2251 results about "Grid cell" patented technology

The invention provides an urban functional area gridding identification and classification method based on multi-source spatio-temporal data, and relates to the technical field of area identification, and the method comprises the steps: carrying out the dynamic grid division of an urban area through employing a self-adaptive grid division algorithm, obtaining the multi-source spatio-temporal data of a gridding area, carrying out the space-time alignment, and generating a multi-dimensional time sequence feature set; extracting multi-dimensional feature vectors of the grid units through a multi-modal feature fusion algorithm, acquiring long-range time sequence features by using a bidirectional gating recursion unit, calculating spatial correlation features based on a multi-head attention mechanism, and inputting the two types of features into a feature fusion network with jump connection to generate enhanced spatial-temporal features; and an optimization strategy is adopted, a functional area clustering center point is determined through density peak value clustering in sequence to obtain an initial division result, the initial result is optimized by combining spectral clustering of spatial adjacency constraints, and the boundary of the functional area is optimized by using a simulated annealing algorithm, so that accurate identification and classification of the urban functional area are realized.

A path planning method for a multi-unmanned-aerial-vehicle collaborative maritime search. The method comprises: acquiring and analyzing related ocean current information of a region to be searched, and establishing an ocean current model on the basis of the obtained ocean current information (step 1); on the basis of the region to be searched, a command and control center establishing a grid map by taking a grid cell as a unit, and then establishing a target probability map on the basis of the related ocean current information of the region to be searched, prior target information and the grid map, wherein the target probability map provides the probability of a prior target being present in any grid cell (step 2); if there is an obstacle in the region to be searched, the obstacle being represented by an irregular polygon, generating a segmentation line for region division by taking a vertex of the irregular polygon as one end point of the segmentation line and also meeting a limiting constraint condition for region division, so as to divide the region to be searched into Nr sub-regions, and the command and control center allocating a sub-region to each unmanned aerial vehicle on the basis of the performance of the unmanned aerial vehicle by using a static allocation strategy (step 3); after the sub-region is allocated to each unmanned aerial vehicle, the command and control center planning a cross-regional shortest path for each unmanned aerial vehicle and using a path planning method to plan an unmanned aerial vehicle coverage path, so as to implement a single-region shortest-time coverage path (step 4); and monitoring the endurance state of each unmanned aerial vehicle during the process of executing a search task, and if it is found that the endurance capability is insufficient, recalling the unmanned aerial vehicle, and if a target is found or the search task is completed, recalling the unmanned aerial vehicle and returning to target coordinates (step 5).

The invention discloses a low-altitude airspace management method and system based on data analysis, and relates to the technical field of air traffic management and low-altitude airspace planning, and the method comprises the steps: 1, multi-scale dynamic grid division: adjusting the grid granularity in real time according to the aircraft density and environment complexity, generating a multi-scale grid by adopting an optimized quadtree data structure, and carrying out the multi-scale dynamic grid division; boundary smoothing processing is carried out on boundary areas of the grids with different scales; 2, intelligent airspace layering: dynamically adjusting the thickness of a height layer according to a risk score, and setting a buffer layer to enhance the flight safety; and 3, intelligent semantic annotation: giving dynamic semantic information such as risk levels and traffic priorities to the grid units, embedding airspace control regulations and flight rules, and realizing multi-source data fusion and real-time updating of semantic information. According to the method, the problems of static division limitation, boundary problem, insufficient real-time performance and the like in the prior art are solved, and the airspace resource utilization rate and the flight safety are improved.

The invention relates to the technical field of material physicochemical property monitoring of a multi-source sensing network, in particular to a multi-source sensing storage environment collaborative monitoring and early warning method, which comprises the following steps of: dividing grid units according to material storage types, binding sensor node coordinates, constructing a grid region reference model, and constructing a grid region reference model; integrating the volatilization characteristic parameters analyzed by the laboratory, historical monitoring data and an environment threshold value to generate a substance and environment relation matrix; the method comprises the following steps of: calling model parameters to invert a theoretical gas concentration value, and triggering finite element physical field simulation through residual analysis to generate a three-dimensional simulation field data set containing a temperature gradient and a diffusion path. And the dynamic correlation analysis precision and the early warning reliability of storage environment monitoring are improved.

The invention provides a meteorological element three-dimensional analysis method combining remote sensing and ground observation, and relates to the technical field of meteorological monitoring, and the method comprises the steps: obtaining remote sensing and ground observation data, and obtaining meteorological element data of a unified space-time reference; dividing the data into space-time grid units and performing quality evaluation to obtain a quality evaluation index; calculating a fusion weight coefficient according to the quality evaluation index based on an adaptive fusion algorithm of a dynamic weight, and performing weighted fusion on the meteorological element data in the grid units to generate an initial three-dimensional meteorological field; performing scale decomposition and reconstruction by adopting a spectrum analysis method to obtain an optimal three-dimensional meteorological field, constructing a self-adaptive tree-shaped composite analysis grid, analyzing evolution characteristics of a weather system in a multi-layer progressive mode, determining topological evolution parameters, and outputting a three-dimensional analysis result of meteorological elements.

The invention relates to a low-altitude logistics unmanned aerial vehicle path planning method, which comprises the following steps of 1, discretizing an urban low-altitude region into computable grid units, a dynamic-static obstacle classification modeling technology is combined to endow a static obstacle with a basic risk degree, a height attenuation factor is introduced to quantify the influence of a high-altitude obstacle on a low-altitude path, and a collision risk of a dynamic obstacle is dynamically evaluated through a time dimension risk degree prediction model; 2, based on an improved genetic algorithm, comprehensively considering flight time, height change, risk degree and dynamic risk cost through a multi-objective optimization objective function, and generating a globally optimal task allocation scheme; 3, generating a local optimal three-dimensional path considering meteorological conditions and dynamic obstacle influence by adopting an improved particle swarm algorithm; and 4, performing path planning to feed back actual flight time and energy consumption to task allocation for optimization.

The invention provides a numerical simulation method suitable for a three-dimensional spherical flame, and belongs to the field of electrical data processing, and the method comprises the steps: building a computational domain model according to the requirements of a computational scene, dividing a grid unit, and obtaining the node information of the required grid unit; establishing a Navier-Stokes equation set of a coupling chemical reaction and diffusion process model; estimating convection and diffusion term variable gradients in the conservation equation; calculating a time step length according to a set Kulang number, and carrying out time advancing; estimating an expected value of a velocity field, and solving a predicted velocity; the divergence of the velocity field and the correction value of the pressure field are calculated; obtaining a corrected velocity field; a self-adaptive grid means is introduced to dynamically adjust the grid resolution; performing iterative correction for multiple times until the velocity field and the pressure field converge to convergence conditions; performing spherical flame numerical simulation, and monitoring the convergence of the solution. According to the invention, the flame dynamics and cellular instability of the premixed spherical expansion flame can be reproduced.

The invention discloses a method and system for intelligently constructing a two-dimensional hydrodynamic model based on a large language model, and relates to the technical field of artificial intelligence and hydraulic numerical simulation, and the system comprises an intelligent region block division module which is used for analyzing geographic information data through the large language model LLM, recognizing topographic features and engineering facilities, and dividing region blocks; the self-adaptive grid generation module is used for setting different grid types and dynamically setting grid density according to different partitions; the terrain interpolation optimization module is used for selecting an interpolation algorithm to calculate a grid unit terrain and solve an optimal grid terrain based on a water level-reservoir capacity relationship verification result, and checking and marking an elevation abnormal mutation grid unit; and the two-dimensional hydrodynamic model construction module is used for outputting the grid and the time sequence data to a configuration file according to the format requirement of the target two-dimensional hydrodynamic model, and constructing the two-dimensional hydrodynamic model. According to the method, the problems of dependence on artificial experience, long time consumption of grid division and tedious adjustment are effectively solved, and integrated intelligent modeling is realized.

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 provides a logistics distribution demand prediction analysis method and system based on big data, and relates to the technical field of logistics distribution, and the method comprises the steps: dividing a distribution region into hexagonal grid units, building an order density evaluation model based on historical order data, extracting order propagation features through combining with a graph attention network, and carrying out the prediction analysis of the logistics distribution demand. And an external factor encoder is introduced to obtain an influence factor, probability correction is carried out by adopting kernel density estimation, and finally order demand prediction distribution with a confidence interval is obtained. The logistics distribution demand can be accurately predicted, the distribution efficiency is improved, the operation cost is reduced, and intelligent scheduling is realized.

The invention discloses a fire-fighting monitoring and generator set collaborative optimization multi-target constraint solving method, which comprises the following steps: carrying out real-time acquisition and structured processing on multi-source data, acquiring safety monitoring and equipment operation data through a sensor network, and storing the data in a distributed real-time database after preprocessing; carrying out dynamic thermodynamic field modeling and risk area division, discretizing a plant area as a grid unit, solving a heat conduction equation in combination with heat source modeling and boundary conditions, and dividing three levels of risk areas; building a multi-objective optimization model, defining three types of core constraints, building a normalized objective function, and realizing multi-objective decoupling through dynamic weight adjustment; according to an asymmetric load migration strategy, differential load adjustment is implemented according to an optimization result, an auxiliary system is linked, constraints are verified in real time, and closed-loop control is formed. According to the technology, the collaborative goals of safety risk controllability, system efficiency maintenance and equipment redundancy optimization are achieved, and the method is suitable for fire prevention and efficient operation in an energy production scene.

The invention relates to the technical field of land resource management, in particular to a land resource space optimization configuration method and system, and the method comprises the following steps: dividing grid cells based on a land research region, setting the initial land utilization state of each cell, comparing the land suitability of adjacent cells, updating the land utilization type, and determining the land utilization state of each cell; and recursively calculating the change in each time step, and iteratively executing the transfer specification to obtain a land utilization change state sequence. According to the invention, through recursive calculation and land utilization state updating, land utilization dynamic simulation, data continuity and regional adaptability enhancement, vacancy value filling and neural network adjustment, data and environmental factor feedback adjustment, prediction precision and trend judgment capability are improved, and economic, social and ecological benefits are quantified. The land use proportion and spatial layout are optimized, the land resource allocation efficiency is improved, policy adjustment and scene simulation are combined, differentiated decision support is provided, and social fairness and efficient resource utilization are promoted.

The invention relates to a low-altitude flight dynamic safety guarantee method and system based on airspace gridding, and relates to the technical field of low-altitude airspace management, and the method comprises the steps: constructing a gridding airspace; on the basis of the gridding airspace, labeling network attributes for each grid unit in real time and evaluating a low-altitude safety state; according to network attributes, extracting a grid center point of an airspace grid which is evaluated to be in a low-risk range as a safe flight point, and dividing the gridding airspace into a plurality of safe triangular grids; and according to the predicted trajectory of the aircraft, selecting a candidate point with the lowest risk cost from adjacent triangles of the safety triangular mesh, and introducing a risk cost function to dynamically adjust the distribution of flight path points. According to the invention, the flight path of the aircraft group can be dynamically adjusted according to the environment change, and the stability and safety of cluster task execution are improved.

The invention provides a geometric correction method and device for a satellite remote sensing image and a storage medium, and relates to the technical field of image correction, and the method comprises the steps: obtaining remote sensing image data of at least two different spectral bands, synchronously recording satellite attitude parameters and orbit position parameters, and carrying out the verification processing; based on the satellite attitude parameters and the orbit position, geometric coordinate conversion is carried out on each remote sensing image data, and target remote sensing image data is generated; dividing each piece of target remote sensing image data into a plurality of continuous grid units, and constructing a local geometric transformation model at the vertex of each grid unit; and resampling the original pixels in each grid unit according to the local geometric transformation model, and outputting a plurality of pieces of target remote sensing image data subjected to geometric correction. According to the invention, the geometric correction precision of the high-resolution satellite remote sensing image can be improved.

The invention relates to the technical field of weather forecasting, in particular to a weather clock forecasting system based on multi-source data fusion, which comprises an equipment terminal and a cloud end, the cloud end comprises a data acquisition module, a data processing module and a data transmission module, the data acquisition module is used for acquiring position information of the equipment terminal and multi-source data at the position, and the data processing module is used for processing the position information; the data processing module performs grid division on the target area range based on the position information of the equipment terminal to obtain grid units, and the data processing module is further used for mapping the position information of the equipment terminal into the grid units to obtain a target unit grid of the position where the equipment terminal is located. According to the method, the local feature data is introduced into the fine meteorological data of the target grid unit for correction, so that the finally obtained meteorological correction data has a finer space description capability, and the defect that prediction is inaccurate due to the fact that a traditional meteorological model ignores the local feature data under complex terrains or urban microclimate is overcome.

The invention relates to flight path planning of aircrafts, in particular to a collaborative flight path planning method and system for a low-altitude multi-aircraft mixed take-off and landing field, and the method comprises the steps: carrying out the three-dimensional subdivision of an airspace based on a Beidou grid code, and carrying out the real-time mapping of an airspace state of a three-dimensional grid unit; dividing the three-dimensional grid units according to the grid state; constructing a geometric track generation rule, and eliminating a track crossing risk; introducing a time dimension on the basis of a three-dimensional space, carrying out four-dimensional space-time conflict detection on all tracks, and carrying out conflict resolution according to a four-dimensional space-time conflict detection result; a double-queue dynamic scheduling mechanism is constructed, and the aircraft is guided to land safely through queue separation and polling distribution; according to the technical scheme provided by the invention, the defects that the avoidance path of the low-priority aircraft is difficult to reasonably plan and the flight safety of the aircraft in the take-off and landing stage cannot be effectively guaranteed in the prior art can be effectively overcome.

Disclosed in the present invention are a low-altitude air route risk map construction method and system based on hexagonal grid cells. The method comprises: on the basis of management and control requirements for different types of airspace, forming low-altitude airspace three-dimensional hexagonal hierarchical grids by means of multi-scale partitioning of an airspace horizontal plane and multi-scale partitioning of an airspace vertical plane; constructing an unmanned aerial vehicle flight risk assessment indicator system for the low-altitude airspace three-dimensional hexagonal hierarchical grids, and after optimization, constructing an unmanned aerial vehicle operational risk assessment model based on a dynamic Bayesian network, so as to generate multi-scale grid risk values; and combining the multi-scale grid risk values with geographic location information of a region, designing a hexagonal grid code index, and generating a dynamic multi-scale low-altitude air route three-dimensional hexagonal risk map. The present invention can improve the accuracy and real-time performance of unmanned aerial vehicle flight risk assessment of grids, quickly obtain low-risk grids of an unmanned aerial vehicle in a target region, and improve the safety of air route planning.

The embodiment of the invention provides a low-altitude digital management method and device based on an urban space-time grid, and the method and device achieve the precise division of an airspace through a plurality of layers of grid units by creatively building a unified urban space-time reference grid system. And designing a space-time grid data assignment model, processing multi-source heterogeneous data by using a deep neural network and a space-time convolutional layer, and realizing intelligent mapping of grid features through an attention mechanism. And constructing an airspace situation awareness model based on a graph neural network, and capturing a space-time dependency relationship among grid units in combination with a message passing mechanism to realize accurate prediction and hierarchical management and control of an airspace state. According to the method, the defects of the traditional technology in the aspects of airspace division, data processing, situation awareness and the like are effectively overcome, and the accuracy and practicability of urban low-altitude digital management are remarkably improved.

The invention provides an urban groundwater inrush risk assessment method based on a three-dimensional grid model and an XGBoost algorithm, and belongs to the technical field of geological disaster early warning. The method comprises the following steps: acquiring geological, environmental and historical disaster data in real time through a multi-source heterogeneous data interface; constructing a heterogeneous grid unit based on three-dimensional geological modeling, and dynamically binding parameters such as underground water level, fault activity and lithologic permeability coefficient; an XGBoost algorithm is combined with second-order Taylor expansion to optimize gradient calculation, and a nonlinear risk assessment model is constructed through Bayesian hyper-parameter tuning and SHAP value feature interpretive analysis; identifying contiguous dangerous areas based on spatial autocorrelation analysis and an adaptive threshold segmentation algorithm; and a risk thermodynamic diagram, geological section superposition and real-time early warning are realized through a 3D GIS platform. According to the method, the problems that a traditional method depends on a linear model, data is single and local risk description is insufficient are solved, and the accuracy, efficiency and engineering practicability of risk assessment are remarkably improved.

The invention discloses a self-adaptive octree three-dimensional magnetic method inversion method fused with abnormal region recognition, and belongs to the technical field of three-dimensional magnetic method data inversion processing. Comprising the steps that initial inversion is rapidly completed on a coarse grid, the overall contour of an underground structure is obtained, a fuzzy c-means clustering algorithm is introduced, and a target area with significant magnetic anomaly is automatically extracted from a coarse solution; and constructing a multi-level octree grid in the extracted abnormal region to realize fine subdivision of the complex geological boundary, and further executing high-precision inversion by using a fine model as a new starting point. The adaptive octree grid has the characteristics of high efficiency, small grid number and small data fitting difference. The process of coarse inversion, intelligent identification, local refinement and fine inversion can be circularly executed as required, and computing resources are dynamically allocated. Compared with a traditional global fine grid scheme, the inversion process has the advantages that the inversion precision is maintained and even improved, and meanwhile, the number of grid units is remarkably reduced.

The invention discloses an AGV path planning algorithm and a conflict-free strategy based on a Dlite algorithm. The algorithm and the strategy are suitable for multi-AGV collaborative operation in dynamic complex environments such as track equipment assembly workshops. The method comprises the following steps: (1) adopting an improved Dlite algorithm to realize dynamic path planning, and shortening the time consumed for updating a single AGV path through an incremental re-planning mechanism to adapt to the real-time change of dynamic obstacles such as personnel flow and equipment displacement; (2) constructing a space-time joint constraint model, dividing a workshop channel into limited grid units, and accurately marking the space-time occupancy state of the AGV through a time window; and (3) designing a hierarchical conflict resolution strategy, and when path crossing is detected, dynamically allocating priorities based on the task emergency degree and the AGV cargo carrying value, and realizing conflict avoidance through a speed adjustment window and path fine adjustment.

The invention relates to the technical field of industrial nondestructive testing, in particular to a paper drum defect full-inspection method based on machine vision, which comprises the following steps: step 1, constructing a curved surface self-adaptive optical environment: arranging annular low-angle strip-shaped light source arrays along the circumferential direction of a paper drum at equal intervals, and forming a dynamically adjusted set acute angle between the emergent direction of each light source and the surface normal of the paper drum, the acute angle is reduced along with the increase of the diameter of the paper barrel, and near-infrared coaxial diffused light sources are arranged at the two axial ends of the paper barrel; step 2, motion compensation three-dimensional reconstruction; 3, multi-modal defect hierarchical decision making: expanding the three-dimensional point cloud into a two-dimensional image along the circumferential direction, and constructing a dynamic reference grid with the grid cell size adaptively adjusted along with the pattern complexity on the two-dimensional image; cooperatively extracting geometric structure features, surface texture features and gluing seam features; and outputting defect classification through a three-level decision tree. By means of the method, efficient and accurate paper barrel full inspection can be achieved, and the automation level of a production line and the paper barrel quality control capacity are effectively improved.

The invention discloses a three-dimensional geologic model subdivision method based on an earth space grid structure, which adopts a plurality of types of earth space grid structures including longitude and latitude grids, octahedral projection grids and Hilbert curve filling grids to carry out three-dimensional division on the earth surface, and combines a layered recursive grid division technology to obtain a three-dimensional geologic model. Multi-level grid unit generation and geometric mapping from coarse resolution to high resolution are achieved, grid parameters are dynamically adjusted according to geologic features, large-size grids are adopted in a simple area to improve efficiency, small-size grids are adopted in a complex area to capture details, redundant units are eliminated in combination with a grid optimization algorithm, and the efficiency is improved. The accuracy and the calculation efficiency of the model are improved; a global unique identifier is generated through a GeoSOT-3D hierarchical recursion technology, union set, intersection and difference set operations of grid units are completed in combination with efficient Boolean operation, and the integrity and topological consistency of geologic model subdivision are ensured.

The invention discloses an intelligent fire evacuation system and method based on dynamic environment perception, and relates to the technical field of fire fighting and safety protection, and the method comprises the steps: collecting real-time data in a fire scene; dividing the evacuation area into a plurality of grid units by using a two-dimensional rasterization technology to form a real-time updated environment map; in combination with dynamic modeling data, an optimal evacuation path is generated by using an improved path planning algorithm; an evacuation path is transmitted to trapped people in real time in a visual and auditory mode, and the visual and auditory mode comprises the use of a dynamic indicator lamp, a marker lamp, an electronic screen and a voice broadcast system; the guidance content is adjusted in combination with real-time data, and the evacuation efficiency is optimized; fire fighting equipment is linked, and the evacuation path environment is optimized; and the evacuation state is monitored through a dynamic data feedback mechanism. According to the invention, an evacuation path is calculated and updated in real time by integrating a sensor network, fire scene dynamic modeling and an intelligent path planning algorithm, and a safe and efficient evacuation scheme is provided for trapped people in a fire.

The invention relates to the field of mobile robot motion planning, and discloses a quadruped robot motion planning method and system based on height self-adaption. The method comprises the following steps: constructing 3D and 2D local obstacle maps according to coordinate transformation, and calculating a grid vertical distance through ray casting to generate a 2.5 D local obstacle map; determining an initial control sampling space by combining speed, acceleration and height constraints; discretizing a sampling space and simulation time, deducing a control value sequence and recursively calculating a local trajectory; then calculating a shaft alignment bounding box in a directed bounding box of the robot, screening grid units in the shaft alignment bounding box, comparing the height of a machine body with the allowable height of grids, and rejecting collision trajectories to obtain a feasible trajectory set; and finally selecting an optimal track by the evaluation function, extracting a control value and sending the control value to hardware for execution. According to the method, the 2.5 D local obstacle map containing height information is constructed, the four-dimensional control sampling space is designed, and the robot height self-adaptive adjustment is realized in combination with the collision detection and evaluation function.

The invention relates to the technical field of goods shelf detection, in particular to a self-detection method and device for goods shelf settlement, and provides the following scheme: obtaining a top view image through an image sensor arranged right above the top of a goods shelf, dividing the image into a plurality of grid units, and positioning a rectangular geometric shape by utilizing Hough transform; and screening a plurality of to-be-detected areas in combination with the edge features. For an area to be measured, homographic registration and ortho-rectification are carried out based on a reference image, a displacement field is obtained by adopting sub-pixel-level dense registration, and a geometric parallax component field corresponding to imaging parameters is obtained through robust estimation. And under the hypothesis of small deformation, inverting the parallax into a pixel normal distance, and carrying out weighted aggregation on the local region to obtain a local distance measurement result. And by iteratively combining adjacent grids, determining a settlement area boundary, and finally outputting a settlement detection result. Millimeter-level settlement quantification can be realized under a single-frame image, hardware transformation is avoided, and the method is suitable for automatic detection and long-term monitoring of multi-specification goods shelves.

The invention provides an urban low-altitude AI collaborative management and control method and system based on a Beidou grid code and a block chain, and the method comprises the steps: obtaining the information of an unmanned plane, and searching a corresponding evidence storage record in the block chain; the unmanned aerial vehicle information is obtained through a low-altitude intelligent flight control box compatible with various unmanned aerial vehicle protocols; verifying the qualification of the unmanned aerial vehicle through the evidence storage record, and bringing the unmanned aerial vehicle into a distributed collaborative management and control network for unified scheduling when the unmanned aerial vehicle passes the qualification authentication; setting flight authority according to airspace classification of the airspace map and generating a corresponding geofence to limit the flight range of each unmanned aerial vehicle; the airspace map is divided into space-time grids with specified resolution based on Beidou grid codes, state information of each grid unit in the space-time grids is analyzed based on an AI reinforcement learning engine, and flight paths of the unmanned aerial vehicles are dynamically optimized. Flight proxy of the unmanned aerial vehicles is realized through the low-altitude intelligent flight control box, unified scheduling of the multi-source heterogeneous unmanned aerial vehicles is realized, and the airspace use efficiency is improved.

The invention discloses an urban area runoff pollutant transportation process simulation forecasting method, which is characterized in that a building area runoff production calculation method under the influence of a wind field is provided, and the building area runoff production calculation method is coupled with an SWMM rainfall flood model; secondly, constructing a two-dimensional hydrodynamic model based on an FVCOM and a Lagrange particle model, coupling the two-dimensional hydrodynamic model with the SWMM rainfall flood model, and driving the two-dimensional hydrodynamic model by taking the overflow flow and the pollutant concentration simulated by the SWMM rainfall flood model as boundary conditions; simulating a transportation process of pollutants along with evolution of surface ponding by establishing a'concentration-quantity 'conversion relation between the pollutants and particles at grid units and nodes, and obtaining a time sequence pollutant concentration data set; and training and establishing a pollutant concentration prediction model based on the obtained data, and then predicting the pollutant concentration of each prediction point after specific time in an actual rainfall event. According to the method, the runoff pollutant concentration can be predicted, and the urban non-point source pollution risk under the rainstorm weather is reduced.

The invention provides a grouting simulation method and system based on time sequence interaction and self-adaptive grid reconstruction, and relates to the technical field of grouting engineering.The grouting simulation method comprises the steps that monitoring data of the grouting construction process are obtained, the monitoring data are input into a grouting diffusion prediction model, a grouting pressure prediction value is output, and a series of prediction responses are conducted based on the grouting pressure prediction value. Obtaining a grouting pressure predicted value, a boundary point set of the grouting diffusion area, the area and the volume of the grouting diffusion area and a diffusion response result of the pressure distribution diagram; constructing a geometric model of the grouting area based on the diffusion response result, and performing grid unit division and grouting diffusion area mapping on the geometric model; and performing local grid encryption and reconstruction on the grid units according to a density control rule to obtain a structured grid data model, adaptively updating the structured grid data model by using real-time monitoring data, and performing real-time visual grouting optimization control. According to the invention, the high-precision prediction response of grouting is improved.

The invention belongs to the technical field of photovoltaic power prediction, and particularly relates to a distributed photovoltaic power prediction method, which comprises the following steps of: acquiring multi-source information of a plurality of photovoltaic power stations; constructing a gridding geographic space model based on the collected geographic position information; mapping the geographic position information of each photovoltaic power station to a corresponding space grid unit; for each space grid unit, fusing multi-source information of all photovoltaic stations in the grid, and constructing grid-level multi-source features including meteorological features, power features and geographic position features; and the grid-level multi-source features are inputted into a space-time Transform model which is already trained, and the space-time Transform model outputs a photovoltaic power prediction value. According to the method, the photovoltaic power prediction value of the target at the future moment is obtained through joint modeling of the time sequence dependence and the spatial relationship, so that the prediction precision and stability of the future power change trend of the distributed photovoltaic power station are improved.