1923 results about "Grid cell" patented technology

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 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 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 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 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 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 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.

The invention provides a photovoltaic power station cleaning robot path planning method, medium and system, and belongs to the technical field of photovoltaic power station operation and maintaining.The photovoltaic power station cleaning robot path planning method includes the steps that firstly, a photovoltaic panel grid division model is established, and stain distribution information is obtained through the image processing technology to construct a stain distribution matrix; secondly, calculating a cleaning water demand matrix based on stain distribution, and meanwhile, considering an influence relationship among vertical column grid units; determining the cleaning duration by combining the efficiency of the cleaning device; constructing a robot energy consumption model; calculating an optimal cleaning path by applying a resource optimization distribution function; integrating the environmental parameters and inputting the environmental parameters into a photovoltaic cleaning neural network model to predict the actual cleaning difficulty; a cleaning strategy is dynamically adjusted accordingly; and executing the final path and recording data to update the related model. According to the method, through real-time sensing and analysis of the stain distribution characteristics, intelligence of path planning of the cleaning robot is realized, and the technical problem that the cleaning robot cannot perform intelligent path planning according to the surface stain distribution characteristics of the photovoltaic panel is solved.

The invention discloses a zero-carbon park planning method and device, a storage medium and a computer program product, and relates to the technical field of carbon emission management, and the method comprises the steps: collecting the original space data of a target park, and obtaining the environment parameters of the target park, constructing a park three-dimensional digital twinborn model according to the original spatial data of the target park and the environmental parameters of the target park; dividing the park three-dimensional digital twinborn model into a plurality of grid units, respectively calculating the photovoltaic theoretical generating capacity of the plurality of grid units, and generating a plurality of candidate zero-carbon park planning schemes according to the photovoltaic theoretical generating capacity; performing economic cost measurement and calculation and carbon efficiency income measurement and calculation on each candidate zero-carbon park planning scheme to obtain an economic carbon efficiency measurement and calculation result; and selecting an optimal zero-carbon park planning scheme from the candidate zero-carbon park planning schemes according to an economic carbon effect measurement result. Zero-carbon park quantitative simulation is carried out in the planning stage, high cost of later modification of the park is avoided, and the economic carbon-efficiency ratio of the zero-carbon park is increased.

The invention discloses a dynamic noise analysis method and device for an urban elevated road and a medium, and belongs to the technical field of noise monitoring and analysis. The method comprises the following steps: acquiring a three-dimensional geographic information model and real-time traffic flow data of a target elevated road; processing the three-dimensional geographic information model based on a sound wave propagation attenuation model to generate a noise propagation path grid; fusing the real-time traffic flow data with a noise source intensity calculation model to generate a dynamic noise source intensity sequence; calculating a real-time equivalent sound pressure level of each grid unit through a noise superposition algorithm in combination with the dynamic noise source intensity sequence and the noise propagation path grid; performing rolling prediction on the real-time equivalent sound pressure level based on a time window sliding mechanism to generate a noise space-time distribution thermodynamic diagram; and according to the noise space-time distribution thermodynamic diagram, identifying a noise exceeding area, and outputting a dynamic noise evaluation report. According to the method, the technical effect of dynamically predicting and analyzing the noise of the elevated road is achieved.

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 discloses a predictive order dispatching system based on digital twinning, and relates to the technical field of intelligent order dispatching, the predictive order dispatching system comprises four core modules: an urban transport capacity digital twinning construction module integrates order dispatching platform historical orders, real-time traffic flow and urban GIS data, constructs and updates a twinning model, and divides standard grid units; a short-term order demand prediction module obtains grid order, traffic and environment feature sequences based on the model, and predicts order quantity and thermodynamic diagrams in 15-30 minutes in the future; a transport capacity supply and demand gap early warning module analyzes the gap and generates early warning; the active intervention and optimization module generates an instruction according to a preset strategy, dispatches transport capacity through order pre-dispatching and the like, and then evaluates and optimizes the strategy according to feedback. The system realizes order pre-judgment, accurate transport capacity allocation, reduction of supply and demand imbalance, shortening of user waiting time, reduction of operation cost, and improvement of delivery efficiency and service quality.

The invention provides a tunnel defect automatic identification and evaluation method and system, and relates to the technical field of engineering monitoring, and the method comprises the steps: obtaining the surface deformation data and internal stress distribution data of a tunnel collected by each sensing node, and carrying out the unified clock calibration and vibration data screening, generating a multi-source data packet; feature level fusion is carried out on the multi-source data packet, a fusion feature matrix is generated, and the stress abrupt change points are the sensing nodes with stress abrupt change; and based on a plurality of grid units in the three-dimensional initial model of the tunnel, embedding the fused feature matrix according to the timestamps and spatial localization of the sensing nodes to generate a feature structure model. The tunnel structure can be dynamically monitored in all directions, the space-time coupling characteristics of the defects in the structure evolution process are accurately and effectively captured, and reliable decision support is provided for early warning, traceability, treatment and the like of the tunnel quality defects.

The invention discloses a real-time flight conflict detection method based on digital gridding. The method comprises the steps of dividing airspace grids, managing a three-dimensional space, dividing nodes according to a spatial range, and storing a specified number of grid units in each node; when the node capacity exceeds a threshold value, the node capacity is automatically subdivided into a plurality of sub-nodes, and recursive division of the space is realized; realizing grid query and insertion through spatial cross judgment; acquiring real-time flight path data of the aircraft, and mapping the real-time flight path data into the corresponding grid units according to the position of the real-time flight path data in the airspace; the track data comprises latitude and longitude, height and speed; and dividing conflict detection tasks of each grid unit and adjacent grids into a plurality of sub-tasks for parallel processing, generating a global conflict result set, and realizing large-scale real-time flight conflict detection. According to the invention, the flight conflict can be rapidly and accurately detected.

The invention relates to the technical field of meteorological disaster early warning and emergency management, and discloses a meteorological disaster risk assessment and prevention method based on artificial intelligence, and the method comprises the steps: obtaining multi-source heterogeneous data, carrying out the cleaning, alignment and standardization processing of the multi-source heterogeneous data, and constructing a multi-dimensional feature data set; based on the multi-dimensional feature data set, outputting predicted meteorological elements of the target area in a future preset time period through a meteorological prediction model, extracting interaction features of the predicted meteorological elements and non-meteorological factors from the multi-dimensional feature data set, and inputting the predicted meteorological elements and the interaction features into a long and short term memory-convolutional neural network hybrid model to obtain a long and short term memory-convolutional neural network hybrid model; outputting the meteorological disaster risk probability and risk level of each grid unit in the target area; based on the meteorological disaster risk probability and the risk level, differential prevention instructions for different risk level areas are generated, and the technical problems that in an existing meteorological disaster risk assessment and prevention method, multi-source data integration is difficult, and meteorological prediction precision is insufficient are solved.

The invention discloses a multi-source environment monitoring data fusion analysis and abnormity early warning method based on deep learning. According to the method, the spatial attention weight is calculated by fusing the feature similarity and the geographic distance, and the modeling precision of the complex spatial dependency relationship between the environmental monitoring nodes is remarkably improved. Specifically, the model firstly divides a monitoring area into fine grid units, each unit serves as an independent node to participate in calculation, the direction consistency of node feature vectors is quantified through cosine similarity, and meanwhile, a geographic distance attenuation factor is introduced to dynamically adjust weight distribution. The double consideration enables nodes with similar environmental characteristics in adjacent areas to obtain higher attention, and effectively avoids the problem of spatial correlation misjudgment easily occurring in a complex terrain in a traditional fixed adjacent matrix. When local pollution diffusion occurs in a monitoring area, the model can quickly position a propagation path of a core pollution source and surrounding affected nodes, and the space correlation identification efficiency is improved.

The invention belongs to the technical field of industrial crane control, and provides a bridge crane motion path planning method and system, and the method comprises the steps: uniformly dividing the motion environment of a bridge crane into grid units, recognizing a dynamic obstacle, endowing each grid unit with a state value, carrying out the track prediction of the dynamic obstacle, and carrying out the prediction of the motion path of the bridge crane. The method comprises the following steps: generating a time grid state matrix, constructing a time sequence passing grid set sequence, exploring a shortest path by adopting an improved A * algorithm, exploring multiple feasible paths by adopting an RRT algorithm, marking change nodes, screening the multiple feasible paths based on the shortest path, marking an initial path, and sorting an initial path set and a path node sequence; and quantitatively calculating the path tortuosity of the initial path, screening based on the path tortuosity, marking an optimal path, analyzing the load swing of the load at the change node, calculating the swing envelope range of the load, marking a risk change node, and screening to obtain an optimal path.

The invention discloses an evaluation method for dynamically monitoring carbon sink of a grassland and wetland ecosystem, which relates to the field of ecological environment monitoring and comprises the following steps: acquiring historical carbon flux observation data of a target area; dividing the target area into a plurality of space grid units, taking each grid unit as a node in the graph structure, and establishing graph edge connection between adjacent nodes; extracting ecological features for each node; calculating the edge weight of each edge in the graph structure according to the ecological characteristic difference between the adjacent nodes; inputting the graph structure containing the node features and the edge weights into a graph neural network model, and training the graph neural network model by using historical carbon flux observation data as labels; and predicting the carbon flux of an unobserved node or a future time period node by using the trained graph neural network model. The method solves the problem that a traditional model is large in estimation error of the carbon flux in the ecological boundary region, and can remarkably improve the carbon exchange prediction precision of the boundary region.

The invention discloses a hydraulic engineering monitoring and early warning system and method based on artificial intelligence, and belongs to the technical field of hydraulic engineering. Gate operation data are collected through a gate sensor group to form a gate state data set; floating object data are collected through a high-definition camera and an image recognition technology, and the floating object data are marked as floating substance points containing volume, form and coordinates; dividing a flood discharge area into grid units, and recording floating object information in each grid to form a spatial distribution data set; predicting a migration path and duration based on the water flow data and the floating object parameters, marking a grid overlapped with the gate area as a core grid, and identifying a high-risk grid; dynamically associating floating object parameters and gate state parameters in the core grid, calculating an association coefficient, and positioning a potential contact risk grid; according to the floating object accumulation rate and the correlation coefficient, early warning grades are divided, and gate blockage early warning is generated; meanwhile, high-risk floating objects passing through the gate are tracked, and the path of the high-risk floating objects is predicted to generate salvage early warning reminding.

The invention discloses an unmanned aerial vehicle channel management method, platform and device under low-altitude economy, and relates to the technical field of unmanned aerial vehicle navigation and path planning, and the method comprises the steps: determining a preset airspace range, dividing a height layer and longitude and latitude grid units, and constructing a three-dimensional airspace resource map; distributing state label generation mapping; receiving a flight task; performing conflict detection and mediation to generate a target decision in combination with the atlas and the mapping; and executing channel management according to the decision to realize low-altitude unmanned aerial vehicle channel management. The technical problem that a traditional unmanned aerial vehicle channel management mode lacks a low-altitude airspace three-dimensional refined modeling, dynamic conflict detection and priority scheduling mechanism in a low-altitude economic scene is solved, and refined management, real-time conflict avoidance and differentiated resource scheduling of a low-altitude unmanned aerial vehicle channel are achieved. And thus, the airspace utilization efficiency and the flight safety are improved.

The invention discloses an urban low-altitude-oriented grid dynamic evaluation and intelligent route planning system, and belongs to the field of urban low-altitude airspace and routes. And the data processing module is used for inputting static data including the task number, the task time, the unmanned aerial vehicle number, the take-off and landing field number and the total airspace, the continuous airspace is divided into grid units by a project, and a quantitative evaluation model with seven factors including the underlying surface, the communication quality, the navigation quality, the meteorological environment, the electromagnetic environment, the noise and the countering as the core is constructed. The airworthiness score of each grid is calculated through normalization processing and weighted summation, a real-time and scientific decision basis is provided for path planning of the unmanned aerial vehicle, crossing from static planning to dynamic evaluation is achieved, and a set of airspace gridding dynamic evaluation and task planning system is provided and implemented. The system aims to cope with flight risks caused by multiple factors such as complex underlying surfaces, dynamic weather and communication navigation interference in urban low-altitude flight, and the safety and reliability of unmanned aerial vehicle logistics are improved.