41 results about "Regional model" patented technology

This invention relates to the field of airspace management technology, specifically disclosing a method and system for dynamically updating airport airspace obstacle data. The method includes: acquiring regional benchmark data based on a first acquisition precision under standard operating conditions to construct a benchmark regional model; acquiring real-time regional data periodically based on a second acquisition precision under real-time operating conditions to construct a real-time regional model with time labels; identifying the real-time regional model based on an adaptive detection parameter set, locating abnormal candidate areas, and simultaneously locating abnormal obstacles; marking the location results, and visually displaying the real-time regional model. This invention acquires relevant airport information through an acquisition device with data transmission capabilities, performs 3D modeling of the airport, and, combined with a pre-acquired standard model, quickly determines abnormal areas and objects at different times, thus providing intuitive feedback to management personnel. The method boasts extremely high identification efficiency and readability.

The embodiment of the invention provides an optimal scheduling method and system for a virtual power plant based on a cloud and fog edge architecture, a computer readable storage medium and a computer program product. The method comprises the following steps: edge nodes realize local optimization scheduling of end side equipment, fog nodes realize coordinated aggregation of a plurality of edge nodes in a jurisdiction area, and a cloud platform realizes cross-area global optimization and knowledge sharing. According to the scheme, a cloud platform, fog node and edge node three-level collaborative optimal scheduling architecture is constructed, and local optimization capability, fog node area coordination capability and global optimization capability are given to the edge nodes, so that the inherent single-point fault risk of traditional centralized scheduling is overcome, and when the communication of the cloud platform is interrupted or invalid, the cloud platform can be effectively optimized. Each fog node can guide autonomous operation in the jurisdiction based on the latest regional model; and when the fog node hierarchy fails, the edge node can still independently carry out local optimization, so that the robustness and reliability of the system under the abnormal working condition are enhanced.

This invention discloses a method and network model for reconstructing missing soil moisture data based on cross-attention, belonging to the fields of remote sensing image processing and artificial intelligence. It involves collecting SMAP, ERA5-Land soil moisture products, and various auxiliary prediction factor data. The auxiliary data is resampled to 9km and preprocessed using multi-source data. A CASTNet model is then constructed. After inputting the preprocessed data into the model, feature extraction, refinement, and enhancement are sequentially performed through modules for spatiotemporal automatic completion, multi-factor feature refinement, and multi-scale attention enhancement. Finally, the model is trained based on a combined loss function composed of global and local losses, and the trained model is used to fill in the missing areas of soil moisture remote sensing data. The CASTNet model contains three cascaded core modules, each implementing preliminary spatiotemporal feature completion, multi-factor feature fusion refinement, and multi-scale feature enhancement, respectively. This invention fully leverages the complementary information of multi-source data, improving the accuracy of soil moisture data reconstruction and ensuring the spatiotemporal continuity and integrity of the reconstructed data.

The invention relates to the field of artificial intelligence, and provides a transformer state visualization method based on a BIM model, and the method comprises the steps: obtaining a region model of a region where a target transformer is located according to a preset BIM model; determining a first loss parameter of the target transformer according to the regional model; determining a second loss parameter of the target transformer according to the load of the target transformer; and calculating life information of the target transformer according to the first loss parameter and the second loss parameter, and displaying the target transformer in the BIM model according to the life information. The service life information of the transformer is displayed in the BIM model, so that the intelligent degree of applying the BIM model to power equipment management and the intuitive degree of the transformer state in the BIM model are improved.

The application discloses a wind field reconstruction and prediction method and device, electronic equipment and storage medium, and belongs to the technical field of wind field data processing. The method comprises: uniformly encoding historical wind speed data and real-time wind speed data of multiple height layers, so that the model can mine the vertical coupling linkage relationship between wind fields at different height layers and the time evolution law of the wind field; geometric prior information and observation masks are introduced, so that the model can complete wind field flow constraint analysis combined with regional building layout and divide the measured data area and the blank reconstruction area; when the model finally completes global wind field deduction based on multi-dimensional fusion features, not only the accuracy of the wind field reconstruction result is improved, but also multi-step fine wind field prediction is realized, the disadvantages of single wind field deduction dimension, lack of environmental constraints and insufficient prediction accuracy of the traditional technology are solved, and the actual needs of fine wind field reconstruction and short-term accurate wind field prediction in a complex building group scene are met.

The present application relates to the technical field of hub polishing robots, and particularly relates to a hub polishing robot trajectory planning method based on 3D vision curve technology; by comparing an actual 3D curved surface graph with a preset 3D curved surface graph, a to-be-polished region model graph of each to-be-polished region in a target hub is generated; according to the to-be-polished region model graph of each to-be-polished region, optimal polishing parameters for polishing processing of each to-be-polished region in the target hub are obtained by pairing in a database; according to the optimal polishing parameters for polishing processing of each to-be-polished region in the target hub, optimal polishing trajectories of a polishing robot are planned; and the optimal polishing trajectories of the polishing robot and the optimal polishing parameters for polishing processing of each to-be-polished region in the target hub are sent to a control terminal. More accurate, self-adaptive, efficient and consistent polishing effects can be achieved, and production efficiency and product quality are improved.

The application discloses a cross-region crop remote sensing image processing method, device and equipment fused with geographic information, relates to the technical field of crop data processing and remote sensing cross technology, constructs a generalization data set of latitude and terrain joint partitioning through sample geographic information data and sample satellite remote sensing image data, obtains a geographic prototype library corresponding to a geographic unit, then trains a crop classification prediction model based on the geographic prototype library, and performs crop classification prediction by using the crop classification prediction model, which can effectively capture the combined influence of geographic multidimensional factors on crop growth, solve the fundamental problem of poor generalization of the cross-region model, improve the cross-geographic processing capability of the crop classification prediction model, and realize high-precision, high-robustness and low-cost crop remote sensing classification.

The invention discloses a regional high-resolution atmosphere reanalysis data construction method based on dynamic downscaling, and aims to improve the depiction capability of medium and small-scale weather processes. According to the method, global reanalysis data is used as a background field, multi-source observation data and high-resolution land utilization information are fused, multi-time and short-period simulation is executed by constructing a multi-layer nested WRF region mode and combining an assimilation strategy combining analysis Nudging and observation Nudging, and a reanalysis data set with hour-level and kilometer-level resolution is constructed step by step. Through comparison and verification of multi-source observation data, the constructed data set is obviously superior to ERA5 in the aspects of ground meteorological elements and vertical profile structure reduction, and has higher regional adaptability and simulation precision. The method is suitable for a plurality of high-resolution meteorological application scenes such as regional artificial intelligence meteorological large models, urban fine meteorological services, high-influence weather disaster early warning, regional climate change evaluation, energy meteorological resource evaluation and the like.

The application provides a meteorological disaster high-resolution regional mode prediction system and method, relates to the technical field of meteorological disaster prediction, and comprises the following steps: performing scale reduction processing on target data of a region to be processed to obtain scale-reduced target data, and performing fusion on multi-source observation data to obtain grid fusion data; according to a preset period, different target observation data are subjected to physical quantity correction, error estimation, quality control and format conversion processing by using specific technical methods, and the processed target observation data are subjected to hourly rapid cycle assimilation processing to obtain target assimilation analysis data; and the scale-reduced target data, the target assimilation analysis data and localized data are used to simulate the operation of a localized numerical prediction mode, meteorological disaster numerical prediction results of different scale requirements are generated, and the meteorological disaster numerical prediction results are corrected by using corresponding scale grid fusion data, so that the technical problem of low prediction accuracy of an existing meteorological disaster system is solved.

The invention relates to the technical field of karst geological exploration, in particular to an intelligent detection method and system for karst action of a pumped storage power station in a karst area, and the method comprises the steps: firstly constructing a large-range sample database covering multiple types of karst areas; training a Transform basic model based on the database, and performing adaptive fine tuning through a target area lightweight sample to obtain an area model capable of automatically outputting a DBSCAN parameter and a transient electromagnetic low resistance threshold adaptive to the area; meanwhile, mining an earth surface-underground feature association rule based on a sample database to construct an association library, training a GCN model according to the association library, and performing fine tuning through a target region fine tuning data set to obtain a target GCN model; and finally, identifying earth surface and underground karst nodes of the target area by using parameters output by the area model, and inputting the earth surface and underground karst nodes into the target GCN model to output a communication state between the nodes. According to the method, the cross-regional parameter adaptive precision and efficiency of karst detection are improved, and accurate identification of the earth surface-underground karst communication system is realized.

The invention relates to the technical field of artificial intelligence, in particular to an industrial equipment fault diagnosis method and system based on multilevel federated learning, and the method comprises the steps: collecting operation time sequence data of industrial equipment, training a fault diagnosis model deployed at a current equipment end, and obtaining trained local model parameters; based on a dynamic weight distribution strategy, performing weighted aggregation on the received local model parameters of the plurality of equipment ends to generate region-level model parameters; integrating all region-level model parameters, and injecting differential privacy noise to generate a global fault diagnosis model; carrying out collaborative optimization compression on the global fault diagnosis model, and distributing the compressed model to each equipment end to update the fault diagnosis model currently deployed by the equipment end; and based on the contribution data of each equipment end in the current round and historical federated learning, executing a contribution evaluation and incentive distribution process, and updating the dynamic weight distribution strategy with an evaluation result. And the quick response requirement of an industrial scene is met.

This invention discloses a multi-level dynamic monitoring method and system for slope deformation and instability, relating to the field of slope monitoring technology. It includes defining the degree of continuity of the medium under the slope according to the geological information of the medium, marking the degree of continuity at corresponding locations under the slope, and dividing the slope into different regional types. This standardizes the slope geological conditions and provides a reliable foundation for the subsequent construction of regional models. Setting target scales for different regional types at different locations under the slope improves the adaptability of different regional models and balances timeliness and computational accuracy. Analyzing the acoustic emission characteristic rules corresponding to each part of the slope, a FLAC-PFC model is established to monitor slope deformation and instability. Targeted acoustic emission characteristic rules are set, establishing a slope model that takes into account both geological description and acoustic emission characteristics, improving the reliability and adaptability of slope deformation and instability monitoring, and ensuring the stability of slope deformation monitoring.

The invention provides an adaptive model precision control method and system oriented to city-level multi-physics field simulation. The method comprises the following steps: aiming at urban simulation tasks such as a wind environment, a thermal environment, sunlight, a pedestrian flow, energy consumption and the like, reading regional models with different precision levels from an urban model library with multi-level detail levels as candidate models; based on the simulation target quantity corresponding to the simulation task, carrying out regional division on the city space, and calculating a regional sensitivity index according to the influence of the precision change of each regional model on the simulation target quantity; and determining a model precision grade adopted by each region according to a sensitivity priority principle by combining an error estimator introduced by model precision change and computing resource constraints. Through the above mode, on the premise of ensuring the reliability of a simulation result, the calculation scale of a low-sensitivity area is reduced, so that the overall simulation calculation amount and the calculation duration are reduced, and reasonable distribution of limited calculation power resources in urban simulation is realized.

The invention discloses an intelligent furniture adaptive control system based on external environment conditions, which relates to the field of intelligent furniture control and comprises a data acquisition module, a regional model construction module and an adaptive control module. The data acquisition module comprises a parameter acquisition unit and an environment sensing unit which are respectively used for acquiring operation parameters of the air conditioner and external environment data; the regional model building module is used for building a regional simulation model for the region where the air conditioner is located and importing the obtained external environment data and the operation parameters of the air conditioner into the regional simulation model; the self-adaptive control module is used for adaptively adjusting the operation parameters of the air conditioner according to the real-time response threshold value; therefore, the running parameters of the air conditioner can be dynamically and adaptively adjusted according to the change of the overall environmental parameters in the service area, and the temperature in the service area better meets the user requirement.

The invention provides a model processing method and device, electronic equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a first face model and a to-be-processed second face model, and performing global rigid alignment on the first face model and the second face model to obtain an initial alignment result; for each first area model, performing first rigid alignment on the first area model to obtain an area alignment result corresponding to the first area model; based on a region weight corresponding to the first region model, performing first smoothing processing on the region alignment result to obtain a smoothed region alignment result; performing second rigid alignment on the accessory component corresponding to each first area model to obtain a target alignment result corresponding to the first area model; and performing second smoothing processing on the target alignment result corresponding to each first area model to obtain a target model corresponding to the second face model. According to the invention, the accuracy and fineness of face model processing can be improved.

The application relates to the technical field of production monitoring, and particularly discloses an Internet of Things centralized monitoring method and device for industrial automation equipment, which comprises the following steps: acquiring image information containing corresponding monitoring terminal parameters; receiving image information containing monitoring terminal parameters sent by a monitoring terminal, and performing regional identification on the image information; the monitoring terminal parameters comprise a monitoring terminal number; sending a monitoring instruction to the monitoring terminal according to the regional identification result, acquiring a detail image obtained by the monitoring terminal based on the monitoring instruction, and generating a monitoring report according to the detail image. The technical scheme of the application can acquire images of the whole region through multiple image acquisition devices with high degrees of freedom, update a regional model in real time, perform segmentation on the regional model into a device area and a personnel area, further acquire a detail image based on the segmentation result, and can play a good reference role and reduce the monitoring fatigue of monitoring personnel.

The present application relates to the technical field of environmental monitoring, and particularly relates to an air-ground multi-source spatio-temporal information fusion perception method and system based on artificial intelligence, comprising: constructing a three-source data acquisition architecture, using an intelligent calibration model to dynamically calibrate the three-source data in space and time, and obtaining calibrated data; correlating and verifying the calibrated data and quantifying the weight of interference factors through a causal reasoning model, dynamically adjusting the field constraint parameters through a time sequence feature coding model, completing deep fusion through an intelligent fusion model, and outputting high-precision fusion results; constructing a field knowledge graph, mining potential business rules and identifying unknown abnormal patterns through rule mining and abnormality identification, and obtaining an iteratively updated knowledge graph dynamic; based on a collaborative learning framework and combined with privacy protection technology, realizing cross-region model collaborative evolution, constructing a scenario-based value conversion engine to generate a business structured report. The present application can improve the data fusion accuracy and the full-link traceability accuracy.

This application provides a method and apparatus for training a diversity data aggregation model based on industrial network partitioning, relating to the field of computer technology. It enables model training by adjusting client partitions according to data diversity, thereby improving data security and robustness during model training. The method includes: partitioning multiple clients into industrial network regions based on client characteristics and robustness parameters of each client, obtaining client regions, each client region including at least one client; acquiring client data from each client region; training the model based on client data to obtain regional model parameters corresponding to each client region; aggregating the regional model parameters from each client region to obtain global model parameters; and updating the model parameters based on the global model parameters.

The invention discloses a slope deformation instability multi-level dynamic monitoring method and system, and relates to the technical field of slope monitoring, and the method comprises the steps: defining a medium continuity degree under a slope according to medium geological information, marking the medium continuity degree at a corresponding position under the slope, and dividing region types of different positions under the slope, the slope geological condition is standardized, and a reliable basis is provided for construction of a subsequent region model. And the target scales of different area types at different positions of the slope are set, so that the adaptability of different area models is improved, and the timeliness and the calculation precision are balanced. According to the method, the acoustic emission characteristic rule corresponding to each slope partial region is analyzed, the FLAC-PFC model is established to monitor the slope deformation instability condition, the acoustic emission characteristic rule is set in a targeted manner, the slope model capable of giving consideration to geological description and acoustic emission characteristics is established, the reliability and adaptability of slope deformation instability monitoring are improved, and the slope deformation instability monitoring accuracy is improved. And the stability of slope deformation monitoring is ensured.

The invention relates to the technical field of hydro-fluctuation belts and water and soil conservation, in particular to a hydro-fluctuation belt mulberry bean interaction system construction method based on hydrological rhythms. According to the method, a water level change rate gradient parameter set is constructed, and area mode division, step sequence cooperative execution and self-adaptive optimization feedback are sequentially driven; gradient parameters are generated based on measured data of mechanical water level gauges arranged along the elevation, regional threshold values are dynamically set in combination with soil type labels, mode switching reliability is ensured through upstream and downstream physical cooperative verification, a cross-regional operation time sequence is woven according to the gradient direction and numerical values, and finally an execution effect is fed back to gradient parameter reconstruction. And closed-loop regulation is formed. The invention provides a hydro-fluctuation belt mulberry-bean interaction system construction method based on hydrological rhythm, and solves or at least alleviates the problems of regional response mismatch, high system collapse rate and step blockage caused by global synchronous operation triggered by a single-point water level in the prior art.

The application discloses a numerical model and large model cooperative prediction method based on multi-dimensional water vapor assimilation, through cooperative driving of a WRF numerical model and a Pangu large model, global large-scale characteristics trained on reanalysis data are retained, and the deficiency of a regional model in detail description is made up through local fine tuning, so that solid technical support is provided for construction of a hybrid prediction system with generalization ability and regional prediction accuracy, and the problem of insufficient capturing ability of an existing meteorological prediction large model on an extreme weather event is solved.

The invention relates to the technical field of energy control, in particular to a distributed energy dynamic control system and method based on a soil medium, and the method comprises the following steps: collecting physical data of a buried pipe field, and constructing a regional model based on the physical data; collecting equipment data of the heat pump connected with each buried pipe group, and integrating the three-dimensional model of the heat pump into the regional model to form a heat exchange model; configuring a control network for each heat exchange sub-region in the heat exchange model, and establishing communication connection between the control network and the heat pump corresponding to each heat exchange sub-region; and the heat load demand temperature values of the heat pumps corresponding to the heat exchange sub-areas and the operation data corresponding to the heat pumps are obtained, the stability of the water flow temperature in the heat exchange areas corresponding to the heat pumps can be improved, and therefore the operation efficiency of the heat pumps is improved.

The application discloses a geographical grid space data processing method, a terminal device and a computer readable storage medium. The method comprises the following steps: receiving a position information acquisition request, wherein the position information acquisition request at least comprises to-be-measured position latitude and longitude information and a grid level; converting the to-be-measured position latitude and longitude information and the grid level into a grid number based on a preset function; when receiving a region investigation request based on the grid number, acquiring geographical grid space features of a to-be-investigated region; and constructing a region model according to the geographical grid space features and the grid number. The problem that the geographical data obtained finally is inaccurate due to large grid deformation in related grid cutting technology is solved. The effect of improving the accuracy of geographical grid data is achieved.

The invention relates to the technical field of regional new energy configuration, in particular to a regional new energy capacity optimal configuration method, which comprises the following steps: S1, collecting dynamic data of new energy equipment in a region, and constructing a virtual region model mapped with a physical region by using a digital twinborn technology; s2, based on the virtual region model, constructing a reinforcement learning agent, and setting multiple targets as a reward function of the reinforcement learning agent; and S3, according to dynamic data obtained through real-time monitoring of the virtual region model, when the method is used, multiple targets of new energy supply stability, economy, environmental friendliness and the like are comprehensively considered on the basis of an optimization strategy of multi-target reinforcement learning, the environmental friendliness and the reliability of energy supply are improved while the economy is guaranteed, and the method is suitable for popularization and application. The problem that single target optimization excessively depends on a certain type of energy or neglects environmental protection requirements is effectively avoided, and multi-dimensional optimization configuration and comprehensive benefit improvement are facilitated.

The invention relates to the technical field of pipeline laying, in particular to a multi-sensing feedback drilling, jacking and pulling combined intelligent construction method and system for pipeline laying, and the method comprises the steps: firstly collecting environment data of a construction region to construct a region model, simulating a construction scheme based on the model to generate an optimization scheme, and carrying out fixed-point sampling according to the optimization scheme; the model and the scheme are adjusted through sampling data, construction is conducted according to the adjusted scheme, monitoring data are collected in construction, evaluation information is obtained through model analysis, intelligent management and control of the whole construction process are achieved, the scheme is simulated and optimized in advance, and construction risks are reduced; and real-time feedback adjustment is achieved, the construction quality and progress are guaranteed, the cost is reduced, the accuracy and efficiency of pipeline laying are improved, and efficient and safe pipeline laying is achieved.

The invention discloses a joint control lighting control method and device based on edge collaborative awareness and a related medium, and the method comprises the steps: carrying out the structural modeling of floor information, and obtaining edge deployment topological data; configuring lamp parameters according to the edge deployment topological data to obtain reference sensing field data; performing steady-state fitting on the reference sensing field data to obtain an edge reference data set; differential calculation, threshold judgment and window aggregation communication processing are respectively carried out to obtain collaborative awareness event data; performing regional data association on the collaborative awareness event data to obtain joint control demand data; and combining the joint control demand data with a preset region model to generate a control instruction, and obtaining a real-time control result. According to the method, the control instruction is generated by combining the joint control demand data obtained through calculation with the preset region model, and the real-time control result is obtained through distributed scheduling issuing, so that a standard reference system for unified spatial modeling of the joint control region is provided, and data required for joint control decision making is perfected.

The application discloses a Gaussian transformation and mixed four-dimensional variation assimilation method for instantaneous precipitation rate of a stationary satellite, constructs a FY-4 stationary meteorological satellite precipitation rate observation operator and a mixed four-dimensional variation assimilation scheme, establishes a Gaussian transformation and error optimization statistical method meeting different precipitation rate data requirements, introduces high-temporal and high-spatial resolution stationary satellite precipitation rate data into a regional model mixed four-dimensional variation assimilation system, and improves analysis and prediction effects of the model on a typhoon. Meanwhile, two selectable Gaussian transformation schemes meeting different requirements and a precipitation rate observation error scheme which changes adaptively with rain intensity are established in the assimilation system, and the problems that the assimilation effect of precipitation physics in the model is weakened due to the strong non-Gaussian distribution characteristics of the precipitation physics are effectively improved.

This disclosure provides a method, apparatus, and device for cutting a virtual model. The method includes: optimizing the geometry of a target 3D model in a virtual space to obtain a reconstructed model that meets the requirements of a preset cutting algorithm; controlling interactive rays to move in the virtual space according to received cutting control commands, determining cutting path points of the target cutting region on the surface of the reconstructed model, and recording the cutting path point information; generating a 3D cutter model that intersects with the reconstructed model using a preset cutting algorithm; and decomposing the reconstructed model into a target region model and a residual model based on the 3D cutter model. The method provided by this disclosure can obtain a reconstructed model suitable for cutting by optimizing the geometry of the target 3D model; it controls interactive rays to cut based on cutting control commands, thus enabling more intuitive cutting operations; and it provides higher robustness by using a 3D cutter model for cutting.

The invention discloses a quadruped robot mapping method based on multi-sensor fusion, and belongs to the technical field of robot environment perception and SLAM. The method comprises the steps of sensor calibration, laser radar inertial fusion SLAM mapping, global point cloud registration, visual inertial fusion SLAM mapping and sliding window optimization marginalization processing. By introducing ground segmentation of a concentric region model, ground likelihood estimation, degradation robust decoupling point cloud registration, hybrid optical flow tracking and an improved Scherr marginalization strategy, the mapping precision and robustness in complex terrains, dynamic illumination and unstructured environments are significantly improved. Experiments show that the method is superior to an existing mainstream algorithm on a public data set and an actual quadruped robot platform, and is suitable for high-precision map construction of the quadruped robot in complex scenes such as uneven ground and illumination variation.

This invention relates to the field of rice paddy methane carbon emission assessment technology, specifically to a spatiotemporal dynamic assessment method and system for rice paddy methane carbon emissions that integrates multiple source factors. The method includes: collecting multi-source data to construct a spatiotemporal dataset; performing preprocessing and feature mining to generate a comprehensive feature set; calculating the physical constraint field based on the rice paddy methane production and oxidation mechanism, inputting this field and the feature set into a spatiotemporal graph neural network, extracting spatiotemporal features through graph convolution and temporal modeling, strengthening key factors using an attention mechanism, and integrating uncertainty estimation to output emission data with confidence intervals; optimizing model parameters through multi-regional validation and data assimilation; and achieving cross-regional model transfer based on federated learning and domain adaptation techniques. The system includes data acquisition, preprocessing, physical prior and assimilation, spatiotemporal modeling, federated learning, and output service units. This invention achieves deep integration of mechanisms and data, improving assessment accuracy and cross-regional applicability.