33 results about "Environmental modelling" patented technology

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 provides a mobile robot path planning method based on an improved ant colony algorithm, and relates to the technical field of robot path planning, and the method is technically characterized in that the method comprises the following steps: carrying out environment modeling by adopting a grid method, and constructing a multi-target performance evaluation function; an improved ant colony algorithm is adopted to carry out optimal path search, path planning is carried out on a grid map from a starting node to a target node, and the improvement of the ant colony algorithm comprises the steps of distributing pheromone concentration by adopting a non-uniform initialization method; improving a state transition rule; differentiating a pheromone updating strategy; and smoothing the path. An initial pheromone distribution optimization strategy is introduced, heuristic information based on a triangular inequality, a steering angle suppression function and a state transition rule of artificial potential field force are combined, a differential pheromone updating strategy based on a multi-target performance evaluation index is adopted, and finally a planned path is smoothed. Therefore, the convergence speed, the path quality and the environment adaptability of the algorithm are remarkably improved.

The invention relates to automatic driving, in particular to a dynamic environment modeling and active adaptation system for an automatic driving vehicle, which comprises a multi-band cognitive radar module and a millimeter wave radar integrated with multiple bands, adopts an intelligent switching and cooperative working mechanism, and automatically optimizes the duty cycle proportion of the bands according to the environment condition. Radar waves of different frequency bands are transmitted and received, and multi-dimensional information of a target is obtained; the data processing and dynamic environment modeling module adopts a multi-sensor data fusion algorithm to fuse target multi-dimensional information acquired by radars of different frequency bands, performs dynamic environment modeling on the periphery of the vehicle, and updates a dynamic environment model in real time; the decision-making and active adaptation control module is used for making a reasonable driving decision, planning a path and carrying out active adaptation control by adopting a deep learning and artificial intelligence algorithm; according to the technical scheme provided by the invention, the defects that an accurate and real-time dynamic environment model is difficult to construct and the vehicle driving state cannot be timely and reasonably adjusted can be effectively overcome.

The invention provides a path planning method, system and equipment for underwater rock drilling operation of a high-frequency breaking hammer, and relates to the technical field of underwater rock drilling. A multi-source data modeling technology and a position indicator area positioning technology are fused, microscopic details of a rock drilling target are complemented through optical data, equipment motion constraints are calibrated through position indicator data, and three-dimensional environment modeling of a macroscopic operation area in combination with the microscopic rock drilling target and the equipment motion constraints is achieved; a multi-objective optimization model is constructed, and an objective function for aggregating the path length, the path fluctuation, the energy consumption and the breaking hammer slip rate is a composite cost function; solving a global optimal operation path corresponding to the composite cost function by adopting IPSO; obtaining the global optimal path through speed updating mechanism optimization, adaptive inertia weight adjustment, asynchronous change learning factor setting and natural selection population iteration; the data volume and the calculation complexity of environment modeling are reduced, and the accuracy and the practicability of environment perception and modeling are improved.

The invention discloses an urban road sky-ground integrated automatic driving collaborative awareness and planning system in severe weather, and the system comprises a multi-mode perception fusion and environment modeling module which generates a dynamic-static fusion BEV map through the fusion and complementation of a vehicle-end multi-mode sensor and low-orbit satellite perception data; the dynamic global path planning module is used for constructing a dynamic cost BEV map and generating a global optimal path by using an improved A * algorithm; the dynamic spatio-temporal reasoning planning module dynamically updates the dynamic cost BEV map based on the surrounding vehicle prediction trajectory and the occupied area, and predicts the local optimal path of the own vehicle; and the space-ground collaborative closed-loop optimization module dynamically updates global and local path planning results through real-time data interaction between the vehicle end and the satellite end. According to the method, the sensing precision, the path planning efficiency and the dynamic adaptive capacity of the urban road automatic driving system under the complex dynamic environment and severe weather conditions are remarkably improved.

The invention relates to the technical field of cold chain transportation environment control, and discloses a temperature and humidity control system for cold chain transportation, and the system comprises an environment modeling module which is used for constructing a heat conduction model of a cold chain space based on a random field theory and a finite element method, and outputting a stiffness matrix containing random parameters; the self-adaptive sensing network module is used for receiving the characteristic information of the stiffness matrix, dynamically optimizing sensor distribution positions, collecting multi-node temperature and humidity data and adding space-time labels; and the distributed data processing module is used for receiving the sensing data. According to the invention, through random field theoretical modeling and parameter-state joint estimation, the temperature field prediction precision is significantly improved and the system environment adaptability is enhanced; a distributed sensing network architecture and a heterogeneous calculation acceleration technology are combined, so that the cooperative control with optimal energy efficiency is realized while millisecond-level real-time response is ensured, and the performance bottleneck of a traditional cold chain temperature control system is comprehensively broken through.

The invention relates to the technical field of power dispatching, in particular to an air conditioner cold storage power intelligent dispatching method for virtual power grid transaction. According to the method, the periodically acquired data information is preprocessed, so that time synchronization of the data information can be realized, and the acquired data is more effective; and environment modeling is completed based on the preprocessed data information, so that the change of the state is represented more accurately by using the environment modeling based on the periodically updated data information, and the scheduling proportion output after the environment modeling is input to the deep reinforcement learning agent is more accurate. And the dispatching state of the electric power is determined based on the cold storage characterization value, and relevant parameters are adjusted based on the dispatching state, so that the dispatching of the cold storage and transaction electric quantity is more effective. According to the invention, the problem of low power dispatching efficiency caused by the fact that the electric quantity for cold storage and transaction cannot be reasonably dispatched in the prior art is solved.

The invention relates to the technical field of environmental protection engineering, and discloses a landfill pit bottom treatment process which comprises the following steps: preparing for three-dimensional environment modeling, scanning pit bottom sludge by utilizing an intelligent sensing execution subsystem, and generating a three-dimensional digital terrain model; intelligent self-adaptive desilting is conducted, the real-time sludge concentration in the pumping process is collected in real time and compared with the preset target concentration to obtain a control error, and operation parameters of the intelligent sensing execution subsystem are adjusted according to the control error; after residual materials at the pit bottom are cleaned, multi-point sampling is conducted on the pit bottom, and whether the warehouse cleaning work is qualified or not is judged according to the content of organic matter in the sample. According to the invention, through three-dimensional modeling and closed-loop control, precision and efficiency optimization of desilting operation are realized; meanwhile, a quantitative acceptance standard based on the organic matter content is introduced, an objective basis is provided for warehouse cleaning quality, and the problems that a traditional process is low in efficiency and the acceptance standard is subjective are solved.

The invention relates to the technical field of unmanned vehicle path planning and autonomous navigation, in particular to a special unmanned vehicle autonomous obstacle avoidance method, which comprises the steps of environment modeling, vehicle positioning, path planning, path execution and control and the like, and also discloses a special unmanned vehicle system used for realizing the special unmanned vehicle autonomous obstacle avoidance method. Comprising a vehicle-mounted main controller, a satellite navigation receiver and a driving and executing mechanism, according to the autonomous obstacle avoidance method for the special unmanned vehicle, through the steps of environment modeling, vehicle positioning, path planning, path execution and control and the like, the core problems of too high system cost, poor environment adaptability, large map data volume, poor planning algorithm performance, non-ideal path quality and the like are effectively solved; through integration of sparse environment modeling, satellite accurate positioning and hybrid intelligent planning, a set of complete autonomous obstacle avoidance solution which is completely independent of expensive real-time sensors and has low cost and high reliability is constructed.

The application discloses a reservoir temporary inundated area risk assessment method based on scheduling simulation and relates to the technical field of water conservancy project assessment, and comprises the following steps: step one, data collection and environment modeling; step two, reservoir hydrological dynamics and risk assessment calculation; step three, loss calculation; and step four, temporary inundated area risk assessment. The reservoir temporary inundated area risk assessment method based on scheduling simulation guarantees security and scientificity, optimizes the safety management of the inundated area through risk grading and control measures, and enhances the emergency response capability of the reservoir under high water level scheduling.

This invention belongs to the technical field of agricultural environmental control and UAV applications, and relates to a UAV-based microclimate precision control system for forest understory cultivation areas. The system includes: an environmental modeling and initialization module, a prediction and decision-making module, a command execution and observation module, a data verification and extraction module, an error calculation and correction source term generation module, a correction source term propagation and field update module, and a closed-loop iterative scheduling module. By constructing a three-dimensional voxel grid and generating an initial microclimate prediction field, identifying target voxels based on prediction trends and generating predictive control frames, the UAV executes intervention actions and collects observation data. After verification and error calculation, correction source terms are generated. The microclimate prediction field is updated based on predicted airflow propagation, achieving closed-loop iterative control. This invention solves the problems of lagging control and insufficient intervention targeting caused by the lack of dynamic prediction of three-dimensional microclimate fields and digital twin linkage capabilities in existing fixed sensor networks and traditional UAV operation methods.

The application discloses a physical guidance-based spatio-temporal hypergraph condition denoising diffusion probability model construction method, and relates to the field of ecological environment modeling and artificial intelligence prediction. The application innovatively combines the physical heat diffusion process and the spatio-temporal modeling of multi-modal environmental factors by introducing a temperature driving module based on heat conduction and a spatio-temporal perception fusion module. In addition, a lightweight convolution block attention module and a hierarchical hypergraph attention mechanism are embedded in the diffusion denoising network to realize high-order correlation modeling and dynamic fusion of multi-scale spatial features. The method can effectively capture the spatio-temporal evolution characteristics and ecological dependence of species distribution while maintaining reasonable computational complexity, thereby significantly improving the accuracy of distribution prediction. The experimental results on different types of data sets prove the superiority and robustness of the proposed PSTH-CDPM.

The invention discloses a cloud and rain environment modeling prediction method and device based on supervised machine learning. The method comprises the steps that a meteorological environment historical data set is acquired; the meteorological environment historical data set comprises a historical data subset of each type of meteorological parameters; utilizing the meteorological environment historical data set to construct a meteorological training data set; based on the meteorological training data set, processing a preset cloud and rain field model to obtain a cloud and rain field fusion prediction model; and processing the acquired meteorological data set by using the cloud and rain field fusion prediction model to obtain a cloud and rain prediction result information set.

This invention provides a multi-machine collaborative scheduling system for a human-machine controlled container, belonging to the field of multi-machine collaborative scheduling and control technology. The system includes: a task semantic understanding and planning module; an environmental feature intelligent classification and surface modeling module connected to the task semantic understanding and planning module; a task semantic enhancement and risk assessment module connected to the environmental feature intelligent classification and surface modeling module; and a multi-machine collaborative decision-making module connected to the task semantic enhancement and risk assessment module. This invention achieves this by closely cooperating through task semantic understanding, environmental modeling, risk assessment, and decision-making functions to form an organic whole. Under the unified scheduling of the system, different types of machines can efficiently and collaboratively complete complex tasks, breaking the limitations of isolated module operation in traditional systems and meeting the stringent requirements for multi-machine collaborative operations in multiple fields and scenarios.

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.

The invention provides a multivariable coupling library temperature and humidity prediction model construction method and system, and the method comprises the steps: obtaining delay response data of environment parameter changes in a time window through constructing a hysteresis effect feature analysis model according to the quantitative indexes and change rules of temperature and humidity jump, and obtaining the delay response data of the environment parameter changes in the time window; the specific influence range and duration distribution of the hysteresis effect are judged; according to the nonlinear change rule and the response parameter set, abnormal point location data with the environmental parameters exceeding the safety range are obtained through a preset threshold comparison method, and key time nodes needing to be regulated and controlled preferentially are determined; and according to the adjusted temperature and humidity data and the optimization result of the dynamic boundary condition, the environment modeling difficulty is reduced by adopting a data fusion technology, a more accurate environment parameter response mechanism is obtained, and a final control strategy output result is determined.

The subject matter provides a path planning method, apparatus and electronic device. Wherein, the method comprises: performing environment modeling according to static road network information and dynamic road condition information of a road network so as to obtain an environment model; determining a plurality of candidate paths according to a starting point and an ending point; extracting from the environment model an environmental feature corresponding to each candidate path by a feature extraction network of a path planning model; inputting the environmental feature to a value estimation network of the path planning model so as to obtain an estimated value for each candidate path output by the value estimation network; determining an optimal path among the candidate paths according to the estimated value.

This invention discloses a robot movement path planning method based on ant colony optimization algorithm. It includes the following steps: environmental modeling of the robot's movement space using a grid method to obtain a grid map, with grid coordinates representing the robot's starting and ending positions; generating an initial path on the grid map using a jump-point search algorithm, and calculating the non-uniform initial pheromone concentration distribution along the initial path; searching for the optimal path on the grid map using the ant colony optimization algorithm; eliminating redundant nodes on the optimal path and performing smoothing processing to obtain the robot's movement path. This invention improves convergence speed and search efficiency by using a non-uniform distribution of the initial pheromone concentration in the grid map, and shortens the planned path length by eliminating redundant nodes on the optimal path and performing smoothing processing.

The present application relates to the technical field of power dispatch, and particularly relates to an air conditioner cold storage power intelligent dispatching method for virtual power grid transaction. The present application can make the data information realize time synchronization by preprocessing the periodically obtained data information, so that the obtained data is more effective; and completes environment modeling based on the preprocessed data information, so that the state change is more accurately represented based on the periodically updated data information using the environment modeling, so that the dispatching proportion output after the environment modeling is input into the deep reinforcement learning intelligent agent is more accurate; determines the power dispatching state based on the cold storage representation value, and adjusts the related parameters based on the dispatching state, so that the dispatching of the power for cold storage and transaction is more effective. The present application solves the problem that the power for cold storage and transaction cannot be reasonably dispatched in the prior art, so that the power dispatching efficiency is low.

The application discloses a multi-spatial resolution remote sensing simulation and error influence stripping method, and relates to the technical fields of remote sensing image processing, quantitative remote sensing and environmental modeling. Firstly, a multi-scale remote sensing image is simulated by combining a modulation transfer function (MTF) and mean value interpolation; secondly, a multiple regression model is constructed to explain model errors by image quality indexes, so that the interference of image quality on analysis results is stripped, and normalized residual errors caused by changes in spatial resolution are obtained; finally, a sliding window-based RSI and "Median+IQR" scoring method is proposed to identify stable resolution intervals and perform next-step analysis, thereby guiding the selection of actual modeling scales. The method has high universality and scale adaptability, and is suitable for pre-scale analysis and optimized modeling in various remote sensing quantitative analysis tasks such as land cover, vegetation parameters, urban heat island, water body monitoring and the like.

The application discloses a cloud rain environment modeling and predicting method and device based on supervised machine learning, and the method comprises the following steps: collecting a meteorological environment historical data set; the meteorological environment historical data set comprises a historical data subset of each type of meteorological parameter; a meteorological training data set is constructed by using the meteorological environment historical data set; a preset cloud rain field model is processed based on the meteorological training data set, and a cloud rain field fusion prediction model is obtained; and the collected meteorological data set is processed by using the cloud rain field fusion prediction model, and a cloud rain prediction result information set is obtained.

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.

The invention provides an intelligent ship navigation method, device and equipment and a storage medium. The method is applied to an intelligent ship navigation system comprising a dynamic environment modeling module and a deep reinforcement learning model. The method comprises the following steps: the dynamic environment modeling module obtains wind speed, wave height and ocean current speed data, and calculates a threat index in combination with a ship structure and a loading state; the deep reinforcement learning model converts the threat index into an environmental risk entropy value, the environmental risk entropy value, the relative azimuth angle, the DCPA and the TCPA form a state space, a rule compliance indicator is set in a reward function, and an action conforming to the COLREGs rule is stimulated; and finally, the model outputs a control instruction sequence of the course correction delta psi and the speed correction delta V according to the information. According to the method, the adaptability, the real-time performance and the precision of navigation under complex sea conditions are remarkably improved.

The invention discloses a multi-spatial resolution remote sensing simulation and error influence stripping method, and relates to the technical fields of remote sensing image processing, quantitative remote sensing, environment modeling and the like. The method comprises the following steps: firstly, simulating a multi-scale remote sensing image in a mode of combining a modulation transfer function (MTF) and mean interpolation; secondly, constructing a multiple regression model, explaining a model error by using an image quality index, thereby stripping the interference of the image quality on an analysis result, and obtaining a normalized residual error only caused by the change of a spatial resolution ontology; and finally, an RSI and Median + IQR scoring method based on a sliding window is provided, a resolution stable interval is identified, and next analysis of an experiment is performed so as to guide selection of an actual modeling scale. The method has high universality and scale adaptability, and is suitable for early-stage scale analysis and optimization modeling in various remote sensing quantitative analysis tasks such as land coverage, vegetation parameters, urban heat islands and water body monitoring.

The application provides a fish growth rate prediction method and system based on environment modeling, comprising: acquiring environment data; based on the environment data, constructing an adaptive time mode network to obtain a water temperature prediction reasoning model; inputting fish experience data to be predicted into the water temperature prediction reasoning model to obtain fish growth rate prediction results to be predicted. The application constructs a water temperature prediction reasoning model by collecting comprehensive environment data, combines different fish growth experience values, obtains prediction results of different fish growth rates, has the advantages of wide adaptation range, comprehensive environmental factors, and no need to repeatedly train the prediction model.

The application discloses an environment modeling method and device based on a hyperspectral laser radar, and the method comprises the following steps: acquiring point cloud information of a to-be-detected scene and original spectrum of a detection target in the to-be-detected scene through the hyperspectral laser radar; positioning and mapping are performed by using the point cloud information of the to-be-detected scene, and an initial environment three-dimensional model is constructed; surface attribute parameters of the detection target are determined; the original spectrum of the detection target is inversely processed according to the surface attribute parameters of the detection target and an output response model of the hyperspectral laser radar, so that reflectivity spectrum of the detection target is obtained; and the initial environment three-dimensional model is mapped again by using the reflectivity spectrum of the detection target, so that a target environment multi-dimensional model is obtained. According to the scheme, the original spectrum output by the hyperspectral laser radar is finely inversely processed, so that the real spectrum of the detection target is obtained, the accuracy of the spectrum is effectively improved, and the spatial geometric structure information and the spectrum information are fully matched, so that accurate environment modeling is realized.

The invention discloses an AI-supported environment-friendly robot. According to the technical scheme, the AI-supported environment-friendly robot is characterized by comprising a data sensing module, an AI intelligent processing module, a task decision module, an execution control module, a remote interaction and cooperation module and a data storage and iteration module; the data sensing module is used for collecting multi-source environment data and robot operation state data; the AI intelligent processing module carries out fusion analysis, intelligent identification and environment modeling on the collected data; the task decision module generates a dynamic operation strategy based on an AI analysis result; the execution control module executes the operation strategy and corrects the action deviation; the remote interaction and cooperation module realizes man-machine interaction and multi-machine cooperation; the data storage and iteration module stores data and updates an algorithm model; according to the invention, the recognition accuracy is improved through AI-driven multi-source fusion.

This invention relates to the field of environmental modeling and employs a multi-layered, multi-scale marine environmental field modeling method for marine robots. The aim is to address the low accuracy of environmental field models for marine robots operating in complex and dynamic marine environments. The invention generates a grid map of the target sea area and obtains predicted environmental field data, including sea breezes, waves, and currents, from an international weather forecasting model, storing this data in the grid area. Subsequently, an environmental perception module collects current and historical environmental field observation data. Based on the characteristics of marine robots, the spatiotemporal model of the environmental field is divided into three categories: large-scale, medium-scale, and small-scale models. A large-scale spatiotemporal model is constructed using the predicted environmental field data; a medium-scale model is further constructed based on the large-scale model; and a small-scale model is generated by combining observation data. Finally, the environmental field data of the medium-scale model is updated using the observation data from the small-scale model.

The invention relates to the technical field of three-dimensional positions, in particular to an unmanned aerial vehicle positioning and dynamic obstacle avoidance system and method based on multi-sensor fusion, and the system comprises a multi-source fusion module, an error optimization module, an environment modeling module, a time sequence analysis module and a dynamic planning module. According to the invention, through synchronous fusion of multi-source data and integration of visual images, laser point cloud and IMU data, feature information can be effectively extracted and pose positioning can be optimized in a complex environment, the problem of insufficient adaptability of a traditional system in a dynamic environment is solved, and motion errors and positioning errors of visual and point cloud data are jointly analyzed through error optimization, so that the accuracy of the system is improved. And the positioning precision and the system robustness are improved. And environment modeling and time sequence analysis are further combined, so that the motion state of the obstacle can be detected in real time, the flight path priority can be adjusted, the obstacle can be dynamically avoided, the response speed and accuracy of navigation and obstacle avoidance are improved, and stable operation and safety of the aircraft in a changing environment are ensured.

In various examples, an environment visualization pipeline may determine whether to generate or otherwise enable a visualization using an environmental modeling pipeline that models an environment as a 3D bowl or using an environmental modeling pipeline that models the environment using some other 3D representation, such as a detected 3D surface topology. The determination may made based on various factors, such as ego-machine state, (e.g., one or more detected features indicative of a designated operational scenario, proximity to a detected object, speed of ego-machine, etc.), estimated image quality of a corresponding environment visualization, and / or other factors. Accordingly, an environment around an ego-machine, such as a vehicle, robot, and / or other type of object, may be visualized in systems such as parking visualization systems, Surround View Systems, and / or others.