1505 results about "Grid based" patented technology

The invention discloses a polyethylene plastic hollow plate defect detection method and system, and relates to the technical field of nondestructive testing, the system is composed of a plurality of functional modules, and the system comprises: a multi-mode sensing module for collecting multi-source data including a hollow plate surface image, three-dimensional morphology data and an ultrasonic reflection signal; the historical database construction module is used for performing space-time alignment and feature level fusion on the multi-source data to generate a composite defect feature matrix; collecting ultrasonic signal waveforms of known defects, extracting characteristic parameters, and establishing a historical waveform library; calculating a characteristic parameter classification range of each type of defects based on a historical waveform library; judging whether the detection waveform parameter falls into a classification range corresponding to the processing noise or not, and if so, rejecting the signal; otherwise, keeping; and the defect area identification module is used for dividing the surface of the hollow plate into uniform grids based on the surface image of the hollow plate, and counting the density of the residual ultrasonic detection sources in each grid.

The invention discloses a low-altitude integrated management system based on a grid digital twin model and an intelligent algorithm, and relates to the technical field of low-altitude aircrafts. The method comprises the following steps: multi-source sensing data fusion and grid coding are carried out, and an airspace environment space-time database is constructed; constructing an airspace environment space-time database to carry out three-dimensional subdivision on the airspace; generating a multi-scale grid twinborn body, and associating a corresponding attribute for each level of grid according to a management requirement; generating an airspace risk thermodynamic diagram in the future 5-30 minutes; establishing a grid state change triggering rule; and generating a multi-target optimal path set by taking the grid navigation cost as a weight. According to the invention, by combining the real-time operation data of the low-altitude aircraft, the meteorological environment and other data with the artificial intelligence algorithm and the navigation rule base, the airspace traffic flow is analyzed, the flight plan is optimized, the flight route is intelligently distributed, the automatic route setting of any two points is realized, and the response speed of the low-altitude flight service system and the processing capability of various flight data are improved.

The invention discloses a high-temporal-spatial-resolution refined flow field reconstruction method, device and equipment and a medium, and relates to the technical field of ocean current reconstruction, and the method comprises the steps: carrying out the normalization and temporal-spatial alignment of satellite remote sensing, buoy observation and numerical simulation data; based on the alignment data, performing rehearsal on the unstructured nested grid through an FVCOM model, and then dynamically encrypting the grid according to the flow field gradient and generating a background flow field; inputting the background flow field into a PINN-GAN combined framework, and outputting a refined flow field through physical constraint loss and double-discriminator adversarial training; and scheduling a calculation task by adopting a heterogeneous accelerator, verifying the reconstructed refined flow field in real time, and performing feedback optimization. Through generation of the background flow field and refinement reconstruction, the ocean current flow field with high temporal-spatial resolution can be reconstructed efficiently and accurately.

The invention relates to the technical field of spatial-temporal trajectory data anomaly detection, in particular to a trajectory anomaly route detection method and system based on self-supervised trajectory representation learning. The method comprises the following steps: preprocessing acquired trajectory data; track double-view comparison representation learning is carried out based on a double-view-angle synchronous mask strategy; coding time dynamic based on a space-time fusion mechanism and fusing the time dynamic with spatial features; learning essential representation from the fused spatio-temporal features to perform trajectory reconstruction; and error checking is carried out based on the reconstructed trajectory, and an abnormal trajectory is judged through the reconstructed error. The invention provides a brand new trajectory anomaly detection model. According to the model, a GPS track and a grid-based track feature are fused, so that track representation is enriched; and meanwhile, a double-view synchronous mask mechanism is designed, so that the model can sense local disturbance of space and time dimensions at the same time in a training stage, and thus the sensitivity to local anomaly is improved.

The invention discloses an intelligent dynamic tracking and early warning method and system for carbon emission, and relates to the technical field of carbon emission management.The method comprises the steps that a digital twinborn model of a target area is constructed, grid division is conducted based on monitoring precision, and a first layer of fixed monitoring points are arranged; planning a mobile monitoring path in combination with a carbon emission attention fitting result, and configuring a second layer of mobile monitoring points; collecting data through two layers of monitoring points, and establishing a carbon emission data network; performing airflow simulation based on a digital twinborn model, performing traceability inversion in combination with a data network, and positioning an emission source; and finally, equipment-level positioning and carbon emission tracking early warning are realized through equipment matching analysis. The technical problems that in the prior art, the carbon emission monitoring coverage range is limited, the traceability is insufficient, and accurate positioning and real-time early warning of the carbon emission source are difficult to achieve are solved, and the technical effects of high-precision traceability positioning and real-time traceability early warning of the carbon emission source are achieved.

The invention provides a water conservancy grid parameter inversion method and system based on artificial intelligence, and the method comprises the steps: obtaining multi-source monitoring data of a target water conservancy project region, combining the multi-source monitoring data with a preset water conservancy grid physical mechanism model, and building an artificial intelligence probability inference frame for heterogeneous medium parameter inversion; and iteratively optimizing an implicit association relationship between permeability coefficient spatial distribution and hydraulic conductivity dynamic change in the water conservancy grid physical mechanism model, generating a parameter probability distribution set, and according to the parameter probability distribution set, combining with a historical seepage field evolution mode of the target water conservancy project area to obtain a historical seepage field evolution model of the target water conservancy project area. And the equivalent parameter value of each grid unit in the water conservancy grid physical mechanism model is dynamically corrected, a water conservancy grid parameter inversion result is generated, the precision and stability of water conservancy grid parameter inversion under complex working conditions are improved, and reliable support is provided for engineering safety and risk early warning of reservoirs and dams.

The invention relates to the technical field of grinding machining, and discloses a blade grinding wheel abrasion loss online compensation method which comprises the steps that a dynamic hexagonal detection grid is constructed based on the rotating phase of a grinding wheel, non-repeated high-density sampling is achieved through a Fibonacci spiral expansion path, a detection area is dynamically adjusted to reduce the overlapping rate, and the blade grinding wheel abrasion loss is obtained. Micron-sized wear transition is accurately captured; dispersing the abrasion loss into minimum compensation units, constructing a dynamic state equation in combination with grinding force, dressing displacement and workpiece errors, introducing an attenuation coefficient related to the service life of a dressing wheel, and automatically correcting a model drift error; a grading triggering strategy of residual uncompensated quantity is adopted, dynamic decision making of a processing period is combined, and through integral multiple compensation and a margin temporary storage mechanism, it is ensured that long-term accumulative errors are restrained at the submicron level while overmodulation oscillation is avoided. Through the synergistic effect of space-time coupling detection, discrete-continuous double-domain modeling and intelligent compensation decision, the grinding wheel abrasion compensation precision and the system stability are remarkably improved.

The invention relates to a quick river flood forecasting method based on a physical information neural network, and belongs to the field of river flood forecasting. The method comprises the following steps: on the basis of a traditional physical information neural network (PINN), introducing a boundary condition parameter as an input variable, and enabling the PINN to learn a flood wave propagation rule under different boundary conditions. Furthermore, on this basis, a physical information neural network flood fast forecasting framework (RFF-PINN) integrated with a hydrodynamic method is provided, a numerical solution based on grid discretization is reconstructed into a continuous function in a time-space domain through a piecewise polynomial interpolation method, residual error loss between network output and a hydrodynamic model simulation value is constructed, and therefore, a flood fast forecasting result is obtained. The network parameters are optimized in cooperation with the PDE loss, and the problem that the network parameter optimization effect is reduced due to the fact that the PDE loss of the complex flow state area is difficult to converge is solved. The method has the beneficial effect that the water depth change process of each section of the river channel under any boundary condition can be accurately and quickly predicted.

The invention discloses a curved surface quadrilateral dominant grid generation method, device and equipment for an aircraft and a storage medium, and relates to the technical field of computational fluid mechanics, and the method comprises the steps: creating a surface unstructured grid based on surface grid generation parameters of a CAD file of the aircraft, carrying out the conformal mapping of the surface unstructured grid to a parameter domain plane, and calculating the vertex compression ratio of each grid vertex; selecting the current starting point with the minimum compression ratio and the maximum to-be-compared vertex in the adjacency list, and determining the current to-be-processed vertex with the minimum compression ratio in the adjacent vertexes and the advancing direction; determining to-be-processed discrete points according to the current compression ratio and the coordinate information to generate a current isoline, if the current compression ratio does not exceed a threshold value, iteratively updating the current starting point, and skipping to the step of determining the current to-be-processed vertex again; the discrete points are projected to the outer-layer isolines according to the isoline sequence, and the curved-surface quadrilateral dominant grid is generated based on the distance proportion between each discrete point and the projection point, so that the generation efficiency of the curved-surface quadrilateral grid is improved.

The invention discloses an interpretable machine learning genome prediction method and device, belongs to the technical field of combination of biological breeding, biological information and machine learning, and utilizes an advanced machine learning algorithm to perform parameter optimization in combination with biological prior information. Through processing of multi-source data (genome, transcriptome and epigenetic data), dynamic feature engineering (PCA and PHATE dimensionality reduction) and organic combination of various machine learning models, and an automatic parameter adjustment framework based on a grid search and sparrow search algorithm, genome prediction precision and calculation efficiency are significantly improved; meanwhile, the interpretability of the model is realized based on the SHAP value, the SNP site contribution is quantified, a reference is provided for precise breeding, and the method is suitable for animal and plant molecular breeding and medical genetic analysis, and can accelerate genetic analysis of high-value characters, assist precise breeding decision and disease risk prediction, and promote leap-forward development from experience breeding to intelligent breeding.

The invention relates to the technical field of wind field simulation, and provides a wind field numerical simulation method based on multiple meteorological data sources. The objective of the invention is to solve the problems of large simulation error, rough terrain boundary processing and poor turbulence model parameter adaptability caused by non-uniform coverage of a single data source, insufficient precision and unscientific multi-source fusion. The method is characterized by comprising the following steps: step 1, constructing a CFD three-dimensional grid based on a geometric model of a target area; 2, collecting original data of a ground station, satellite remote sensing, numerical forecasting and the like; 3, performing standardized preprocessing (abnormal value elimination, missing interpolation, radiation / geometric correction and resampling), determining a multi-source fusion weight by combining historical data analysis, and generating comprehensive meteorological data by adopting a weighted average method; and 4, inputting the comprehensive data into a CFD-RANS model, dynamically adjusting turbulence parameters, accurately setting terrain boundary conditions, and obtaining a wind field space-time distribution result through numerical solution. According to the invention, through combination of multi-source data fusion and CFD simulation, the wind field simulation precision and stability are improved.

A GNSS positioning slowly-varying fault detection method based on residual-SVR regression comprises the steps that an observation information sequence is acquired based on a Kalman filter, and a covariance matrix of the observation information sequence is calculated; accumulating multi-step information through a sliding window, and constructing chi-square statistics; based on the fault-free data, constructing a training set by taking an innovation sequence as input and chi-square statistics as output, and generating an innovation-statistics mapping function; and fitting a normal slope threshold value based on an SVR predicted value, carrying out least square fitting on an observation statistic curve by sliding a window in real time, and judging whether to start a slow change fault alarm or not. According to the method, the residual error sequence is directly used as model input, and the dynamic chi-square statistical magnitude is used for replacing a traditional dichotomy label, so that the detection delay is reduced; an SVR detection model based on grid search and cross validation collaborative optimization is utilized, and an optimal parameter combination of a minimum mean square error (MSE) is screened through logarithm uniform sampling, interval linear sampling and five-fold cross validation, so that the average absolute error of slowly varying fault detection is reduced.

The invention relates to the technical field of water conservancy management, and discloses a water conservancy dynamic decision-making method and system based on digital twinning and multi-source heterogeneous data, and the method comprises the steps: carrying out the real-time information capturing of a target water conservancy system, and generating a system operation information scheduling matrix based on a grid geographic model and a time dislocation mark; performing hierarchical slicing on the system operation information scheduling matrix according to a multi-scale time window to form a multi-channel flow framework of regional hydrological dynamic behaviors; analyzing the evolution trends of the water level, the flow and the water quality by using a multi-channel flowing framework and a dynamic fusion analysis operator; dividing a river reach, a reservoir and a regulation and control facility corresponding to the potential risk event as candidate scheduling areas; and based on the local risk coupling network, dynamically optimizing a scheduling strategy, a pump gate operation sequence and a data acquisition frequency, and generating a real-time scheduling and emergency response scheme of the candidate region. The method has the advantage of improving flood early warning accuracy and water supply scheduling efficiency.

The embodiment of the invention provides an estuary region storm surge disaster assessment method and device. The assessment method comprises the steps of obtaining basic geographic data, meteorological data, tide and wave data and disaster-bearing body data of a preset region of an estuary region; constructing a discrete grid according to the basic geographic data; according to the tide and wave data, constructing a storm surge and wave numerical simulation model in a discrete grid; simulating different typhoon scenes in a numerical simulation model of the storm surge and the waves according to the meteorological data to obtain simulation results; evaluating according to the simulation result to obtain the danger level of each area; evaluating according to the disaster-bearing body data to obtain the vulnerability level of each region; and determining the comprehensive risk level of each area according to the danger level and the vulnerability level. According to the embodiment of the invention, the evolution process of storm surge disasters can be simulated more accurately, and then the comprehensive risk level of each area of the estuary region is determined.

The invention discloses a three-dimensional geologic model modeling method and device. The three-dimensional geological model modeling method comprises the following steps: acquiring to-be-established geological region data and actual geological boundary data; generating an initial stratum interface grid according to the to-be-established geological region data; correcting the initial stratigraphic interface grid to obtain a corrected stratigraphic interface grid; carrying out geological rule constraint on the corrected stratigraphic interface grid so as to form simple three-dimensional stratigraphic interface model data; correcting the simple three-dimensional stratigraphic interface model data according to the seismic profile information so as to obtain a composite geologic structure model; generating a three-dimensional fault plane model according to the composite geologic structure model; and generating a composite geological structure model including a fault network according to the three-dimensional fault plane model and the composite geological structure model. Through the three-dimensional geological model modeling method, the deviation between the model and an actual geological boundary can be remarkably reduced.

The invention provides a remote sensing-based land utilization carbon emission evaluation method, device and equipment and a medium, and the method comprises the steps: carrying out the radiometric calibration and spatial registration of obtained multi-source remote sensing data, and obtaining a standardized raster data set; generating a land utilization classification grid map based on the standardized grid data set; generating a land change vector layer through a continuous multi-stage land utilization classification grid map; generating a dynamic evaluation unit vector boundary according to the land utilization classification grid map and the land change vector layer; correcting a vegetation carbon absorption coefficient and an industrial emission coefficient in real time based on remote sensing inversion data, and constructing a spatial carbon transfer model; and inputting the land utilization classification grid map, the dynamic evaluation unit vector boundary and the spatial carbon transfer model into a layered mixed carbon emission evaluation model to obtain a carbon emission intensity distribution map. By adopting the scheme, the carbon emission under different land utilization modes can be accurately evaluated, and the scientificity and policy applicability of regional carbon balance evaluation are improved.

A dynamic prediction method for the residual gas content of a pre-extraction coal seam comprises the steps that on-site working conditions are considered, parameters such as gas content changes and related geological factors and extraction factors are collected, and a dynamic monitoring data set is constructed; screening main control factors based on grey relational degree, and determining the main control factors influencing the residual gas content after extraction; a main control factor of the residual gas content is used as an independent variable, a pre-pumping dynamic correction factor is combined, a dynamic prediction model is established, and actually measured data is adopted regularly to verify and optimize parameters of the dynamic prediction model; dividing a pre-extraction working face into uniform grid units, and calculating the residual gas content of each grid based on a dynamic prediction model; an extraction blind area is judged according to a quantitative standard and is visually presented; and formulating a differentiated drilling optimization scheme according to the blind area distribution. According to the method, the residual gas content prediction model dynamically responding to mining condition changes is constructed, extraction blind area prediction and drilling optimization are achieved, the coal seam gas extraction efficiency can be improved, and gas disasters are reduced.

The invention relates to the technical field of video analysis, in particular to a video editing method based on grid layout alternate diffusion and multi-attention control, and the method comprises the steps: segmenting an original video frame sequence into a plurality of grids, each grid comprising a plurality of pixel space video frames which are continuously arranged, and forming grid data; mapping the gridding data to a low-dimensional submerged space through an encoder, and generating initial submerged space feature data; the initial submerged space feature data are edited, the editing process comprises a diffusion process and a sampling process, the diffusion process is based on a pre-trained stable diffusion model, and a time attention module is embedded in the diffusion process; in the sampling process, executing an odd-even time step alternate replacement strategy on the grid layout to promote cross-grid global consistency, and dynamically fusing attention maps of a reconstruction branch and an editing branch according to a timestamp threshold to generate de-noised data; and decoding, splitting and recombining the de-noised data through a decoder to generate an edited continuous video frame sequence.

The invention discloses an intelligent city planning method and system based on multi-source remote sensing data fusion, and particularly relates to the technical field of city planning, and the method comprises the steps: S1, region division: dividing a planning region into M dynamic management grids based on GIS geographic coordinates and city function zoning data, carrying out the auxiliary recognition of topographic relief and building height through LiDAR point cloud data, and adjusting the boundary of the grids, establishing a bidirectional mapping relation between the grid and the multi-source sensing equipment; s2, multi-source data acquisition: constructing a city holographic data set through global coverage and a multi-modal sensing technology, acquiring target city data in real time, then executing multi-stage cleaning and space-time alignment, and performing normalization operation on each layer of data by adopting a standardized processing mode to obtain city data; according to the invention, an air-sky-ground-network global sensing network and a multi-level space-time grid framework are constructed, satellite remote sensing, unmanned aerial vehicle point cloud, ground sensors and urban operation data are deeply fused, and the limitation of single-source data of a traditional system is broken through.

The invention relates to a chemical plant dangerous area electronic fence generation method and system, and the method comprises the steps: obtaining the positioning data of a target person, the positioning data of production equipment, and the video monitoring data and environment sensing data of a target area, carrying out the recognition based on the video monitoring data, and obtaining the behavior data of the target person, the method comprises the steps of dividing a target area into virtual grids based on a dynamic grid algorithm, determining distribution of geographic features based on GIS geographic information of the target area, filtering impassable areas based on the distribution of the geographic features, aggregating and combining adjacent grids, generating a closed boundary, and establishing a risk level evaluation model to evaluate the risk level of each virtual grid. The boundary adjusting module is used for adjusting the minimum buffer area width of the closed boundary to obtain a final electronic fence boundary, and the electronic fence boundary is used for carrying out risk early warning. According to the invention, dynamic and accurate sensing and protection of the high-risk area in the chemical plant are realized.

The invention discloses a multi-microgrid system distributed optimization scheduling method based on an electric power-carbon market, and the method comprises the steps: constructing an energy consumption equipment model and a carbon quota transaction model based on the energy flow and carbon quota transaction process in a microgrid; a dynamic collaborative pricing model is constructed based on the power and carbon quota market supply-demand relationship; constructing an operation cost optimization model of a single micro-grid system based on the above models, and constructing a multi-micro-grid collaborative optimization scheduling problem with the goal of minimizing the total operation cost of all micro-grids based on a Nash bargaining game framework; and solving by adopting an accelerated prediction-correction alternating direction multiplier method algorithm to obtain an optimal scheduling scheme based on power-carbon market coupling, thereby realizing energy operation scheduling of the multi-microgrid. Through power-carbon market coupling, dynamic pricing, game theory optimization and an efficient distributed algorithm, the operation cost of the micro-grid system is reduced, and the reduction of the operation cost assists in improving the operation income of the micro-grid system.

A system for extracting a number of data elements from one or more data sources. A text representing tables using a markdown language is extracted from spreadsheets and or other grid-based documents. The text is provided to a language model with a prompt. The prompt may be a chain-of-thoughts prompt. The prompt includes several requests and / or steps that cause the language model to extract one or more tables from the spreadsheet and output the tables using the markdown language or a different markdown language. The tables extracted from the spreadsheet are converted into table chunks and indexed for retrieval by a retrieval augmented architecture. When a prompt to extract particular information from the spreadsheet is provided, one or more relevant table chunks are identified and provided to a language model for extraction. Using the language model to separate tables improves information extraction accuracy while maintaining downstream instructions.

The invention relates to an earthwork balance calculation method, system and device and a storage medium, and relates to the technical field of earthwork balance design. The method comprises the steps of performing fusion processing on oblique photography data and laser radar point cloud data to generate a live-action three-dimensional model; performing grid division on the live-action three-dimensional model to obtain a grid model; based on the gridding model, defining a decision variable set including the design elevation of each grid point, the overall slope of the site, the slope height of each slope unit and the earthwork allocation amount, and constructing a comprehensive objective function including an earthwork balance objective, an economical objective and a safety objective; and under the engineering constraint condition, performing multi-objective optimization solution on the comprehensive objective function to obtain a Pareto optimal solution set, and selecting a final implementation scheme from the Pareto optimal solution set. According to the method, the problem of topographic data missing of the dense vegetation region is effectively solved, the limitation of traditional single-target optimization is broken through, and the maximization of comprehensive benefits is realized.

The invention provides an orphan insurance policy customer historical information abstract processing method, and relates to the technical field of data processing, and the method comprises the steps: setting an insurance policy monitoring state flow control point if it is detected that an insurance policy state is changed into an orphan insurance policy; the process control point is located at an initial triggering node of policy state change, an instant node for confirming the orphan policy state and a starting node of the service handover process; based on matching analysis of the flow control points and the monitoring time sequence nodes, a geometric control area is obtained, and gridding segmentation is carried out on the geometric control area; obtaining a data collection adjustment value based on the distribution characteristics of the grids; performing optimization adjustment on the cross-source heterogeneous historical data according to the adjustment value; and based on the adjusted cross-source heterogeneous historical data, obtaining a customer interaction timeline through semantic alignment and time sequence reconstruction processing. According to the invention, customer active characteristics and service preferences can be quantified, and the service handover efficiency and customer service quality are improved.

The invention relates to the technical field of flutter, and particularly provides a flutter stability prediction method and device, equipment, a storage medium and a program product. The flutter stability prediction method comprises the following steps: dividing a flow field grid and a solid grid based on a gas compressor geometric model; on the basis of the solid grid, finite element modal analysis is executed to obtain intrinsic mode data, and the intrinsic mode data comprises inherent frequency and intrinsic mode distribution thereof; based on the flow field grid, the intrinsic mode data and preset circumferential air inlet distortion parameters, executing unsteady numerical simulation so as to determine first aerodynamic work under single-node diameter vibration; based on the phase angle between the blades and the first aerodynamic work, second aerodynamic work under all node diameter vibration is determined, and aerodynamic damping under all node diameter vibration is determined based on the second aerodynamic work; based on the aerodynamic damping, flutter stability is predicted. According to the technical scheme, the prediction speed of the flutter stability can be increased.

The invention relates to a multi-scene facility site selection method based on a gridding population mapping and hybrid optimization algorithm, and the method comprises the following steps: S1, constructing regular rectangular grids covering a research area, each network having a unique grid identifier, a centroid abscissa and a centroid ordinate; s2, obtaining different forms of population data, performing conversion processing, integrating the population data into regular rectangular grids, and calculating the population quantity for each network to obtain a grid data set with standardized population weight; and S3, based on the data set and the coverage determination function, respectively constructing target functions of three scenes of full-facility re-optimization, partial-facility optimization and newly-added facility site selection, and solving the target functions to obtain optimal multi-scene facility site selection results of the three scenes. Compared with the prior art, the method has the advantages of improving the calculation efficiency of the optimal facility number range and the site selection position and the like.

The invention relates to the technical field of computer vision, in particular to a method for generating a blind person navigation obstacle avoidance map. The method comprises the following steps: driving a blind guiding robot to a preset inspection point, and collecting a surrounding environment image in real time; generating a three-dimensional point cloud according to the environment image, and constructing an environment map; extracting feature points based on the surrounding environment image, performing feature matching, and outputting a feature matching result; evaluating an inter-frame motion state by using a feature matching result; determining pose features of the blind guiding robot according to the inter-frame motion state; semantic segmentation is carried out by using the pose features, and an obstacle area is determined; acquiring an obstacle image of the obstacle area, and fusing the obstacle image with the surrounding environment image to generate an obstacle map; and constructing a dynamic occupancy grid based on the obstacle map. According to the invention, real-time updating and intelligent obstacle avoidance functions of the blind navigation map are realized based on the computer vision technology, and the navigation safety and the path planning accuracy are improved.

The invention discloses a flood lake wetland heat flux simulation method in a watershed hydrological model, and relates to the technical field of heat flux simulation, and the method comprises the following steps: dispersing a watershed space into a slope river channel and a lake wetland, simulating the water movement of a slope river channel unit, and simulating the water movement of the lake wetland; the method comprises the following steps: simulating surface energy exchange for a lake wetland unit on the basis of simulating a hydrological process, simulating land hydrology and lake inflow runoff of a sub-basin by adopting a grid-based distributed hydrological model, respectively dividing simulation grid units for a slope river channel and a lake wetland, establishing evapotranspiration, infiltration and surface runoff models, and establishing an evapotranspiration, infiltration and surface runoff model; a two-dimensional shallow water equation is used for processing slope confluence, a one-dimensional hydrodynamic model is used for calculating river channel confluence, a lake water quantity balance model is established for calculating the lake water level, the lake submerging process is simulated, and the lake wetland distributed heat flux process is simulated. And the technical blank of flooding wetland hydrothermal simulation is filled.

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

The invention discloses a boundary grid node-oriented shipborne unmanned aerial vehicle delivery scheduling method and system, and relates to the technical field of shipborne unmanned aerial vehicle path optimization, and the method comprises the steps: carrying out the systematic grid division of a boundary region based on a geographic coordinate system and marine environment constraints, and constructing a boundary grid node set; mapping attribution is carried out based on boundary grid nodes, and path feasibility is evaluated; and according to an evaluation result, screening and scheduling candidate tasks, generating a corresponding time window, constructing an optimal path, and completing dynamic scheduling and path planning of multiple tasks through time, space and platform constraint linkage. By constructing a task space mapping structure, an airspace schedulability scoring mechanism and a path multi-dimensional cost evaluation model, dynamic, efficient and safe scheduling of the shipborne unmanned aerial vehicle in a boundary sea area is realized, the task matching precision, the path feasibility and the system robustness are remarkably improved, and the method has high intelligent cooperation capability and environmental adaptability.