1523 results about "Global optimization" patented technology

The invention provides a dynamic routing selection method and system, electronic equipment and a storage medium, and aims to solve the problem that a routing strategy is difficult to adapt to a dynamically changing network, the method comprises the following steps: a terminal layer collects the state of a terminal and network data, and performs lightweight feature extraction; the edge node layer receives the data of the terminal layer, carries out space-time-semantic feature aggregation, and generates a region-level resource scheduling and routing decision strategy based on fragmented reinforcement learning; the central cloud service layer gathers whole network data, generates a global optimization strategy and issues the global optimization strategy; the edge node layer fuses global optimization and a region-level strategy, and executes dynamic routing selection; and security and privacy protection are provided through the trusted chain layer. According to the invention, adaptive path selection can be realized, the network resource utilization rate is improved, and the network stability is improved.

The invention relates to the technical field of mining industry, and discloses an intelligent prediction method for gold ore beneficiation process parameters based on cloud and edge fusion, which realizes space-time correlation modeling of beneficiation process parameters and accurately depicts dynamic interaction influence among equipment. The cloud edge collaborative architecture considers global optimization and real-time response requirements, and the prediction stability under complex working conditions is effectively improved. The introduction of physical constraints enhances the applicability of the model in an actual production environment, a bidirectional feedback mechanism ensures the adaptive ability of the system in a dynamic change environment, and through the joint reasoning of a knowledge graph and a neural network, the consistency of a prediction result and a process principle is enhanced, and the risk of misjudgment under an abnormal working condition is reduced; the man-machine cooperation mechanism significantly improves the labeling efficiency of high-value samples, shortens the model iteration period, and ensures the continuous optimization capability of the prediction system in the actual production environment.

The invention discloses a Gaussian representation SLAM method based on dense matching priori and factor graph constraint, which comprises the steps of inputting a current image and a key frame image, outputting a point graph corresponding to the image through a pre-trained model, returning a matching condition of two frame image points and respective point cloud information, and obtaining a point-level matching result based on a point-level matching result. The method comprises the following steps: constructing a joint optimization problem of a current frame and a key frame by taking a luminosity consistency error and a geometric projection error as targets, performing joint estimation on a camera pose and a point cloud of the current frame, realizing high-precision pose solution, generating point diagram data after Gaussian scene representation and rasterized rendering processing, and transmitting the point diagram data to a rear end for global optimization. And the rear end receives the pose and point cloud data, executes loopback detection to identify repeated key frames, and performs Gaussian rendering through an optimized key frame image to complete global dense three-dimensional reconstruction. The method effectively solves the problem of track drift and scene inconsistency caused by lack of pose priori and global geometric constraints in an existing system.

The invention relates to the technical field of computers, discloses an intelligent scheduling method and system for server cluster load balancing, and aims to solve the defects of the existing server cluster load balancing technology in response lag, non-uniform resource utilization rate, service quality guarantee, global optimization capability, fine-grained state perception and scheduling decision. The method comprises the following steps: collecting server state and request feature data, constructing a cluster state and service capability model, and predicting a load trend; and generating an optimal routing strategy by using deep reinforcement learning and multi-objective optimization, and issuing adjustment request distribution. The system comprises a data acquisition module, an application request feature acquisition module, a state sensing and modeling module, a load prediction module, an intelligent scheduling decision module and an instruction execution module. By adopting the technical scheme, the resource utilization rate can be improved, the response time can be reduced, the throughput can be improved, the system stability and elasticity can be enhanced, and the operation cost and energy consumption can be reduced.

The invention discloses a process intelligent simulation method and system based on rational number fusion, and belongs to the technical field of high-end equipment manufacturing and artificial intelligence. The method comprises the following steps of: constructing a fusion database used for storing a mapping relationship between process parameters and organization characteristics by taking a mathematical model fusion normal form as a core, taking the mathematical model data as theoretical model data and taking the mathematical model data as industrial field data; constructing a multi-scale AI simulation agent model corrected by the industrial field data, and constructing an intelligent prediction model used for representing an association relationship between organization characteristics and service performance; and finally, receiving a target performance index, carrying out global optimization under double constraints of a physical boundary of a theory and a simulation environment which is corrected by a number by utilizing an AI reverse design engine, and carrying out reverse solution to obtain an optimal process parameter. Through a two-way closed-loop mechanism of a theoretic constraint number and a number correction theoretic, the fundamental problems of distortion of a theoretical model caused by a scale effect and lack of physical constraint with an AI model are solved.

The invention provides a multi-modal fusion and semantic enhancement train positioning method and system, and belongs to the technical field of rail transit, and the method comprises the steps: carrying out the time-space alignment of data, obtaining a dense point cloud, constructing a dense semantic point cloud, and dynamically estimating the confidence coefficient weight of each type of sensors; a set residual error and a Manhattan structure constraint residual error of a plane are constructed, laser radar point cloud parameters are obtained, and visual projection constraints are constructed at the same time; constructing a comprehensive degradation scoring function to carry out degradation judgment on the current environment; when the degradation result is yes, introducing a structure and motion information independent of an external environment, maintaining trajectory estimation, and constructing a compensation constraint; introducing a prior semantic constraint and a large model semantic factor constraint; and constructing a global optimization objective function, dynamically adjusting the weight of each modal factor, obtaining an optimal estimation state, and outputting a high-precision train positioning result. According to the method, high-precision and robust track estimation in an extreme scene is realized, so that the continuity, safety and intelligence of train positioning are guaranteed.

The invention discloses an optimal design method for a floating wind power-wave energy multi-energy complementary power generation platform, which comprises the following steps of: constructing an integrated and parameterized system model which is a fully-coupled and parameterized numerical model comprising all key components of the floating wind power-wave energy multi-energy complementary power generation platform; all key design parameters influencing the system performance are set as parameterized variables; establishing a multidisciplinary coupling dynamic simulation model; defining a multi-objective optimization problem including decision variables, objective functions and constraint conditions; the decision variable selects a group of core variables from the parameterized variables as optimization input; and combining the multi-objective optimization problem with a multidisciplinary coupling dynamic simulation model, executing a multi-objective optimization cycle, and generating and deciding a Pareto optimal solution set to obtain typical design schemes with different characteristics. According to the method, the global optimization design of the floating wind power-wave energy multi-energy complementary power generation platform can be realized.

The invention discloses an LLM central multi-agent tool arrangement and virtual-real closed-loop evolution scheduling system. The system comprises an intelligent sensing module, a knowledge module, an intelligent scheduling decision module and a closed-loop execution module. The intelligent sensing module collects real-time and multi-modal data of a physical environment and outputs structured sensing data, the knowledge module is used for constructing a knowledge graph and a historical task case library, and the intelligent scheduling decision-making module takes a large language model (LLM) as a core scheduling center, receives and analyzes task instructions, fuses the structured sensing data and graph and case information, and performs task scheduling on the knowledge graph and the historical task case library. Generating a decision scheme according with a constraint condition by using knowledge enhanced reasoning, and continuously optimizing in multiple iterations to generate a decision instruction; and the closed-loop execution module receives and drives the execution end to execute the decision instruction. According to the method, an intelligent closed-loop system of perception-decision-execution-evolution is constructed, and global optimization and high-reliability self-adaptive scheduling of multi-machine collaborative welding are realized.

The invention relates to an electricity-carbon cooperative scheduling optimization method and device for a low-carbon park integrated energy system, and the method comprises the steps: carrying out the cooperative prediction of a multi-state parameter through employing a panoramic situation deduction model, and generating a panoramic dynamic situation scene set; establishing an electricity-carbon cooperative scheduling model considering a carbon transaction mechanism, and deeply embedding the real-time carbon cost into a target function to carry out Pareto optimization of economic cost and carbon emission cost; an electricity-carbon cooperative scheduling model is converted into a standard mixed integer linear programming model, a situation deduction-day-ahead optimization-rolling correction hierarchical calculation framework is adopted to decompose a cooperative scheduling optimization problem to different time scales for decision making, and a global optimization plan is made on the day-ahead layer based on a panoramic dynamic situation. Deviation is corrected on line through rolling optimization in the intraday layer; and the integrated energy system executes the corrected scheduling plan. Compared with the prior art, the method has the advantages that the consumption rate of renewable energy sources can be remarkably increased and carbon emission can be effectively reduced while the operation economy of the system is ensured.

The invention discloses an intelligent optimization method for a multi-type well seam joint control fine injection-production mode, and relates to the technical field of oil-gas field development. The method comprises the following steps: setting a well seam joint control fine injection-production mode, establishing an oil reservoir numerical simulation model in oil reservoir numerical simulation software, obtaining multiple groups of oil reservoir injection-production schemes based on a Latin hypercube sampling method, performing simulation according to each group of oil reservoir injection-production schemes by utilizing the oil reservoir numerical simulation model, generating multiple pieces of sample data, and establishing a sample database; a deep learning agent model is established, after the sample database is utilized to train and train the deep learning agent model, a particle swarm optimization algorithm is adopted to carry out single-target pre-search global optimization to obtain a preferred reference strategy, a reinforcement learning dynamic decision model is established, and a reinforcement learning agent is obtained through training based on a PPO near-end strategy optimization algorithm; and the optimal injection-production development scheme of the oil reservoir is obtained by utilizing the reinforcement learning agent, so that rapid optimization and decision support of the oil reservoir injection-production scheme in a new multi-type well seam joint control mode are realized.

The invention discloses a zeolite adsorption and desorption integrated treatment system, and particularly relates to the field of zeolite separation treatment, and the zeolite adsorption and desorption integrated treatment system comprises a process sensing module, a dynamic partition control module, a global optimization control module and a cooling regeneration module. According to the zeolite adsorption and desorption integrated treatment system, the problem of blind operation in the traditional technology is solved through the process sensing module, a data basis is provided for dynamic partition, the intelligent monitoring level is improved, and the performance of an adsorbent is fully exerted; the problem of non-uniform heat and mass transfer of a bed layer is effectively solved through the dynamic partition control module, the desorption heat utilization efficiency is improved, and meanwhile, the service life of an adsorbent is prolonged; a dynamic optimization function is constructed through the global optimization control module, energy consumption, solvent recovery income and stability are comprehensively considered, self-adaptive adjustment of operation parameters is achieved, the energy consumption is reduced while the processing efficiency is guaranteed, and the environmental benefits are remarkably improved.

The invention discloses an intelligent control and optimization system and method for the whole process of a photovoltaic power station. The method comprises the following steps: constructing a component-level high-fidelity digital twin through multi-source heterogeneous data fusion and an attention enhancement deep learning model; injecting annotated historical fault data, carrying out joint training by combining a feature embedding layer and an adversarial generative network, and generating a digital mapping body with fault evolution deduction capability; a three-layer architecture is introduced, reinforcement learning and a multi-objective optimization algorithm are fused, and autonomous decisions of power station level power output maximization, equipment loss minimization and maintenance cost optimization are realized in a virtual simulation environment constructed by a digital mapping body. According to the method, accurate dynamic mapping of the physical entity and the virtual space, fault pre-judgment and deduction and closed-loop control of global autonomous optimization are realized, the intelligent level and the full-life-cycle economy of the photovoltaic power station are improved, and the problems of insufficient fidelity, lack of fault deduction capability and low global optimization efficiency in the prior art are solved.

The invention discloses a cooperative scheduling method for a zero-carbon park complementary energy storage system, and the method comprises the steps: enabling an electric energy quality index to be explicitly incorporated into an optimization target through multi-source resource dynamic modeling and scene prediction, and building a strong coupling relation between a physical constraint and a scheduling decision; the hierarchical execution mechanism gives consideration to global optimization and local quick response, realizes undisturbed switching under abnormal working conditions, forms a prediction-optimization-execution-feedback closed-loop control system, and can accurately describe physical connection and electrical characteristics of a park power grid by establishing a power distribution network equivalent model and acquiring topological parameters, thereby realizing the optimal control of the park power grid. And basic network data is provided for subsequent optimization. By determining the controllable resource set and completing topological mapping, the position and the regulation and control range of each device in the power grid can be determined, and mistaken sending or conflict of instructions can be avoided. An apparent power upper limit constraint and SOC dynamic model is established, overload operation of equipment can be avoided, the energy storage charging and discharging capacity can be accurately represented, and the performability of a scheduling scheme is ensured.

The invention discloses a three-dimensional modeling method and system, and relates to the technical field of intelligent perception and three-dimensional reconstruction, and the method comprises the steps: taking BIM as a priori, sampling a geometric entity as a visibility evaluation point cloud, and constructing a graph structure environment state under the constraints of a field of view, distance measurement, an incident angle, overlapping and other sensors; on the basis, a deep reinforcement learning agent which is pre-trained by a synthetic scene and subjected to domain randomization migration is introduced, stations and scanning parameters are selected online according to an observation-decision-execution-update closed loop, visibility and coverage are re-estimated after each step of scanning, and self-adaptive correction is carried out on an unexecuted sequence in combination with online optimization; compared with an off-line global optimization method, the method has the advantages that a search space is effectively compressed through candidate station pre-generation and visibility gating, and continuous and adjustable balance is formed among coverage, point cloud quality and operation time by multi-target awards; for engineering constraints such as temporary shielding, site reachability, registration overlapping degree and the like, the strategy can be dynamically replanned in an execution period.

The invention discloses an optical system multi-objective optimization method based on computer vision, and relates to the technical field of intelligent optics, and the method comprises the steps: collecting initial design parameters and an installation and adjustment tolerance range of an optical device, and obtaining an initial design vector set; based on the initial design vector set, performing simulation and image processing under different working conditions, extracting image quality and image features, and obtaining same-domain features; constructing a multi-target agent model fusing optical indexes and task performance indexes through same-domain features; obtaining a target function interface according to the multi-target agent model; on the basis of the target function interface, global optimization and local refinement are adopted to obtain a candidate optimal solution set; and through the candidate optimal solution set, re-evaluation is carried out in uncertainty evaluation and robustness screening, and a final nominal solution is obtained. According to the method, the multi-target agent model fusing the optical indexes and the task performance indexes is constructed, and prediction, constraint evaluation and risk sensitivity scoring are performed on the candidate design vectors, so that joint optimization of the optical performance and the task performance is realized.

The invention belongs to the technical field of unmanned aerial vehicle path planning, and particularly discloses an unmanned aerial vehicle path planning method based on a multi-strategy fusion information acquisition optimization algorithm, and the method comprises the steps: obtaining the digital elevation model map data of a target region, and constructing a three-dimensional environment space model based on a task environment; constructing a path cost function and initializing an optimization algorithm population, wherein the initialization adopts three strategy fusion mechanisms of hypercube initialization, chaos initialization and random initialization to enhance the diversity of the population; a crisscross strategy is introduced, the adaptability and stability of the algorithm in the dynamic search process are enhanced, and a sine disturbance mechanism is adopted, so that the global optimization capacity and local development precision of the algorithm are enhanced; the flight path of the unmanned aerial vehicle is constructed by optimizing the path point sequence, and the path is smoothed by introducing B spline interpolation, so that the path is ensured to have continuity and feasibility, the flight requirement of the unmanned aerial vehicle is met, and the robustness and environmental adaptability of path generation are remarkably improved.

Embodiments of the present disclosure relate to automated optimization and / or evaluation of parameterizable models. With respect to optimization, some embodiments record or collect data according to a user instruction during the optimization of the parameterizable model. Based on such recording or collection, some embodiments then update a parameter during optimization, such as via local and / or global optimization. With respect to evaluation, some embodiments perform validation and / or benchmarking based on a type of parameterizable model and one or more performance metrics. Some embodiments perform local validation and / or global validation as part of the validation and / or benchmarking.

The invention relates to the technical field of unmanned aerial vehicle trajectory planning, in particular to a bidirectional tree random search method based on a complex environment node cost function, which comprises the steps of surveying obstacle data in a flight area and modeling, then introducing a target deviation strategy and a bidirectional search tree mechanism, and combining with an improved minimum cost method to obtain a random search result. Alternately expanding the two trees to generate an initial path, and integrating the path length, the obstacle distance and the flight height energy consumption by a cost function; and finally, pruning the initial path, removing redundant nodes, smoothing the trajectory by adopting a cubic spline interpolation method, ensuring curvature continuity, and generating an optimal path meeting the flight performance of the unmanned aerial vehicle. According to the invention, through an improved bidirectional minimum cost fast expansion random tree algorithm, dynamic association of obstacle constraint, target deviation and path cost in unmanned aerial vehicle path planning in a complex environment is represented, and a more efficient search convergence speed and better path quality are obtained. And rapid convergence and global optimization of the track of the unmanned aerial vehicle in a high-dynamic environment are realized.

The invention provides a target layout data generation method, electronic equipment and a readable storage medium. According to the method, the PCB layout generation process is converted into the iterative optimization task of the neural network model, and dual evaluation on the line length and the density distribution in the total loss function is combined, so that the problems of low layout quality, low optimization efficiency and the like in a traditional layout algorithm are effectively solved. The gradient is calculated through back propagation, the positions of the components are updated, it can be ensured that the lengths of the electrical connecting wires and the space uniformity of the components are optimized at the same time in the layout process, and therefore the overall performance and stability of layout are improved. In addition, in combination with a preset training termination condition, overtraining is avoided, and the calculation efficiency is improved. Finally, the generated optimized layout data has higher quality and consistency, and global optimization can be realized in complex design.

The invention discloses a digital twinborn operation and maintenance method and system for cement production equipment, relates to the field of intelligent operation and maintenance of cement equipment, and can solve the problems of insufficient multi-source data processing, operation and maintenance decision and twinborn model efficiency and extensive resource allocation in intelligent operation and maintenance of the equipment at the present stage. Comprising the steps that multi-source heterogeneous data of an operation area of target equipment is collected, and the multi-source heterogeneous data is used for representing real-time state information of the target equipment in the operation process; analyzing the multi-source heterogeneous data according to the multi-modal large model to obtain operation state characteristics of the target equipment; performing global optimization analysis on the operation state characteristics according to the cloud platform and the edge computing node to obtain optimized operation state characteristics of the target equipment; based on the optimized operation state characteristics, constructing a digital twinborn body which is used for mapping the physical state of the target equipment; and determining an intelligent operation and maintenance decision according to the real-time state and the optimized operation state characteristics of the digital twin. The method is used for intelligent operation and maintenance of the cement equipment.

The invention discloses a Bayesian algorithm-based swivel bridge spherical hinge structure optimization design method, which is characterized in that a parameterized model of a swivel bridge spherical hinge structure is constructed, and a finite element simulation technology and a Bayesian optimization algorithm are combined, so that multi-target global optimization design is realized. The method specifically comprises the following steps: establishing a refined finite element model of the swivel bridge spherical hinge; defining input design variables (spherical radius, supporting radius, pin roll radius and the like) and output optimization targets (maximum contact stress, horizontal and vertical friction moment); adopting Latin hypercube sampling (LHS) to generate a plurality of groups of initial parameter combinations; dynamically selecting a high-value parameter combination through a Bayesian optimization framework to carry out finite element simulation; training a Gaussian process agent model and carrying out iterative optimization; and quantizing the parameter sensitivity and outputting a Pareto optimal solution set. According to the method, the simulation frequency can be remarkably reduced, the design efficiency is effectively improved, and the problem that traditional experience design is prone to falling into local optimum is solved.

The invention discloses an offshore rocket recovery platform attitude stability control method based on particle swarm optimization, and the method comprises the following steps: S1, collecting and preprocessing multi-source attitude data, and generating a time series data set; s2, constructing a fuzzy PID controller, and setting nine three-axis control parameters; s3, performing global optimization by adopting an improved particle swarm algorithm, and outputting an initial parameter solution; s4, introducing a zebra optimization algorithm to carry out local refined optimization; s5, constructing a collaborative optimization architecture, and fusing and outputting optimal control parameters; s6, the optimal parameters are input into a controller, and a servo system is driven to adjust the three-axis attitude; s7, monitoring disturbance amplitude, dynamically triggering a zebra strategy and feeding back the zebra strategy to the particle swarm; and S8, evaluating the control performance, and feeding back iterative optimization if the control performance does not reach the standard. According to the method, the improved particle swarm optimization algorithm and the zebra optimization algorithm are fused, so that self-adaptive optimization and accurate attitude stable control of attitude control parameters of the offshore rocket recovery platform are realized.

The invention belongs to the technical field of steel reinforcement framework three-dimensional reconstruction, and particularly discloses an automatic high-precision three-dimensional dense reconstruction method for a box girder steel reinforcement framework, which comprises the following steps of: dividing a scanning area into a plurality of sub-areas according to a depth image by moving an inspection robot along the steel reinforcement framework, and performing multi-angle local scanning in each sub-area; constructing a local point cloud model by combining camera poses during acquisition, further calculating a relative spatial transformation relationship by using an overlapping region between adjacent local models, constructing a pose map and implementing global optimization, and uniformly adjusting the poses of the local models in a world coordinate system; and finally, all corrected local point clouds are fused to generate a complete global three-dimensional model, so that collaborative reconstruction operation of domain-divided scanning, segmentation reconstruction and global optimization is realized, frame-by-frame accumulation of errors in traditional scanning-while-splicing reconstruction is effectively blocked, long-distance drift is remarkably inhibited, and the precision of three-dimensional reconstruction of the reinforcement cage is greatly improved.

The invention relates to the technical field of data storage optimization, and discloses a solid state disk wear leveling global optimization method and system based on multi-granularity data features. The solid state disk wear leveling global optimization method specifically comprises the following steps that S101, lightweight data feature monitoring points with blocks, units and chips as basic granularity are deployed in a solid state disk controller, and original multi-dimensional data features related to wear are continuously collected and quantified in real time, all collected multi-dimensional data features are converted into dimensionless feature scores which can be transversely compared through normalization processing. According to the method, multi-dimensional feature fusion and normalization processing of three-level granularity of blocks, units and chips of a fixed hard disk are utilized, a prediction model based on a time sequence is combined, a dynamic risk thermodynamic diagram covering the whole disk is generated, a system can accurately position current and future potential abrasion hot spots, and the system reliability is improved. And passive equalization operation is converted into active risk prevention.

The invention discloses a geological disaster emergency response path planning method based on reinforcement learning. The geological disaster emergency response path planning method comprises the following steps: S1, fusing and processing multi-source data; s2, constructing a four-dimensional space-time model based on the fused data; s3, multi-agent collaborative decision making is carried out, and a mixed agent system based on a Safe-PPO improved algorithm is deployed; a multi-objective optimization algorithm is adopted to balance the rescue efficiency and safety; deciding and outputting a leading path and a plurality of alternative paths; s4, performing conflict detection on the action of the path planning agent; s5, carrying out preferential selection on the paths subjected to conflict detection and global optimization; s6, issuing the path to a terminal for rescuing the action personnel; s7, dynamically monitoring the environment; s8, judging whether a path needs to be re-planned or not according to environment dynamic monitoring data; and S9, completing the task. According to the method, through reinforcement learning and multi-agent collaborative architecture, the risk penalty term is embedded in the reward function through the improved Safe-PPO algorithm, and the accuracy of path planning is improved.

The invention discloses a post-pumping air tank robustness collaborative design method considering hydraulic parameter time-varying characteristics, which comprises the following steps: defining a hydraulic parameter uncertainty fluctuation interval of a water delivery system in a full life cycle, and setting a body structure decision variable range of a post-pumping air tank; performing combined sampling in the water conservancy parameter uncertainty fluctuation interval and the body structure decision variable range by using a test design method to generate an initial sample set; performing steady-state and transient-state coupling simulation on the initial sample set, constructing a constant-flow operation condition of the water delivery system by using a hydraulic equation, updating a water pump working point and pipeline pressure distribution, performing transient simulation by using a characteristic line method to obtain a hydraulic response index, and training to obtain a water hammer response agent model; constructing a robustness optimization objective function based on failure probability constraint; and performing global optimization on the target function by using an intelligent optimization algorithm, calling the water hammer response agent model to perform random simulation, evaluating a failure probability, and outputting a target design scheme.

The invention relates to the technical field of power system dispatching automation control, and discloses an AGC load distribution control method and system based on a multi-region greedy hydropower station, and the method comprises the steps: generating dynamic boundary thresholds of a high-efficiency region, a medium-efficiency region and a low-efficiency region based on a real-time performance curve and a dispatching set value of a hydroelectric generating set; judging whether to start a cross-interval distribution strategy or not by utilizing the generated dynamic boundary threshold value; when the number of the units participating in distribution is lower than a set scale, an enumeration algorithm is automatically switched to carry out global optimization, and generation of an optimal solution under a small-scale unit combination is ensured; calculating a deviation value between the actual total load and a target value based on the obtained cross-interval distribution strategy; and preferentially adjusting the output of the high-efficiency area unit through a dynamic compensation mechanism. The system comprises an operation interval dynamic calibration module, a region selection and load pre-distribution module and a deviation compensation and adaptive adjustment module. The power grid frequency adjusting precision and the overall operation efficiency of the hydroelectric system are remarkably improved.

The invention discloses a wireless power transmission coil optimization method and a related system, which can realize automation of coil structure parameters and multi-target global optimization by acquiring a coil geometric parameter space, constructing a comprehensive optimization function and performing iterative search in the coil geometric parameter space. The problems that in traditional design, the number of simulation iterations is large, design efficiency is low, local optimum is prone to occurring, and multiple performance indexes are difficult to balance are effectively solved, coil design efficiency and precision are remarkably improved, and dependence on artificial experience is reduced. Therefore, the number of manual intervention and simulation is remarkably reduced, the coil design efficiency is improved, and the prototype development period of the WPT system is shortened. Besides, iterative search is carried out in the parameter space, so that the whole design space can be effectively explored, a locally optimal solution trap can be jumped out, and a globally optimal or approximately globally optimal coil structure parameter combination can be obtained more possibly.

The invention relates to the technical field of computers, and discloses an intelligent scheduling system and method for a high-efficiency charging platform, and the method comprises the steps: constructing a four-dimensional constraint model integrating the power grid load, the user demand, the equipment health degree and electricity price prediction, and employing a depth deterministic strategy gradient algorithm to drive a multi-target dynamic scheduling decision maker, the global optimization distribution of the charging resources in the space-time power dimension is realized, and strategy reconstruction is completed within 10s when a power grid emergency instruction or a device fault occurs. The system comprises a power grid sensing module, a user acquisition module, a health assessment module, an electricity price response module, a scheduling decision module, an instruction execution module and an emergency reconstruction module, and supports millisecond-level adaptive evolution. According to the method, the weighted reward function is constructed by quantifying the four indexes of power grid stability, user satisfaction, equipment loss and platform income, and the instruction verification and steady-state adaptive mechanism is combined, so that the user experience is synchronously improved, the service life of the equipment is prolonged, and the operation energy consumption is reduced on the premise of ensuring the safety.

The invention discloses a weld defect identification method based on a dense connection convolutional network model, and the method specifically comprises the following steps: S1, constructing a dense connection convolutional network model, and embedding a coordinate attention module behind a transition layer of a convolutional network; s2, data acquisition and processing: acquiring an RGB image of the welding seam through an industrial camera, constructing a data set of the image, and performing image enhancement and standardization processing; s3, performing hyper-parameter optimization, and performing global optimization on the constructed model by adopting a Bayesian optimization algorithm; s4, performing model training and verification, and training a dense connection convolutional network model by using the optimized hyper-parameter combination; and S5, defect identification: inputting a to-be-detected welding seam image into the trained dense connection convolutional network model, and outputting a defect category and a positioning result. According to the method, the transition layer of the convolutional network is embedded into the coordinate attention module, so that the convolutional network model more accurately positions the welding seam position, and the detail features of the welding seam are extracted.