1453 results about "Grid cell" patented technology

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.

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 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 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.

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 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 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.

The invention discloses a mine comprehensive management and control system based on spatio-temporal data, and the system comprises an acquisition module which is used for collecting and synchronizing observation data of a mining area distributed ground sensor and unmanned plane remote sensing equipment, and mapping the observation data to a three-dimensional grid unit; the digital twinning modeling module is used for constructing a fragmented gridding four-dimensional digital twinning model; the dynamic micro hypergraph modeling module dynamically constructs a node-hyperedge structure based on spatial proximity, time relevance and the like; the propagation module is used for realizing main variable weighted propagation and gating residual error updating; the anomaly detection module is used for automatically identifying and registering time-space anomaly events of the grid units; and the scheduling optimization and instruction module is used for outputting a scheduling priority and a control instruction based on the main variable, the abnormal distribution and the hypergraph link. According to the invention, intelligent integration, abnormal accurate identification and scheduling optimization control of mining area multi-source spatio-temporal data are realized, and the method has the advantages of high real-time performance, intelligent management and control and adaptability to complex scenes.

The invention discloses a high boundary sensitivity three-dimensional inversion method based on an electromagnetic gradient constraint matrix, and belongs to the technical field of electromagnetic signal inversion, and the method comprises the steps: constructing an initial inversion model and a target function, and introducing a model roughness item based on electromagnetic gradient constraint into the target function; calculating the electromagnetic gradient anomaly response at each measuring point under each measuring frequency, obtaining an electromagnetic gradient anomaly response vector of each measuring point, performing normalization processing, combining into a matrix, obtaining an overall normalized observation gradient anomaly matrix, and constructing an electromagnetic gradient constraint matrix in a data space; converting the electromagnetic gradient constraint matrix in the data space into a model space through a frequency-depth weighted mapping matrix; and solving the target function by adopting an iterative optimization algorithm until the model converges. According to the method, the gradient constraint matrix based on the electromagnetic field is introduced, the dynamic adjustment of the change amplitude weight of each grid unit in the roughness item of the model is realized, and the target boundary identification precision is improved.

The invention relates to the technical field of meteorological data processing, in particular to a training data acquisition method and system for a power transmission line fault prediction model. The method comprises the following steps: constructing a multi-source heterogeneous original data pool containing severe convection meteorological data, power transmission line operation and fault data and geographic space data; performing time-space reference unified processing and grid division on the data; mapping the line paragraph to a grid unit, identifying a severe convection event and dividing an influence area through a density clustering algorithm based on a multi-parameter collaborative threshold value, calculating a grid exposure degree, and combining a strength grade weight and a terrain correction weight to calculate a space correlation strength; setting a differentiation time association window to calculate a time factor according to the severe convection dominant type, calculating association credibility based on the space association strength and the time factor, and screening high-credibility association results; and performing attribution labeling on the fault information to obtain a training data set. The problem of low training data quality is solved.

The invention discloses a safe evacuation path planning method under a strong wind condition of island engineering. According to the technical scheme, the method is characterized by comprising the steps that firstly, the physical space of an engineering site is discretized into a plurality of grid units, and a computable graph structure is constructed; then, acquiring multi-source dangerous area information generated in real time, and quantifying the dangerous area information into a dynamic risk value of each grid unit; then, acquiring the real-time position of the constructor as a starting point, and presetting an emergency exit as an ending point; and finally, on the constructed risk grid map, a heuristic search algorithm such as A * is adopted, and the geometric distance and the dynamic risk value between grid units are fused by a cost function of the heuristic search algorithm, so that an optimal evacuation path considering safety and efficiency is quickly searched. According to the method, the safety risk is quantified and fused into the path optimization process, so that the quick response to the dynamic environment and the real-time re-planning of the path are realized, and the efficiency and safety of personnel evacuation under the emergency condition are remarkably improved.

The invention relates to the technical field of task scheduling, in particular to an intelligent multi-unmanned aerial vehicle cooperative task execution scheduling system and method. The method comprises the following steps: periodically scanning a space in front of a low-altitude flight path of an urban building dense area through a visual SLAM system, and processing visual information in real time by using an airborne edge calculation unit to obtain near-earth space point cloud data of a continuous time sequence; projecting obstacle height information in the near-earth space point cloud data to predefined flight grid units to obtain grid obstacle height data of each grid unit at different time points; and comparing the variation values of the grid obstacle height data in the same grid unit in adjacent time periods, and calculating the obstacle height change frequency in unit time. According to the method, the dynamic change characteristics of the obstacles in the environment can be quantitatively analyzed, the high-risk area is identified, and the challenge brought to multi-unmanned aerial vehicle cooperative task execution scheduling by the complex environment of the urban building dense area is overcome.

The invention provides a park environment risk traceability and grading early warning management and control method, and belongs to the technical field of risk early warning based on computer data processing. The method comprises the following steps: firstly, collecting video monitoring, environment and equipment state sensor and real-time meteorological heterogeneous data in a whole domain and a peripheral specific buffer zone of a park, and preprocessing the data to obtain standardized multi-source data; dividing grid units by taking a park boundary coordinate as a reference and distributing a unique standardized code, associating standardized multi-source data to a corresponding grid according to a rule, and performing fusion calculation on a grid-level risk characteristic value of each grid; then combining real-time meteorological data, tracking a risk diffusion path through a reverse derivation algorithm, positioning a risk source position and a core range, and outputting related parameters; and finally, grid risk levels are divided according to the grid risk characteristic values and a preset threshold interval, a corresponding early warning mechanism is triggered, and early warning information is output. According to the invention, the precision of environmental risk positioning, the reliability of traceability analysis and the refinement degree of an early warning result are improved.

The invention provides a multi-dimensional Hash acceleration-based grid ray intersection method, which comprises the following steps of: acquiring original model data, and carrying out standardized preprocessing on the original model data to obtain standardized scene data; self-adaptive space grid division is carried out based on the preprocessed standardized scene data, space coordinates of three-dimensional grid units are mapped to a hash table to construct a one-dimensional index of a space grid, and triangular patches of the scene data are associated with the space grid; performing direction parameter decomposition on rays to be intersected, discretizing direction vectors of the rays, and constructing a ray direction composite hash key; and executing mixed intersection calculation in a mode of combining multi-level cache query construction, direction pruning and invalid intersection filtering and space hash traversal intersection, and determining intersection point information of the ray and the geometry. According to the method, the space grids and the ray direction data are converted into the hash values to be stored, the occupied memory is small, and the ray intersection efficiency is improved.

The invention belongs to the technical field of engineering simulation data processing, and particularly discloses a finite element grid graph structure construction method and system, a terminal and a medium. Comprising the following steps: analyzing original full-amount grid data exported by finite element simulation, segmenting unstructured grid data into a grid vertex coordinate set and a grid unit mark number set, and constructing a graph edge topological structure corresponding to a grid based on a unit mark number relationship; on the basis, loading physical field data and adopting a tolerance-based coordinate matching algorithm to realize accurate mapping of physical field labels and material attributes with grid nodes; and generating standardized finite element grid graph data which can be directly used for graph neural network processing. By means of the method, high-consistency and high-physical-reliability conversion from the finite element simulation data to the graph structure data is achieved, and the physical field modeling and simulation acceleration capacity based on graph learning is improved.

The invention belongs to the technical field of forest disaster monitoring and early warning, and particularly discloses and provides a forest fire spreading prediction method based on multi-dimensional features, which comprises the following steps: dividing a target area into grid units, collecting an infrared thermal imaging data sequence through an unmanned aerial vehicle, dynamically evaluating fire intensity indexes of the grid units, and predicting the fire intensity indexes of the grid units. Identifying each fire spreading path, evaluating the spatial proximity of each grid unit relative to the path, marking a fire occurrence area, and determining the path intersection density in each grid unit; fusing the fire intensity index, the path space proximity and the path intersection density, calculating to obtain a comprehensive fire spreading risk index of each grid unit, and generating a forest fire spreading danger level zoning map based on the index; according to the method, the fire spreading risk of each grid unit is comprehensively evaluated by fusing the fire intensity index, the path space proximity and the path intersection density, and refined and three-dimensional evaluation of the risk of the unburned area is realized.

A low-altitude passable map generation method based on multi-source environmental factors comprises the steps that firstly, a low-altitude airspace is divided into three-dimensional grid units serving as basic space units for environmental factor modeling and passability calculation; performing hierarchical expression on multi-source environmental factors such as meteorology, communication, navigation, monitoring, buildings, terrains, underlying surface personnel and buildings, airspace properties, airspace flow, electromagnetism and the like in each grid unit; setting a passable threshold value of each environmental factor according to different types of low-altitude aircrafts, and judging whether the grid is passable for each type of aircrafts based on the actual grade of each environmental factor in the current grid; and superposing the trafficability judgment results of the grids under all environmental factors, and generating a low-altitude trafficability map reflecting the overall trafficability. According to the method, the passability under various environmental factors is judged, the passable map of the low-altitude aircraft is constructed through superposition, and the method is suitable for passable map generation under the complex and changeable low-altitude flight environment.

The invention relates to an unmanned aerial vehicle track optimization method, system and device in an urban high-density airspace and a medium, and the method comprises the steps: dynamically dividing the urban airspace into space-time four-dimensional grid units of different scales according to the vertical height and the time axis based on the real-time airspace traffic density and environmental conditions; establishing a guide vector field pointing to a target position in each four-dimensional grid unit, and when an obstacle and / or a flight conflict is detected, generating a local disturbance vector and dynamically correcting the guide vector field in real time so as to autonomously generate a candidate flight trajectory; and calculating the energy consumption of the candidate flight path based on an unmanned aerial vehicle dynamics model and an environment model, and selecting a path with the lowest total energy consumption as a final flight path under the condition of meeting safety constraints and task time requirements. Compared with the prior art, in a complex dynamic environment of an urban high-density airspace, unification of real-time efficient planning of the flight path of the unmanned aerial vehicle, safe obstacle avoidance and low-energy-consumption optimization is realized.

The invention discloses a deep tunnel surrounding rock mechanical parameter inversion method based on a three-dimensional brittle failure zone contour and sparrow optimization algorithm-information vector machine (SSA-IVM) combined optimization algorithm. The engineering technical problem that due to the fact that excavation instantaneous displacement is difficult to monitor, deep tunnel surrounding rock mechanical parameters are difficult to reasonably obtain through displacement back analysis is solved. The method comprises the following steps: firstly, constructing a tunnel FLAC3D numerical simulation model with the same ground stress condition at the occurrence position of a three-dimensional brittle failure zone; secondly, taking an absolute error between the total number of computational grid units in the actually measured brittle failure zone and the total number of computational grid units entering a plastic state in the range of the actually measured brittle failure zone after calculation of the FLAC3D numerical model as an optimization objective function; and then, by taking the tunnel surrounding rock mechanical parameters as optimization variables and taking a target function reaching a global minimum value as a target, performing global optimization by combining a tunnel FLAC3D numerical model and adopting an SSA-IVM joint optimization algorithm, thereby obtaining reasonable tunnel surrounding rock mechanical parameters.

The invention discloses a multi-source information real-time identification and positioning method for a coal mine rock burst risk area. The method comprises the following steps: integrating a micro-seismic system, a rock noise system, a stress system and a geological system to construct an acquisition network; extracting multi-source characteristic indexes and carrying out space-time unification; establishing a dynamic weighted risk fusion model to calculate a comprehensive risk index RI of a three-dimensional grid unit, wherein the weight of the comprehensive risk index RI is dynamically and adaptively adjusted along with mining; based on the RI value, a high-risk area is identified and positioned through three-dimensional visualization, and graded early warning information is generated; according to the invention, a time-space synchronous multi-source information acquisition network is constructed, and uniform preprocessing and feature extraction are carried out on four types of data of microseism, rock noise, stress and geologic structure, so that deep fusion and collaborative analysis of multi-source information are realized, and an information island phenomenon existing in traditional monitoring is overcome; and the judgment on the rock burst risk is more comprehensive.

The invention provides an operation situation analysis method and device, equipment and a storage medium, and relates to the technical field of data processing, in particular to the technical field of intelligent transportation, automatic driving, large models and data analysis. The specific implementation scheme is as follows: according to a plurality of geographic grid units formed by dividing a target operation area, carrying out gridding aggregation on operation data in a space-time knowledge graph to obtain aggregation operation indexes of the plurality of geographic grid units; generating an operation thermodynamic diagram representing demand distribution according to the current order data of the plurality of geographic grid units; determining the scheduling efficiency of the plurality of geographic grid units according to the aggregation operation index; and for a target grid unit in the operation thermodynamic diagram, predicting an expected operation index of the target grid unit at a future target moment according to the current operation index, the historical operation index and the scheduling efficiency of the target grid unit. According to the scheme provided by the embodiment of the invention, the real-time diagnosis and future prediction of the operation situation are realized by aggregating, presenting and analyzing the data in the space-time knowledge graph.

The invention provides an AGV path planning method and system based on an improved grey wolf optimization algorithm, and relates to the field of path planning, and the method comprises the steps: constructing an AGV plane moving environment, and building a two-dimensional plane environment model through a grid method; dividing the operation area of the AGV into a plurality of uniform grid units with the same size, and numbering the grid units; based on a standard grey wolf algorithm, a coding and decoding strategy, a population updating discretization strategy, a convergence factor adaptive adjustment strategy and a conflict detection and repair strategy are constructed, an improved grey wolf algorithm is obtained to generate an initial distribution path of each AGV, and gradual optimization and correction are carried out to obtain an optimal target path. According to the method, the concept of timestamps is introduced in the decoding process, so that node conflicts and edge conflicts which are easily generated in AGV path generation are considered. And different types of conflicts are repaired, so that the occurrence of collision events in the distribution process of the multiple AGVs is effectively reduced.

The invention belongs to the technical field of data processing, and particularly relates to a ground pressure monitoring and early warning method and system based on multi-source data fusion, and the method comprises the steps: constructing a space-time distance improvement DBSCAN algorithm of micro-seismic events, carrying out the clustering of the micro-seismic events through the improved DBSCAN algorithm, and calculating the risk source index of each micro-seismic event cluster; in the three-dimensional grid space, acquiring risk potential energy of each grid unit according to the risk source index, the distance between the micro-seismic event cluster and the grid unit and the stress state of the grid unit; calculating a ground pressure risk index by combining the current value of the risk potential energy and the time change of the risk potential energy; and performing multi-level early warning according to the ground pressure risk index. Ground pressure early warning is carried out by fusing the microseismic space-time aggregation characteristics, the stress field state and the risk dynamic evolution trend, and the accuracy and timeliness of ground pressure disaster early warning are improved.

The invention relates to the field of impact failure full-process prediction of complex-structure die castings, and discloses a data-driven impact failure full-process prediction method for complex-structure die castings, which comprises the following steps: step 1, performing mold flow simulation on a target part to obtain the solidification time and porosity of any position of the target part; 2, the solidification time and porosity of any position of the target part are input into a multi-layer perceptron model, and the multi-layer perceptron model outputs mechanical parameters of any position of the target part; the multi-layer perceptron model is trained based on data of actually measured mechanical properties; 3, acquiring a collision simulation grid model of the part, mapping the mechanical parameters to each grid unit of the collision simulation grid model, and generating an input file for collision simulation; and 4, performing collision simulation based on the input file in the step 3.

The invention provides an interference signal identification and coping method, which is used for a ground control platform, and comprises the following steps: a data receiving step: receiving telemetry data; an information screening step: screening receiver positioning information, a navigation signal carrier-to-noise ratio and receiver state information in the telemetry data; a grid unit generation step: performing grid division on the three-dimensional monitoring space to generate a plurality of mutually independent grid units; an anomaly detection step of determining a suspected interference unit based on a machine learning algorithm; an interference area determination step: determining a statistical space, and determining an interference area based on a machine learning algorithm; an interference situation map generation step of generating an interference situation map based on a machine learning algorithm; and an anti-interference strategy generation step: a first working mode and a second working mode are determined, and the anti-interference capability of the first working mode is higher than that of the second working mode. According to the invention, the problems of slow identification and response to interference signals and untimely response of a receiver in the prior art are solved.