clock data recovery in a complex and dynamically changing video transmission environment, and improve the robustness of clock data recovery.

This invention discloses a low-carbon design method for nearshore stainless steel-clad reinforced steel bridges. The method includes: obtaining seismic time-history acceleration curves; identifying the pier section design variables for nearshore long-span beam bridges; obtaining the residual steel reinforcement cross-sectional area after several years considering pitting corrosion; obtaining the expected value of seismic loss cost over the entire life cycle of the bridge; obtaining the direct construction cost and carbon emissions of the bridge under different pier section design variables; constructing a dataset by using the pier section design variables as feature data and the direct construction cost, carbon emissions, and expected seismic loss cost as target data; obtaining a surrogate model based on the dataset; obtaining the Pareto optimal solution set based on the surrogate model and the NSGA-II multi-objective genetic algorithm; and obtaining the optimal design scheme based on the entropy weight method combined with the TOPSIS method. This invention solves the technical problem that existing nearshore bridge design methods struggle to simultaneously consider durability, seismic safety, economy, and environmental impact.

The application provides an integrated circuit wiring optimization method and device, computer equipment and a storage medium, and relates to the technical field of integrated circuits. The method of the application extracts structured initial wiring information based on an initial wiring result, realizes automatic structured mapping of data, and eliminates the inefficiency and errors of manual sorting. By calculating the ratio of physical line length to Manhattan ideal line length and filtering candidate optimization networks, the network with serious line length redundancy is quickly located, and the data processing range is greatly reduced. The pre-constructed capacitance lookup table model is used to evaluate the capacitance of the candidate network, to realize accurate quantification of parasitic capacitance at very low computational cost, replace traditional time-consuming simulation, and ensure the accuracy of the optimization direction. The corresponding capacitance optimization strategy is executed for the target network and iteratively updated and optimized, which reduces the capacitance while avoiding global recalculation through local incremental update, realizes efficient and accurate automatic wiring optimization, and improves the optimization efficiency of integrated circuit automatic wiring.

Embodiments of the present application provide a data processing method, device and related equipment, wherein the method comprises: in the compilation phase, when a string truncation operation is detected, a static call node corresponding to the string truncation operation is generated, and the static call node is stored in a first to-be-processed list; after the compilation is completed, it is judged whether the static call node in the first to-be-processed list is a to-be-optimized node; if the static call node is a to-be-optimized node, the static call node is stored in a second to-be-processed list, and an optimization operation is performed on the string truncation operation corresponding to the static call node in the second to-be-processed list. Embodiments of the present application improve the optimization efficiency of the string.

This invention discloses a SOH estimation method based on Bayesian optimization. The method includes: acquiring a battery dataset, constructing a battery health feature matrix, initializing Bayesian optimization, multiple rounds of Bayesian optimization iteration, determining convergence conditions, and training the optimal configuration model on a full dataset. This invention belongs to the field of data processing technology, specifically referring to an SOH estimation method based on Bayesian optimization. This scheme uses a battery health feature matrix to construct a CNN+LSTM model, defines a precise objective function, and employs multiple rounds of Bayesian optimization iteration to achieve intelligent parameter search and reduce computational costs. It uses a CNN+LSTM model for dual extraction of local and long-term temporal features, covering all dimensions of battery aging features. A multi-LSTM sub-model fusion structure is used to calculate the final SOH estimation result. Bayesian optimization is used to improve the model's generalization ability, and the parameter configuration most likely to improve performance is intelligently selected based on the expected improvement value, thereby improving optimization efficiency.

This invention relates to the field of logistics management technology, specifically to a method, system, and storage medium for optimizing multimodal transport delivery routes. The method includes: generating individual solutions based on basic cargo transportation data to form an initial population; updating the initial population using a hybrid optimization algorithm based on a multi-objective constraint model; and obtaining the optimal individual solution from the initial population as the Pareto optimal solution set. The hybrid optimization algorithm employs an adaptive PSO-CS operator to update the initial population. The PSO operator updates the speed and position of individual solutions and, based on congestion, obtains the globally optimal individual solution as the offspring population. The CS operator, through adaptive scheduling decisions, performs global or local CS perturbations to fine-tune the obtained individual solutions, generating the offspring population. This scheme effectively handles the heterogeneous search space and complex constraints of MTOP, dynamically and adaptively refining the shrinkage speed, thus improving search efficiency and quality.

This invention relates to the field of data processing, and more specifically, to a method and system for intelligent parameter optimization of core coating equipment. The method includes: statistically cleaning historical process data to construct a tree-structured prediction model that can simultaneously predict bonding strength and energy consumption; using this model to select high-quality samples to construct a reference sample library, and adaptively constructing a parameter coupling structure by analyzing the global correlation matrix between parameters; employing a structure-aware genetic algorithm for parameter optimization, maintaining the integrity of strongly correlated parameter groups during recombination operations, and using a time-varying decay strategy during perturbation operations for efficient parameter optimization; when the prediction deviation exceeds a dynamic trigger threshold, fusing recent data with the reference sample library to establish a current operating condition model, and selecting highly correlated historical data and assigning weights to update the model, thereby continuously adapting to process changes. This invention achieves a significant reduction in energy consumption while ensuring coating quality.

This invention relates to the field of meteorological forecasting technology, specifically a meteorological forecasting adaptive optimization method and system based on multi-algorithm fusion. This method constructs a parameter optimization framework based on multi-algorithm fusion, embedding physical constraints, and possessing adaptive capabilities, aiming to systematically solve key problems in the parameter tuning process of traditional meteorological forecasting systems. This framework achieves efficient and intelligent optimization through a series of tightly linked steps, ensuring the effectiveness and stability of the method in operational scenarios. This process integrates preceding modules into a unified platform through a microservice architecture, automating data and control flows. The evaluation system includes historical backtesting, real-time forecast testing, and extreme weather-specific testing, quantifying forecast accuracy improvement, resource consumption optimization, and cross-scenario generalization capabilities. Results are fed back to a knowledge base to form a self-optimization loop, ultimately verifying the comprehensive advantages of this method in improving forecast efficiency, ensuring physical rationality, and operational applicability.

The application relates to the technical field of communication, and discloses a heterogeneous link communication method and device for unmanned area inspection, which comprises collecting real-time performance parameters, inputting the performance parameters into a prediction network 1, outputting an environment interference level perceived by a link type, and qualitatively indicating the link type interfered; if the interference level exceeds a threshold value, a to-be-optimized link subset is screened out according to the indicated link type; the performance parameters of the to-be-optimized link subset are input into a prediction network 2, and a normalized link reliability score of 0-1 is mapped; the link with the highest reliability score is selected as a target communication link, and if the communication delay of the target communication link exceeds a service threshold value, the modulation mode and the transmission power of the target communication link are adjusted in cooperation. The method and device improve the communication continuity and reliability, and improve the optimization efficiency when the communication quality deteriorates.

This invention belongs to the field of aerodynamic optimization technology for unmanned aerial vehicles (UAVs), specifically relating to a hierarchical aerodynamic optimization method for a micro solar-powered UAV. This optimization method employs a hierarchical optimization approach, involving a two-step optimization process. First, a genetic algorithm is used to select the optimal population and optimal sample. Second, adjoint solution is performed on the current optimal sample to obtain gradient information for free deformation optimization based on mesh deformation. Furthermore, this invention employs a step-by-step optimization method, first optimizing the two-dimensional airfoil, and then optimizing the three-dimensional airfoil according to specific practical requirements. This aerodynamic optimization method does not rely on a fixed configuration, reducing the number of gradient deformation iterations and thus minimizing optimization overhead. Simultaneously, it can maximize the coverage of design conditions and reflect the actual surface shape of the solar-powered UAV to the greatest extent possible.

The application provides a compressor non-axisymmetric end wall shaping method based on an adjoint shape optimization, and belongs to the technical field of aero-engine compressor design, in particular to: taking an axisymmetric end wall compressor as an optimization object, sequentially completing a cascade single-channel original geometric model establishment, structured grid division, obtaining the isentropic efficiency of the compressor designed by using the axisymmetric end wall through flow field simulation, establishing an optimization target and a constraint condition, solving the sensitivity of a design variable based on an adjoint shape optimization method, arranging control points on the outer surface of the end wall, combining the total displacement amount of the control points with the sensitivity calculation, realizing the end wall grid deformation through a radial basis function, iteratively optimizing the performance of the current end wall geometric configuration through simulation verification, and until the end wall meets the optimization target and the constraint condition. The application improves the optimization efficiency and shaping precision of the compressor end wall, improves the isentropic efficiency of the compressor, and guarantees the stability of the performance parameters of the optimized compressor.

This invention discloses a method for navigating large cargo transport in complex curved road environments using a two-vehicle cooperative system. The method includes: constructing a double-layer grid map to represent the hierarchical spatial constraints of the transport vehicles and cargo; using the A* algorithm to perform articulation point path search and generate an initial trajectory that satisfies topological constraints; detecting collision regions using an algorithm combining directed bounding boxes and the separating axis theorem; and performing distributed trajectory optimization in the collision regions based on the alternating direction multiplier method and model predictive control algorithm. The two-vehicle cooperative transport group is decomposed into a transport vehicle subsystem and a cargo subsystem, achieving trajectory optimization while satisfying dynamic constraints, articulation geometric constraints, and obstacle avoidance constraints. This invention solves the problem of navigating two-vehicle cooperative transport groups on curves.

This application relates to the field of infrared optical lens technology and discloses a method for optimizing the optical performance of a cooled mid-wave infrared zoom lens, comprising the following steps: obtaining the basic optical parameters of the cooled mid-wave infrared zoom lens to be optimized within the full focal length range, and constructing a multi-focal-length optical performance benchmark library; based on the multi-focal-length optical performance benchmark library, performing a pre-evaluation of the cold reflection effect of the lens, identifying optical surfaces with a risk of cold reflection and performing pre-optimization; by constructing a multi-focal-length optical performance benchmark library and conducting a pre-evaluation of the cold reflection effect, the high-risk optical surfaces of the cooled lens that are prone to cold reflection are identified in advance, and pre-optimization is performed by combining coating adjustment and angle fine-tuning, which solves the problem that existing methods do not specifically address the unique defects of cooled lenses and are prone to cold spot ghosting, thereby improving the basic imaging clarity and contrast of the lens from the source.

The application discloses a design parameter optimization method and device, equipment and a storage medium, and comprises the following steps: determining a fluid-solid assembly model and a flow field model corresponding to a dredging pump according to current design parameters of the dredging pump and working conditions corresponding to the dredging pump; establishing a fluid-solid assembly coupling model corresponding to the dredging pump according to the fluid-solid assembly model and the flow field model, and determining current stress parameters and current deformation variable parameters of the dredging pump according to the fluid-solid assembly coupling model; if the current stress parameters are less than preset limit stress, the current design parameters are taken as target design parameters; if the current stress parameters are greater than or equal to the preset limit stress, an optimization function is determined according to the current stress parameters and the current deformation variable parameters, the current design parameters are optimized according to the optimization function, next design parameters are obtained, the next design parameters are taken as the current design parameters, and the step of determining the fluid-solid assembly model and the flow field model corresponding to the dredging pump according to the current design parameters of the dredging pump and the working conditions corresponding to the dredging pump is returned to.

This invention discloses a multi-objective optimization method based on a GCN proxy model, which mainly addresses the problems of high computational complexity, low optimization efficiency, and low prediction accuracy in cascading failure simulation during critical node detection in complex networks. First, initialization and objective function construction are performed, setting relevant algorithm and model parameters, and constructing a dual objective function for attack cost and attack failure effect. A complex network dataset is generated, downloaded, and preprocessed. Second, addressing the low computational and optimization efficiency caused by multiple traversals of the entire network in each evaluation of cascading simulations during critical node detection in complex networks, this invention constructs and trains a multi-branch attention GCN proxy model. Through multi-dimensional feature extraction, multi-task learning, and group calibration, the prediction accuracy of the number of cascading failure nodes is ensured. Then, a multi-objective optimization algorithm fusing GA and PSO is used iteratively, combined with the GCN proxy model to predict the objective function, improving optimization efficiency. The convergence and diversity of solutions are balanced through GA global search and PSO local optimization. Next, the Pareto optimal solution is calibrated and verified to ensure the relative error is within a reasonable range and to verify accuracy. Finally, the experimental results are output and archived. This invention utilizes the GCN proxy model to assist in the fusion of multi-objective optimization algorithms, significantly improving optimization efficiency and prediction accuracy, and enabling precise detection of key nodes in complex networks.

The application belongs to the technical field of molecular dynamics simulation, and particularly relates to a reaction force field optimization method and device, a computer readable storage medium and a terminal device. The method comprises the following steps: acquiring atomic molecular configuration information, first reaction force field parameters and second reaction force field parameters; wherein the first reaction force field parameters are fixed-value reaction force field parameters, and the second reaction force field parameters are reaction force field parameters to be optimized; calculating a physical property parameter intermediate quantity according to the atomic molecular configuration information and the first reaction force field parameters; calculating a physical property parameter output result according to the physical property parameter intermediate quantity and the second reaction force field parameters; and optimizing the second reaction force field parameters according to the physical property parameter output result to obtain optimized second reaction force field parameters. Through the application, the overall processing process can be effectively simplified, and the optimization efficiency can be improved.

The application discloses a double-order-reduced numerical control machine tool digital twin physical field construction method. The physical field finite element simulation sample data set under the working state sampling space is initially reduced to a relatively sparse grid node data through an improved KNN algorithm, so that the number of point sets is kept within the real-time rendering capability range of graphics rendering software; an RBF radial basis function is used to train a digital twin physical field agent model, so that the physical field is reconstructed; a genetic algorithm is used to iteratively optimize the digital twin physical field agent model, so that the deviation between the agent physical field model and the real physical model is solved; and finally, the digital twin physical field agent model is deployed to a digital twin system and used to solve the physical field distribution in real time. The application reflects the spatial distribution of the structure field and the temperature field in the digital twin system, so that the real-time operation data driven virtual-real image mapping physical field simulation is realized.