250 results about "Global optimization algorithm" patented technology

The invention provides a military clothing production scheduling optimization method and system based on an intelligent algorithm, and the method achieves the intelligent management of a production process through the construction of a dynamic state model and a disturbance cost evaluation system. Firstly, a reference production plan is generated by using a global optimization algorithm, and an event sensing module is deployed to monitor the production process in real time. When a task insertion event is captured, the system automatically collects related production parameters, triggers a disturbance cost evaluation mechanism, and dynamically predicts and quantifies the comprehensive influence of different interruption schemes. And based on the quantification result and a preset decision strategy, the system determines an optimal interrupt execution scheme, calls a rapid local rearrangement algorithm coupled with a disturbance cost index, adaptively adjusts the affected plan segments, and finally generates and issues an optimized production instruction sequence. According to the method, the whole process from disturbance identification to scheme execution is intelligentized, and the problem of dynamic scheduling for emergency tasks in military clothing production is effectively solved.

The invention provides an underground water dynamic evolution prediction method, and belongs to the technical field of underground water prediction based on deep learning. Hydrological parameters and geographic position data of monitoring points are collected, and a regional hydrogeological input tensor is constructed; then establishing an initial finite element model to carry out space extrapolation and flow field completion on underground water level distribution; based on an initial finite element model structure, designing a global optimization algorithm based on target decomposition, carrying out real-time structure optimization on the finite element model, then simulating underground water evolution of a non-monitoring area through a finite element numerical solution, and generating multi-node physical consistency hydrological time series data after resolution is improved; and inputting the generated hydrological time series data into a designed perceptual water level prediction model, and outputting underground water level prediction results at multiple moments and multiple positions in the future and corresponding underground water level thermodynamic diagrams. According to the method, more accurate, continuous and interpretable high-precision prediction of the underground water system is realized, and reliable support is provided for scientific management of underground water resources.

The invention discloses an urban building three-dimensional automatic modeling and visualization method, and belongs to the technical field of building three-dimensional modeling. The method comprises the steps that point cloud data, high-resolution images and geographic information system data of urban buildings are acquired, data cleaning, registration and alignment are carried out, and preliminary building digital representation is formed; accurately segmenting each building, and identifying the contour and main structural features of the building; based on the data integrity and the building complexity, adaptively selecting a proper reconstruction strategy to carry out three-dimensional reconstruction; in the reconstruction process, the geometric structure is analyzed and optimized in real time, and potential topological problems are repaired; automatically generating missing details based on a predefined architectural style library and a component library, and performing material inference and texture mapping; a graph structure is used for representing the relation between the buildings, and the positions and orientations of the buildings are adjusted through a global optimization algorithm; a rendering engine supporting multi-level detail switching is developed, and smooth visualization and interaction of a large-scale city scene are achieved.

The invention relates to the technical field of image processing, and discloses a steel bar positioning control method, system and device based on intelligent scanning and a medium. The method comprises the following steps: establishing a global reference coordinate system through target ball space measurement; scanning the mold table according to the reference coordinate system and extracting boundary features; dividing an operation area and planning a path based on mold table positioning data; performing hand-eye calibration to obtain a pose matrix; filtering the point cloud data and extracting steel bar features; and analyzing the steel bar intersection points and generating an optimized binding instruction. According to the method, the reinforcing steel bar intersection points with different intervals are recognized in a self-adaptive mode, the optimal binding path is generated through the global optimization algorithm, and meanwhile the smoothness and continuity of movement of the mechanical arm are guaranteed.

The invention belongs to the technical field of wind power generation, and discloses a wind driven generator multi-drive variable pitch control method based on big data, which comprises the following steps of: constructing a multi-source data acquisition network covering an environment, a unit, a power grid and a driver, and combining preprocessing means such as denoising and standardization and feature extraction methods such as principal component analysis; high-quality input is provided for a short-term prediction model; the model can accurately pre-judge wind conditions and power requirements in the future 10-30 minutes, and is matched with a global optimization algorithm to dynamically adjust a variable pitch strategy, so that the wind driven generator can still keep stable power output under complex working conditions of gust, power grid fluctuation and the like, and particularly has outstanding performance in areas with unstable wind resources. According to mechanical coupling characteristics of the multi-drive variable pitch system, mechanical parameters such as a gear transmission gap and shafting rigidity are fully included in global optimization, and power output, mechanical fatigue and load distribution are balanced through a multi-objective optimization algorithm.

The invention relates to the technical field of Internet of Things data processing, and discloses an efficient data processing system for Internet of Things equipment. According to the method, multi-node cooperative training and reasoning are supported through the federated learning driven edge computing cluster, and localized efficient processing is realized while data privacy is protected; the cloud edge collaborative intelligent center solves the compatibility problem of heterogeneous equipment and various scenes through a lightweight model dynamic adaptation technology, the communication overhead is reduced, and the model generalization ability is improved; the cross-layer dynamic scheduling subsystem is combined with space-time flow prediction and a global optimization algorithm to realize elastic allocation of computing resources, so that burst loads are effectively dealt with and node overload is avoided; the full-link trusted execution system integrates data desensitization, security aggregation and block chain evidence storage, constructs hierarchical protection from equipment to the cloud, resists hostile attacks and ensures operation traceability; the AI-driven data processing assembly line greatly reduces the redundant data transmission and storage cost through adaptive compression and exception filtering.

The invention discloses a self-adaptive sound effect management method and system with a scene analysis module, and relates to the field of self-adaptive sound effect management, and the method comprises the following steps: S1, collecting environmental acoustic characteristics and user behavior data in real time through the scene analysis module; s2, dynamically extracting user hearing preference parameters and scene classification labels; s3, constructing a virtual audition sweet zone matched with the physical space; and S4, dynamically adjusting the phase delay and the energy distribution proportion of each frequency band, and generating an optimal loudness equalization curve meeting the multi-scene constraint. According to the method, adaptive matching of sound field characteristics is realized in a complex dynamic scene through a multi-level perception-optimization closed-loop architecture, acoustic feature extraction and physical space modeling are deeply fused, the technical boundary of traditional pure signal processing is broken through, and the problem of multi-target conflict is solved based on cross-modal collaborative learning and a global optimization algorithm.

The invention discloses a parameter optimization method based on an evaporation waveguide and troposphere scattering mixed channel, and the method comprises the steps: calculating the loss of a microwave propagation path through simulating a near sea surface evaporation waveguide and troposphere scattering environment in combination with an evaporation waveguide model, a troposphere scattering model, an atmospheric absorption attenuation model and a parabolic equation model; and obtaining the optimal transmitting antenna height, the optimal receiving antenna height and the optimal electromagnetic wave frequency under the evaporation waveguide and troposphere scattering mixed channel by using the target function and a global optimization algorithm. The method disclosed by the invention can adapt to application scenes such as complex offshore beyond visual range communication, can provide a flexible optimization scheme for different offshore propagation conditions and distances, and realizes all-weather beyond visual range communication by combining the advantages of an evaporation waveguide channel and a troposphere scattering channel; the influence of communication system parameters on electromagnetic wave propagation under an evaporation waveguide and troposphere scattering mixed channel can be effectively reflected, the loss of an electromagnetic wave propagation path is remarkably reduced, and the reliability of signal transmission is improved.

The invention discloses a photovoltaic support structural member strength analysis method and system based on a wind load, and relates to the technical field of photovoltaic equipment detection, the method improves the accuracy and economy of photovoltaic support wind resistance design through multi-modal data fusion and intelligent algorithm iteration, and improves the photovoltaic support structural member strength analysis efficiency. Firstly, wind speed / wind direction separation modeling based on machine learning breaks through the limitation of traditional static wind pressure calculation, and the extreme wind condition prediction accuracy is effectively improved; secondly, non-dominant dangerous working conditions such as a 52-degree inclination angle can be automatically positioned through linkage of a parameterized model and a global optimization algorithm, and the efficiency is obviously improved compared with manual trial calculation; and finally, a stress threshold dynamic early warning mechanism realizes conversion from passive checking calculation to active defense.

The invention relates to the technical field of micro-grids, in particular to an alternating current and direct current hybrid micro-grid cooperative control method. According to the technical scheme, the alternating current and direct current hybrid micro-grid cooperative control method comprises the following steps that S1, a multi-stage micro-grid framework is constructed and comprises a main micro-grid, a direct current sub-micro-grid and an alternating current sub-micro-grid, and the main micro-grid and the direct current sub-micro-grid are connected through a bidirectional interconnection converter; s2, deploying a hierarchical Multi-Agent control architecture, wherein the hierarchical Multi-Agent control architecture comprises a main micro-grid agent, a direct current sub-micro-grid agent and an alternating current sub-micro-grid agent; and S3, executing three-level cooperative control, receiving a main network scheduling instruction by the main micro-grid agent, decomposing the main network scheduling instruction into sub-micro-grid control targets, coordinating power distribution among sub-micro-grids based on a global optimization algorithm, and maintaining voltage and frequency stability by the DC sub-micro-grid agent and the AC sub-micro-grid agent through a local control algorithm. According to the method, the power grid consumption efficiency and the operation stability in a high-proportion new energy access scene are remarkably improved.

The invention provides a distributed optical fiber-based vibration source transverse distance determination method and system, and relates to the technical field of optical fiber sensing, and the method comprises the steps: collecting distributed optical fiber vibration signals, extracting features, constructing a multi-resolution scanning region, carrying out the traversal through employing a global optimization algorithm, and calculating the propagation speed of the vibration signals. According to the method, velocity field distribution characteristics based on Gaussian process regression are constructed, velocity field parameters are optimized in combination with a probability sampling optimization method, the transverse distance of a vibration source is accurately determined through discretization space traversal and anisotropy correction, and the precision and reliability of vibration source positioning are improved.

The invention relates to the technical field of electric energy metering monitoring, in particular to an electric energy metering online monitoring method based on adaptive filtering and a deep convolutional neural network. According to the technical scheme, the method comprises the following steps: based on monitoring of correlation between electrical parameters and equipment characteristics, carrying out preliminary correlation analysis by using an intelligent algorithm according to electrical parameters, mutual inductor characteristics and electrical topological structure information collected by an electric energy metering device, and then carrying out global optimization algorithm processing. Noise interference in data is effectively removed through the adaptive filtering technology, the data quality is improved, a reliable data basis is provided for subsequent analysis and model training, the accuracy of a monitoring result is improved, high-precision description of errors of a measuring instrument is achieved, the precision of online monitoring of electric energy measurement is remarkably improved, and the method is suitable for popularization and application. Potential problems of the measuring instrument can be found in time, electric power transaction disputes and power grid operation risks caused by measuring errors are reduced, and stable operation and economic operation of an electric power system are guaranteed.

The invention discloses a distributed cooperative control method and system for silicone rubber glass fiber sleeve production equipment, and relates to the technical field of industrial automation control. Through the block chain technology, the non-tampering and credibility of the data are ensured, the problems that in a traditional method, the data are prone to being tampered, and a trust mechanism is missing are solved, efficient cooperation and task distribution between devices are achieved in combination with a multi-agent system, device operation parameters are optimized, the production efficiency and product quality are improved, and the method is suitable for large-scale popularization and application. A virtual model is constructed by using a digital twin technology, simulation verification is performed on optimized parameters, the achievement of a production target is further ensured, and the system dynamically adjusts equipment operation parameters and enhances the flexibility and dynamic response capability of the system through a global optimization algorithm and a feedback control mechanism.

The invention belongs to the technical field of geophysical exploration, and particularly relates to a method and a system for quantitatively measuring intrinsic parameters of bedrock fractures based on all-angle domain amplitude and phase joint inversion. The method comprises the following steps: acquiring geological radar reflection data of a target fracture at a plurality of incident angles; synchronously extracting data sequences of amplitude change along with angles and phase change along with angles; establishing a fracture reflection theoretical model; constructing a target function jointly constrained by the amplitude residual and the phase residual; and adopting a global optimization algorithm to minimize the target function, and synchronously inverting to obtain the fracture opening, the filler dielectric constant and the occurrence parameter. According to the invention, through the combined constraint of the amplitude and the phase, the inversion multiplicity is effectively inhibited; accurate system effect correction is adopted, so that the reliability of large-angle data is ensured; according to the method, multi-parameter synchronous inversion without prior hypothesis is realized, the inversion precision is small in relative error, the method is obviously superior to a traditional method, and the method has important engineering application value.

The invention discloses an online car-hailing order allocation method and system based on an artificial intelligence algorithm, and relates to the technical field of intelligent scheduling, and the method comprises the steps: obtaining to-be-allocated order data and driver data; determining a candidate driver set by adopting a dynamic search radius and a multi-state driver screening strategy, and calculating an estimated arrival time in combination with a real-time traffic condition; establishing a multi-factor order receiving willingness evaluation model, and calculating the order receiving probability of each order-driver combination; constructing a comprehensive matching degree evaluation model, calculating the matching degree of all order-driver combinations, and generating a matching degree matrix; processing the matching degree matrix by adopting a global optimization algorithm, and outputting an order-driver allocation result; and pushing an order to a driver in the order-driver allocation result, monitoring a response state, and performing reallocation processing on a non-response condition. Through the multi-factor order receiving willingness evaluation and the global optimization algorithm, the success rate and matching precision of order allocation are improved, the order waiting time is shortened, and the overall allocation efficiency is improved.

The invention provides a wind generating set tower tube weld joint optimization method based on hydrodynamics, and relates to the technical field of hydrodynamics, the wind generating set tower tube weld joint optimization method comprises the following steps: obtaining an image of a junction of to-be-welded positions of a wind generating set tower tube, and extracting a junction center line in the image of the junction; according to the extension direction of the boundary center line, a constraint condition for keeping energy continuity of the molten metal at the inflection point of the extension direction is constructed, and the heat flux density formed by the molten metal at the inflection point of the center line is calculated; according to the heat flow density needing to be met in the step S2, the welding heat needing to be absorbed by the to-be-welded material when the welding seam is formed at each inflection point of the tower barrel welding seam is calculated; and a welding parameter-inflection point heat mapping model is constructed, optimal welding parameters are output through a global optimization algorithm, the fatigue strength of a welding seam under the wind load effect is improved, and the service life of a tower drum of the wind generating set is prolonged.

The invention discloses a wind power plant network following / constructing type fan proportion optimization configuration method and device, a medium and a product, and belongs to the technical field of new energy power system stability control, and the method comprises the steps: collecting the operation data of a wind power plant and a power grid, and carrying out the standardization preprocessing; constructing a typical operation scene set based on the preprocessed data; establishing an equivalent model of a following network type fan and a construction network type fan and a coupling model with a power grid; constructing a frequency, voltage and transient stability index system based on the coupling model; designing constraint conditions containing stability indexes; constructing a multi-objective optimization function with stability priority; solving by adopting a global optimization algorithm to obtain an optimal fan proportion solution set; and performing simulation verification and scheme screening on the optimal solution set, and determining a final configuration proportion. On the premise of ensuring the stability of the power grid, the proportion of different types of fans in the wind power plant is scientifically configured, and the safe and stable operation capability of a high-proportion new energy power system is effectively improved.

The invention relates to a transformer fault positioning method and device based on multi-point oil chromatographic data. The method specifically comprises the following steps: setting more than or equal to 3 sampling points on a transformer to obtain concentration sequence data of dissolved gas in transformer oil; establishing a relation function based on the distance from each sampling point to the fault point, the peak concentration and the peak concentration time; and based on the acquired concentration sequence data, solving the relation function through a global optimization algorithm to obtain the coordinates of the fault point of the transformer. According to the method, the coordinate of the fault point with the maximum correlation coefficient can be quickly and effectively searched in the range of the transformer oil tank, and compared with a traditional fault positioning method, the method is free of electromagnetic interference, wider in coverage range, higher in safety and capable of reducing the possibility of misjudgment.

The present invention provides a method, device, and apparatus for determining the instantaneous angular velocity of a rotating machine, wherein the method includes: obtaining a time-frequency diagram corresponding to an acceleration signal of the rotating machine within a preset time period; determining multiple first continuous ridge structures within the first sub-time period based on the local peak value of the spectrum corresponding to any moment in the time-frequency diagram; updating the multiple first continuous ridge structures based on a preset global optimization algorithm and the second ridge point of the time-frequency diagram in the second sub-time period to determine multiple second continuous ridge structures of the time-frequency diagram within the preset time period; determining a target instantaneous frequency within the preset time period based on a preset frequency domain sparsity evaluation index and the multiple second continuous ridge structures, and determining a target instantaneous angular velocity based on the target instantaneous frequency. The solution of the present invention can improve the accuracy of extracting the instantaneous angular velocity.

The invention relates to the technical field of oil and gas field surface engineering, and discloses a compressor distribution and working condition parameter planning optimization technology in a shale gas pressurization process, and an optimization method comprises the following steps: establishing a mathematical model for accurately describing pipe network pressure loss and compressor operation power; the outlet pressure of each intermediate booster stage is defined as a decision variable, and a nonlinear programming model with the lowest system total power or the lowest full life cycle cost as an objective function is constructed; solving the model by adopting a global optimization algorithm to obtain a group of optimal intermediate interstage pressure values; and by analyzing the optimal solution, determining an optimal compressor physical distribution scheme and accurate working condition parameters of each stage of compressor under the scheme. According to the method, a complex engineering planning problem is converted into a mathematical problem with a clear solving path, a global optimal planning scheme considering technology and economy can be provided for the shale gas pressurization system, and the energy consumption and total cost of the system are remarkably reduced.

The invention discloses a parameter identification test system and method for a tuned mass damping inerter, and the method comprises the steps: generating a signal through a signal generator in a TMDI parameter identification process, converting the signal into mechanical vibration through a vibration exciter after amplification, and enabling the mechanical vibration to act on a main structure equipped with TMDI; a force sensor and a laser displacement sensor are used for obtaining the exciting force borne by the main structure provided with the TMDI and the displacement time sequence under the action of the exciting load; on the basis, based on a mathematical model of the TMDI, seven key parameters including the mass, the rigidity and the damping coefficient of the main structure with the TMDI and the mass, the rigidity, the damping coefficient and the system inertia coefficient of the TMDI are effectively recognized through a global optimization algorithm; the method is accurate in parameter identification, high in identification efficiency and suitable for performance analysis and optimization of various TMDIs.

The invention belongs to the technical field of road traffic, and particularly relates to a road traffic accident intelligent reproduction method based on automatic Pc-Crash simulation and an MLP algorithm. Comprising the following steps: 1, establishing a simulation model template in Pc-Crash simulation software according to road traffic accident scene information; 2, a simulation test is designed for unconfirmed parameters on the road accident site, and a simulation data set is obtained; 3, constructing, optimizing and training a multi-layer perceptron neural network model; 4, taking the stopping position of an accident participant of the road traffic accident or the tire friction trace deviation minimization as a core, constructing a target function, predicting an accident result by adopting an MLP neural network model, and obtaining an optimal solution of an unconfirmed parameter by using a multi-starting-point search global optimization algorithm; a simulation result is compared with actual exploration data, and the accuracy of the optimal solution is verified; intelligent reproduction of the whole process of a road traffic accident is realized, and important technical support is provided for responsibility determination and compensation processing during traffic accident processing.

The invention discloses a fault prediction model training method, and a fault prediction method and device. Comprising the steps of obtaining a training data set, inputting first operation state index data into a long short-term memory network of a to-be-trained fault prediction model, determining time sequence characteristics of the first operation state index data in a first time period, inputting the time sequence characteristics into a Gaussian process regression network of the fault prediction model, and performing processing according to the time sequence characteristics to obtain a fault prediction result. And the probability distribution data of the operation state indexes of the sample communication network in the second time period are obtained, a global optimization algorithm is adopted to carry out multi-round iterative updating on parameters in the fault prediction model until a preset stop condition is met, the fault prediction model after parameter updating is obtained, and the convergence precision and speed of the model parameters are effectively improved.

The invention belongs to the technical field of optical system design, and particularly relates to an initial structure design method of a quasi-telecentric off-axis three-mirror optical system. The method comprises the following steps: S1, setting optical design parameters of the quasi telecentric off-axis three-mirror optical system; s2, establishing a quasi-telecentric constraint condition, and constructing an evaluation function in combination with an initial aberration expression of the quasi-telecentric off-axis three-mirror optical system; s3, setting a value range of optical parameters to be optimized according to design indexes and structural forms of the quasi telecentric off-axis three-mirror optical system; and S4, performing joint optimization calculation on the evaluation function based on a global optimization algorithm and a local optimization algorithm in a corresponding value range to obtain an optimized structure of the quasi telecentric off-axis three-mirror optical system. According to the method, a telecentric constraint condition is combined, a global and local optimization strategy is adopted, the global search capability of variables is greatly improved, and the off-axis initial structure obtained through final optimization has high imaging quality and high telecentric degree at the same time.

Provided is a smart platform-based environmental control unit management system for a coal-fired power plant, relating to the technical field of flue gas treatment. The system comprises a sensing layer, an application layer, and a decision-making layer, wherein the sensing layer comprises a plurality of data acquisition devices for collecting data of each piece of operating equipment within an environmental control unit; the application layer comprises a flue gas management module, wherein the flue gas management module creates a flue gas management prediction model on the basis of the data collected by the sensing layer and historical data records; and the decision-making layer determines an optimal coordinated control strategy for the environmental control unit on the basis of the model created by the application layer and the data. According to the platform-based environmental control unit management system, parameter information is acquired by means of a sensing layer, and emission prediction is achieved by means of modeling by an application layer; the application layer uploads a prediction model and sensing layer parameters to a decision-making layer; the decision-making layer applies a global particle swarm optimization algorithm to output parameters and the created models, and adjusts a parameter range and a pollutant emission threshold as constraint conditions with the aim of minimizing costs, so as to obtain an optimal system control strategy.

The invention relates to the technical field of medical information processing, in particular to a medical voice instruction semantic analysis and form automatic filling system, which comprises the following modules: a voice recognition and analysis module, which is used for collecting a medical voice instruction, carrying out noise reduction processing and endpoint detection on a voice signal, and sending the voice signal to an automatic form filling module; generating a recognition text with confidence by using a voice recognition model customized by a medical scene, and performing named entity recognition and slot extraction on the recognition text to generate candidate semantic units; and the constraint generation module is used for extracting clinical logic constraints of the form fields according to the candidate semantic units. According to the method, constraint semantic representation is generated in combination with violation degree calculation, matching is completed under logic constraints by using a global optimization algorithm, and high-precision mapping of medical voice instructions and form fields is realized; not only is the integrity of semantic information ensured in the recognition stage, but also mistaken filling is effectively avoided through clinical logic constraints in the matching stage.

The invention discloses a computing power network resource scheduling method, device and equipment, a storage medium and a product. Obtaining task data of the target network area and computing power resources of each node, wherein the task data comprises operation data and first demand data of a current operation task and second demand data of a to-be-operated task; calculating a network load parameter according to the operation data and the computing power resource of each node; judging whether load abnormity exists in the target network area according to the network load parameters; if yes, determining a first resource allocation scheme set by using a preset global optimization algorithm based on demand data of a to-be-allocated task and computing power resources of each node, the to-be-allocated task being determined based on the current running task and the to-be-run task; based on the first resource allocation scheme set, determining a second resource allocation scheme set by using a preset local optimization algorithm; and determining a target resource allocation scheme based on the second resource allocation scheme set. And the resource utilization rate and the resource scheduling efficiency are effectively improved.

The invention discloses a hospital internal positioning, navigation and obstacle avoidance system for assisting a blind person in seeing a doctor, which adopts a visual SLAM (Simultaneous Localization and Mapping) algorithm, a multi-view stereo matching algorithm and a global optimization algorithm Bundle AdJustent to reconstruct a three-dimensional map in a hospital, and adopts a PnP algorithm and an RANSAC (Random Sample Consensus) algorithm to obtain three-dimensional coordinates of equipment. A large language model GPT-4o is adopted to analyze the dialogue between the blind person and medical staff to obtain the destination of the blind person, a navigation algorithm A * is combined with a three-dimensional map, built in advance, of a hospital where the device is located in a cloud server, a navigation path is formulated and played to the blind person in real time, action guidance is provided for the blind person, and the blind person can conveniently and rapidly enter the medical staff. And detecting and segmenting a front obstacle by adopting an instance segmentation algorithm YOLOv8-seg, obtaining information of the front obstacle in combination with the depth image and coordinate transformation, and broadcasting the position of the obstacle to the blind.

The invention discloses a high-speed aircraft aerodynamic configuration agile optimization method based on an artificial intelligence model. The method comprises the following steps: A1, establishing an aerodynamic force agent model; a2, performing a global optimization algorithm; a3, establishing an intelligent autonomous decision-making model; by constructing a multi-target adaptive weight distribution model, weight dynamic collaborative optimization of proxy model confidence, a constraint penalty function and target function key elements is achieved, the mathematical modeling problem of dynamic matching of weight parameters and optimization targets in the iteration process is solved, a closed-loop optimization system with the autonomous perception-decision-execution capacity is developed, and the optimization efficiency is improved. Therefore, the convergence speed is improved in a complex engineering optimization scene, local optimum is avoided, and the robustness of a global optimal solution is ensured; and A4 pneumatic optimization iteration: based on the proxy model for predicting indexes such as the lift-drag ratio, performing global optimization of design parameters by using an artificial intelligence optimization algorithm, performing verification through CFD, and after verification, putting a CFD calculation result into a training set of the model to re-train the model and continuing optimization until optimization design is converged.

The application discloses a kind of orchard environment based on fusion GCNv2 network binocular vision SLAM method and system, comprising: using binocular camera to shoot the stereo image pair of orchard environment;Depth map is generated using stereo matching algorithm as label, construct and train GCNv2 network architecture including attention mechanism and multi-scale feature fusion;Input orchard environment image and extract key point, find left-right image corresponding relationship by descriptor matching, assign corresponding depth value to each matching key point using the depth map predicted by GCNv2 network;Filter feature points and update map with depth information;Loop detection and perform geometric consistency check, execute BA global optimization algorithm to correct errors in the entire trajectory and map;The application provides a kind of efficient, robust and suitable for orchard environment binocular vision SLAM method to improve the navigation ability and automation level of robot in complex orchard environment.