43 results about "Environmental model" patented technology

The invention discloses a dynamic planning method for an air route of an urban low-altitude unmanned aerial vehicle. The method comprises the following steps: firstly, constructing an urban low-altitude environment model based on an airspace rasterization technology, fusing multi-dimensional constraint factors such as geographic data, meteorological conditions and airspace control information through multi-source environment information, and accurately calibrating the position of a take-off and landing point; secondly, a dynamic grid availability evaluation model is established in combination with environmental constraints and a real-time airspace state, and the navigation feasibility of each grid unit is quantitatively analyzed; then, an improved A * algorithm is combined with a grid availability evaluation result to generate a global optimal initial route; finally, a rolling time domain optimization strategy is introduced, and uncertain factors such as sudden obstacles and airspace dynamic limitation are responded in real time through periodic non-flight route re-planning after the unmanned aerial vehicle takes off. Through collaborative fusion of static environment modeling and a dynamic optimization mechanism, the problem of real-time planning of the air route of the unmanned aerial vehicle in the urban low-altitude complex environment is effectively solved, and the environmental adaptability and task reliability of the system are remarkably improved.

The invention relates to the technical field of intelligent ship automatic control and navigation, and discloses a ship automatic driving path planning method and system based on multi-sensor fusion, and the method comprises the following steps: obtaining multi-source sensor data around a ship for environment perception; preprocessing and fusing the sensor data to form unified modeling input; constructing an environment model based on the fused data, and inputting the environment model to a path planning module to generate an optimal path; dynamic obstacle avoidance is executed in combination with the path and the current ship state, and a corrected path is obtained; performing closed-loop control according to feedback information, and updating a state and environment model in real time; and outputting the control instruction to an execution mechanism to drive the ship to run according to the corrected path. According to the method, a closed-loop path correction mechanism based on model predictive control is adopted, the technical effects of dynamically responding to environment changes and optimizing control input in real time are achieved, and the problem of ship yawing caused by path error accumulation and control response delay is solved.

The invention provides a water environment model coupling data processing method and system, and relates to the technical field of hydrology and water quality monitoring. The method comprises the following steps: determining a soil permeability characteristic descending trend and estimating an uncertainty range of a runoff coefficient; obtaining source model data according to the uncertainty range of the runoff coefficient by using a regional pollution production and convergence model; using the one-dimensional river network model to obtain error information according to the source model data, and outputting prediction data; and determining a prediction deviation mode by comparing the prediction data with the real-time monitoring data, and correcting parameters of all related models in the whole model chain. The method aims at solving the technical problem that the permissible discharge amount set based on a three-model coupling system does not conform to the actual absorption capacity of a water body due to continuous nonlinear deviation of river network model prediction and actually measured water quality data in the three-model coupling system, and accurate analysis of the urban pollution load and water environment quality response relation is achieved. And real-time and self-adaptive adjustment of the permissible emission amount is carried out.

The invention relates to the technical field of safety production management, and discloses a safety production hidden danger closed-loop management and control supervision system and a risk assessment early warning method, and the system comprises an environment modeling module which is used for building a digital model of a production unit and calculating a systematic instability coefficient based on historical data; the risk assessment module is used for quantifying the initial risk potential energy in combination with the coefficient and the hidden danger inherent attribute; the risk evolution module is used for dynamically updating risk potential energy according to a time and space coupling rule; the closed-loop verification module is used for judging the actual effectiveness of the intervention measure by comparing the management entropy before and after the intervention after the intervention measure is implemented; and the self-adaptive feedback module is used for automatically updating the systematic instability coefficient in the environment model according to the judgment result. According to the method, risk quantification, dynamic early warning, effect verification and model self-optimization are integrated, a complete closed-loop management process is formed, and the scientificity and foresight of safety production management are remarkably improved.

The invention discloses a remote intelligent control system for mine unmanned equipment, and particularly relates to the technical field of mine unmanned equipment, and the remote intelligent control system comprises an environment sensing modeling module which is used for collecting and processing mine environment data in real time to construct an accurate three-dimensional environment model. Mine environment data are collected and processed in real time through the environment sensing modeling module, an accurate three-dimensional environment model is constructed, basic data support is provided for intelligent decision making, an optimal decision making scheme is made based on environment information and task requirements through the intelligent decision making control module, intelligent operation is achieved, and the intelligent decision making efficiency is improved. A man-machine interaction interface is provided through the remote interaction monitoring module, remote real-time monitoring and operation are achieved, it is ensured that operators can intervene in time, and stable operation and emergency processing capacity of the system under various working conditions are ensured through multiple safety and redundancy design of the safety and reliability guarantee module.

The invention discloses a wind power plant fire intelligent sensing and early warning method based on multi-sensor fusion, and belongs to the technical field of fire sensing and early warning, and the method comprises the steps: obtaining data information collected by a plurality of sensors in real time through the plurality of sensors disposed in a wind power plant, and carrying out the data preprocessing, according to the method, the importance weights of the environmental parameters are dynamically adjusted, the importance degrees of the parameters are reflected in real time according to environmental changes, the accuracy of environmental condition monitoring and modeling is improved, the space-time correlation model is formed in combination with the multi-dimensional time sequence matrix and the space correlation matrix, the uncertainty of the environmental state is comprehensively evaluated, and the accuracy of environmental condition monitoring and modeling is improved. The dynamic environment modeling and prediction are carried out, the time and space factors are fully considered, the fire key features are extracted by adaptively adjusting the weight according to the change of the dynamic environment model and the environment parameters to the fire sensitivity, the information related to the fire can be captured in a more targeted manner, and the accuracy and effectiveness of fire feature extraction are improved.

The present invention relates to the field of ecological monitoring technology, specifically to an agricultural ecological environment monitoring method and system based on digital twins, comprising the following steps: acquiring monitoring data within farmland, constructing a mapping relationship, performing time series arrangement, merging multi-source data, calling a virtual grid to update a three-dimensional model, generating mapping results, analyzing crop growth images, tracking environmental changes, comparing ecological stability intervals, extracting risk trends, calling a three-dimensional model, and generating a risk warning interface layer. In the present invention, by acquiring continuous data of the agricultural ecological environment and constructing a mapping relationship to arrange the information in time series, the response speed and processing accuracy to changes in the agricultural ecological environment are improved, the monitoring and management of the crop growth environment are optimized, and the real-time updated three-dimensional environmental model is used to intuitively display environmental changes. By comparing humidity and temperature changes, spatial fragments are screened, and detailed monitoring and accurate warning of environmental changes are strengthened.

The invention relates to the technical field of environment model construction, in particular to a data and knowledge fusion driven mine integrated modeling method and system. The method comprises the following steps: firstly, preprocessing multi-source heterogeneous data to establish a unified data set, and constructing an object-attribute-environment three-domain associated mine knowledge graph based on physical space information; then, initial geometric models of the earth surface, the ore body, the roadway and the like are constructed respectively, and mapping is established between the models and the atlas; performing forced geometric constraint and logic optimization on the initial model by emphatically utilizing semantic rules in the atlas, and automatically correcting space conflicts; and finally, realizing multi-element space integration under a unified coordinate system. According to the method, deep fusion of geometric reconstruction and geological semantics is realized, the problem of model logic deficiency is effectively solved, a high-precision three-dimensional model with dynamic evolution ability and logic self-consistency is constructed, and reliable support is provided for mine intelligent management.

The application provides an indoor space environment control method based on an optimization algorithm and related equipment, and the method comprises the following steps: constructing an indoor comprehensive environment model by combining historical equipment control parameters and historical space environment data; constructing a comprehensive target function; creating a state space of an ant colony optimization algorithm and generating constraint conditions of the ant colony optimization algorithm; taking the comprehensive target function as an algorithm optimization target, generating an initial equipment control strategy of an environment control device based on the indoor comprehensive environment model and using the ant colony optimization algorithm; taking an optimal parameter interval as a strategy initial solution of a model predictive control strategy, generating a strategy prediction result by the model predictive control strategy based on the indoor comprehensive environment model; and updating algorithm parameters of the ant colony optimization algorithm according to the strategy prediction result and generating an optimal equipment control strategy of the environment control device. The application has the effect of meeting the environmental experience comfort of users on the basis of the lowest energy consumption.

The application provides a water environment model coupling data processing method and system, and relates to the technical field of hydrology and water quality monitoring. The method comprises the following steps: determining the downward trend of soil permeability characteristics and estimating the uncertainty range of runoff coefficient; using a regional source-sink pollution model, obtaining source model data according to the uncertainty range of runoff coefficient; using a one-dimensional river network model, obtaining error information and outputting prediction data according to the source model data; and determining the prediction deviation mode by comparing the prediction data and real-time monitoring data and correcting the parameters of all related models in the whole model chain. The method aims to solve the technical problem that the prediction of the river network model deviates from the actual measurement data in a nonlinear manner in a three-model coupling system, which ultimately leads to the inconsistency between the permitted discharge amount set based on the system and the actual water body absorption capacity, and to realize the accurate analysis of the response relationship between the urban pollution load and the water environment quality and the real-time and self-adaptive adjustment of the permitted discharge amount.

Environment sensing system, comprising at least one sensor (10) for providing location data (12) about objects (18), wherein the location data (12) includes velocity data, and comprising a computational means (14) for converting the location data (12) into an environment model (16) that represents kinematic object data of the objects (18) and assigns an object (18) to a respective object class (K), wherein the computational means (14) is conditioned to preferentially output environment models (16) in which, for an object (18) corresponding to at least one predefined object class (K), at least one predefined physical relationship of the object data to location data (12) assigned to the object (18) is better satisfied for those location data (12) assigned to the object (18) that correspond to a predefined association criterion than for the totality of the location data (12) assigned to the object (18).

The invention provides a robot sampling planning method and system based on environment extreme value position estimation, and relates to the technical field of robot control, and the method comprises the steps: building a model for a to-be-monitored environment through a Gaussian Markov random field, and mapping sampling points in the environment into random variables in an environment model; calculating mutual information values of each non-sampling point and other non-sampling points based on the environment model; according to the observation value of the sampled point, obtaining an environment extreme value position estimation result through Gaussian process fitting and a Kriging estimation method; according to the mutual information value of the non-sampling point and an extreme value position estimation result, a next active sampling point is obtained through a probability decision-making method fusing mutual information and a spatial distance; based on the Gaussian Markov random field and the sampling information gain, the accuracy and rapidity of robot sampling planning are improved.

The invention discloses a raft culture-based marine product typhoon disaster risk assessment method and a storage medium, and the method comprises the steps: assimilating multi-source actual measurement data to an environment model through a data assimilation algorithm, and simulating an environment field of a target culture sea area according to the assimilated and corrected environment model; applying the environmental load corresponding to the environmental field to a structure model of the raft culture structure for dynamic response analysis, and calculating a structural strength index according to an analysis result; calculating a vitality index of the marine product according to a preset environment factor; and according to the structural strength index, the vitality index of the marine product and the disaster-inducing factor, determining the risk level of the valve type culture structure through a random forest model. According to the invention, comprehensive and objective risk assessment can be realized, and the assessment accuracy can be improved.

The invention relates to a coastal area composite network stability and carbon neutralization capability cooperative improvement method, and belongs to the field of ecological informatics and environment modeling. According to the method, firstly, metabolism subjects are divided based on multi-stage land utilization data, artificial carbon emission and natural carbon sink of each metabolism subject are evaluated, a carbon metabolism path is determined according to the area difference of land types before and after land utilization change and the carbon income and expenditure level difference of unit area, a carbon metabolism network is constructed, and key regulation and control nodes are identified; and by simulating different development scenes, dynamically evaluating the change rule of the stability and the carbon neutralization capability of the composite network, and finally forming a space planning and policy intervention scheme for synergistically improving the stability and the carbon neutralization capability of the composite network. According to the method, the crossing from static evaluation to dynamic optimization regulation is realized, the perspectiveness and scientificity of management decision are effectively improved, and the method is of great significance to guarantee the sustainable development of the coastal area.

This invention relates to the field of robotics technology and discloses a large-model-driven method and system for navigation and autonomous path planning of a composite robot. The method includes: collecting environmental data through multimodal sensors; inputting the data into a visual-language large model (VLM) to construct a holistic environmental model containing geometric, semantic, and dynamic object information; parsing high-level natural language navigation instructions using the VLM and generating navigation target points based on the holistic environmental model; and finally, coordinating the robot's body movement trajectory and end effector action sequence based on the model and target points, and driving their execution. This invention, by introducing a large model for holistic environmental modeling and task parsing, solves the problems of weak environmental understanding and unnatural human-computer interaction in traditional robot navigation technologies. It achieves deep perception of complex dynamic environments and autonomous planning of high-level instructions, significantly improving the intelligence level and operational efficiency of composite robots in real-world scenarios.

In some implementations, a method includes obtaining environmental data corresponding to an environment. The method includes determining that the environmental data corresponding to the environment includes environmental data that corresponds to a first portion of an object that represents a sub-portion of the object and not an entirety of the object. The method includes generating a plurality of candidate point clouds for a second portion of the object based on the environmental data corresponding to the first portion of the object. The plurality of candidate point clouds are associated with corresponding confidence scores. The method includes synthesizing a model of the environment that includes a point cloud representing the first portion of the object, at least a subset of the plurality of candidate point clouds for the second portion of the object and the corresponding confidence scores associated with the subset of the plurality of candidate point clouds.

This invention provides a fully automated pretreatment method, system, and storage medium for soil samples. The method involves acquiring a three-dimensional environmental model of the soil sample pretreatment process, determining a task sequence consisting of a starting point, task points, and target points based on the process flow, and planning a segmented, collision-free path using adjacent point pairs as start and end points. A Gaussian mixture probability model is constructed based on the endpoints, generating sampling points in the probability space to guide random tree growth. The node closest to the sampling point is selected as an expansion node, and multiple alternative directions are generated around the expansion direction. The direction with the lowest energy consumption is selected using a joint energy consumption model. New nodes are generated with a preset step size, and these nodes are added only when the weighted sum of the angular acceleration and angular jerk of the path segment is below a disturbance threshold. When a new node enters the endpoint's attraction domain, an initial path is generated and locally optimized to obtain a segmented path. All segmented paths are then concatenated into a global path, and a robotic arm is controlled to perform the soil pretreatment operation.

The invention provides a multi-pipeline layout method based on a niche coevolution algorithm, and relates to the technical field of pipeline layout methods. Comprising the following steps: A1, establishing an environment model of a pipeline layout space; a2, solving a multi-pipeline layout problem by applying an ecological niche coevolution algorithm; and A3, when the ecological niche coevolution algorithm meets a termination condition, outputting an optimal global solution as a final multi-pipeline layout scheme. On the basis, a niche layout mode, an elite partner selection strategy, a multi-dimensional information sharing mechanism and a self-adaptive evolution mechanism are introduced, the defects that in an existing co-evolution algorithm, information interaction is insufficient, and prematurity is prone to occurring are overcome, the global search ability and the quality of solution of the complex combination optimization problem are remarkably improved, and the method is suitable for large-scale popularization and application. Therefore, the problems that when an existing co-evolution algorithm is applied to multi-pipeline layout, a co-author selection mechanism is simple, shared information dimensions are single and lagged, and combined explosion is prone to being generated are solved.

The invention discloses a time-varying wind field environment unmanned aerial vehicle full-coverage flight path planning method and system based on reinforcement learning, and mainly solves the problem that an existing unmanned aerial vehicle coverage flight path planning method cannot plan a full-coverage flight path in a dynamic wind field environment. According to the implementation scheme, the method comprises the steps that two-dimensional environment modeling is conducted on an unmanned aerial vehicle task area through a grid method, and the task area is discretely segmented into grids of the same size; according to the two-dimensional environment model, constructing a Markov decision model comprising an action space, a state transfer function and a reward function through a reinforcement learning algorithm; according to the time-varying characteristics of the wind field, designing an initialized time-sharing Q table capable of planning the full-coverage flight path of the time-varying wind field environment, and training the initialized time-sharing Q table; according to the trained time-sharing Q table and the Markov decision model, the short-endurance full-coverage flight path of the unmanned aerial vehicle in the time-varying wind field environment is obtained.For the dynamic time-varying wind field environment, an effective coverage path completely covering a task area can be planned, complete reconnaissance of the unmanned aerial vehicle on the task area is guaranteed, and the method can be used for unmanned aerial vehicle reconnaissance tasks.

The invention provides a data processing method and system for floating installation path planning, and the method comprises the steps: obtaining multi-source heterogeneous data which comprises three-dimensional submarine topography data, installation equipment three-dimensional data, hydrological parameter data and weather forecast data; performing gridding processing on the weather forecast data, the three-dimensional submarine topography data and the hydrological parameter data, and constructing a marine environment digital twinborn model based on a gridding processing result; performing dynamic prediction on the hydrological parameter data and the weather forecast data to obtain space-time evolution of the flow velocity field; performing multi-objective optimization based on the marine environment digital twinborn model and the space-time evolution of the flow velocity field to obtain a path optimization strategy; and constructing a digital twinborn simulation environment model based on the marine environment digital twinborn model and the path optimization strategy, and performing path verification updating on the digital twinborn simulation environment model based on the three-dimensional data of the installation equipment to obtain target path parameters. According to the invention, the quality and effect of path planning are significantly improved.

The invention relates to the field of photoelectric detection and computer vision, and discloses an intelligent identification method and system for a thermal imaging telescope, and the method comprises the following steps: 1, constructing a rigid thermal reference and a pneumatic noise sequence, and quantifying an environment situation index according to the rigid thermal reference and the pneumatic noise sequence; 2, locking a region of interest, and extracting an average radiation temperature sequence and an edge gray level fluctuation sequence; 3, calculating a thermal response correlation coefficient, and generating a thermal confidence coefficient; 4, performing frequency domain spectral subtraction and periodic verification on the edge fluctuation by using the pneumatic noise sequence, and generating a micro-motion confidence coefficient representing the mechanical attribute; and step 5, dynamically distributing weights according to the environment situation indexes, and fusing multi-dimensional features to calculate the biological existence probability. According to the method, the double-reference environment model is constructed, and passive temperature rise and pneumatic interference are distinguished by using thermal response and motion frequency difference, so that the problem of low recognition accuracy of traditional temperature or shape feature recognition is solved.

The invention discloses a hydraulic engineering dispatching simulation system based on digital twinning, and relates to the field of hydraulic engineering dispatching. A model decomposition module; a scheme forming module; the model building module is used for forming a turbulence prediction model; a drainage modeling module; the drainage prediction module analyzes to obtain a water flow drainage result at the drainage port; the terrain obstruction module analyzes to obtain an obstruction terrain of the environment model at the water outlet, and analyzes to obtain a flowing water obstruction result; and the simulation prediction module obtains a simulation prediction result of the scheduling scheme. The model construction module, the drainage modeling module, the drainage prediction module, the terrain obstruction module and the simulation prediction module are arranged, then all factors are combined, the water flow speed after the gate is opened and closed is predicted, and therefore the accurate flow is obtained.

The invention belongs to the technical field of surveying and mapping task path planning, and particularly discloses a surveying and mapping task path planning method and system based on big data, and the method comprises the steps: firstly obtaining the current and historical surveying and mapping data of a target region, and recognizing an abnormal change pattern spot through calculating the change confidence; judging whether the pattern spots have geographic information changes or not based on a preset rule, generating variable surveying and mapping priorities, performing surveying and mapping zoning by utilizing a spatial clustering algorithm, generating a preliminary surveying and mapping task path in combination with a sequence optimization algorithm, and importing the path into a geographic environment model to perform safety conflict detection and multi-target efficiency analysis; and finally, correcting the path according to a detection analysis result and outputting a final surveying and mapping task path. Through quick discovery and efficient response to geographic information change, the generated surveying and mapping task path has high safety and high efficiency, and the input-output ratio and the operation intelligence level of surveying and mapping resources are remarkably improved.

A computer-implemented method facilitates vegetation management and environmental simulation for a geographic region. The method includes obtaining input data representing tree areas and tree characteristics for one or more species. Tree nodes are created based on identified tree centroids from the tree areas. Spatial data structures are generated to represent vegetation and terrain features based on the tree nodes and terrain data. Selection criteria are applied to partition the tree nodes into management categories. Environmental models representing alternative vegetation configurations, including a baseline and a modified configuration corresponding to tree removal treatments, are generated. Vegetation data in the environmental models is modified to reflect the alternative configurations. Environmental simulations are executed to compute outputs representing ecological responses. Statistical relations are generated between vegetation management actions and the simulated environmental responses, and output data is provided representing predicted outcomes, management planning information, or changes in environmental conditions.

This invention relates to the field of tracking turntable technology and provides a predictive control method and system for a turntable. The method includes: acquiring environmental data of a target area through multiple sensors; performing spatiotemporal feature fusion processing on the environmental data and constructing a dynamic environment model by combining it with three-dimensional mesh modeling, wherein the three-dimensional mesh modeling divides the target area into three-dimensional mesh units; based on the dynamic environment model, using a general obstacle recognition network to identify target features and combining it with an intelligent decision-making algorithm to predict the target's motion trend, generating control commands; the general obstacle recognition network identifies target features and outputs target attributes including motion stability, target priority, and size level; adjusting the motion parameters of the tracking turntable based on the control commands to achieve real-time tracking of the target object; improving the adaptability of the tracking turntable to complex environments, the accuracy of target recognition, and the real-time performance of tracking control, and enhancing the reliability and adaptability of the system in dynamic and complex scenarios.

The invention belongs to the technical field of ecological risk evaluation, and relates to a new pollutant environmental behavior prediction method, in particular to a pollutant concentration prediction method based on an environmental multi-medium spatial differentiation model, which is used for predicting spatial distribution of new pollutants in different media (such as air, water, soil and the like). The method comprises the steps of building an environment behavior parameter prediction model, building an environment parameter database, building an emission rate prediction model and building an environment multi-medium space differentiation model. According to the method, molecular fingerprint coding is combined with a multi-task learning strategy, multiple key environment behavior parameters are predicted at the same time, the parameter prediction efficiency and accuracy are greatly improved, and key input is provided for a subsequent multi-medium environment model. According to the method, deep combination of the machine learning model and the fugacity model is realized, parameter prediction and pollutant migration fate simulation are integrated, and a more comprehensive technical framework is provided for environment exposure prediction.

This invention relates to the field of environmental model construction technology, and in particular to a data and knowledge fusion-driven integrated mine modeling method and system. The method first preprocesses multi-source heterogeneous data to establish a unified dataset, and then constructs a mine knowledge graph based on physical spatial information, linking objects, attributes, and the environment. Next, initial geometric models of the surface, ore body, and tunnels are constructed, and a mapping is established between the models and the knowledge graph. The semantic rules in the knowledge graph are used to enforce geometric constraints and logical optimization on the initial models, automatically correcting spatial conflicts. Finally, multi-element spatial integration is achieved under a unified coordinate system. This method achieves deep integration of geometric reconstruction and geological semantics, effectively solving the problem of missing model logic, and constructing a high-precision three-dimensional model with dynamic evolution capabilities and logical self-consistency, providing reliable support for intelligent mine management.

The invention provides a high-precision modeling method and device based on 3D scanning and a medium. The method comprises the following steps: acquiring multi-scale scene data of a target area through three-dimensional scanning; registering the scene data, and establishing a basic geographical environment model of the target area; according to the target explosion parameter of the to-be-trained task, performing explosion simulation on the structure models in the basic geographical environment model to obtain a target damage model of each structure model under the target explosion parameter; selecting a real sample similar to the target damage model from a preset real sample library; obtaining an explosion geographical environment model according to the real sample, the target destruction model and the basic geographical environment model; and based on the explosion geographical environment model, the target explosion parameters and the meteorological parameters of the to-be-trained task, performing explosion pollution diffusion simulation to obtain a scene model of the to-be-trained task. According to the invention, the geographic structure can be accurately restored, the diffusion condition of nuclear explosion can be simulated, and a real and reliable high-precision scene model can be formed.

This invention relates to the field of data processing, proposing a method and system for recommending garden vegetation based on ecological and environmental data. First, environmental monitoring signals are acquired using sensor networks. Based on geostatistical variation patterns, spatial reconstruction and spatiotemporal interpolation of the signals are performed to construct a digital environmental model. Second, a global ecological neighborhood benchmark is constructed to standardize the signals, and a resource pressure index is calculated and introduced into a dynamic competition model as a nonlinear gain environmental factor weight. Third, multiple ecological strategy control logics are pre-set, and a Gaussian probability mapping between the resource pressure index and the applicability of each strategy is established. The strategy outputs are weighted and integrated using posterior probability weights to obtain configuration instructions including initial maintenance measures. Finally, feedback signals after the implementation of the instructions are monitored. When deviations exceed limits, physical adjustment mechanisms such as water and fertilizer regulation or microhabitat improvement are activated, and the model parameters are adaptively corrected. This invention achieves precise decision-making and closed-loop dynamic control of garden vegetation configuration.