12 results about "Real time mapping" patented technology

Systems and methods for real-time map data coverage optimization are disclosed. A system includes one or more processors configured to determine a real-time map data collection coverage in an interested region, wherein real-time map data are collected by one or more consumer vehicles at locations of the consumer vehicles, determine whether one or more uncovered locations in the interested region are not covered by the locations of the one or more consumer vehicles, in response to determining that the one or more uncovered locations in the interested region are not covered by the locations of the one or more consumer vehicles, generate a route comprising the one or more uncovered locations based on a map of the interested region, and send an instruction to cause operation of a data-collecting vehicle to follow the route for real-time map data collection.

This application provides a real-time trajectory data processing method, apparatus, and system. The method includes: acquiring trajectory data of a moving object and performing denoising processing on the trajectory data to obtain valid trajectory point data; acquiring M candidate road network points of the current trajectory point from the valid trajectory point data; where M is a positive integer; determining a variable sliding window corresponding to the current trajectory point; wherein the length of the variable sliding window is K, the variable sliding window includes N first transition probabilities of the previous trajectory point and a first candidate road network point combination corresponding to each first transition probability, each first candidate road network point combination including K historical candidate road network points, where N and K are positive integers; and performing map matching processing on the M candidate road network points based on the variable sliding window to obtain the road network points of the current trajectory point. This achieves high-precision and low-latency real-time map matching of trajectory data.

A low-altitude 3D radio map reconstruction method based on scene guidance and sparse measurement is proposed. This method constructs and trains a reconstruction network offline, comprising a sparse data converter module, a radio map data feature extraction module, a multi-scale convolutional pyramid module, and a layer-by-layer radio map reconstruction module. In the online phase, the trained reconstruction network performs real-time map reconstruction. This invention utilizes a deep neural network architecture to fuse environmental scene understanding and sparse measurement data, achieving high-precision, physically consistent 3D reference signal received power (RSRP) field reconstruction, significantly reducing measurement overhead and improving reconstruction quality. By fusing environmental building distribution with sparse RSRP measurements, capturing environmental patterns using feature pyramids, and maintaining vertical dependence through a layered reconstruction strategy, high-precision 3D radio map reconstruction is achieved.

The invention provides an online high-precision map construction method and a model construction method thereof, and the model construction method comprises the steps: sequentially constructing a backbone network, a view conversion network, a hierarchical query embedded network and a model optimization network based on a MapTr model architecture, and obtaining an initial model; training the initial model to obtain a map construction model; and carrying out model integration on the plurality of map construction models to obtain an online high-precision map construction model. According to the method, efficient and accurate high-precision map construction can be realized through a stronger backbone network, an effective view conversion and data enhancement technology and a model integration strategy. The method has excellent performance in the aspect of multi-category semantic information extraction, and is suitable for real-time map construction tasks in an automatic driving system.

Systems and methods for real-time map data coverage optimization are disclosed. A system includes one or more processors configured to determine a real-time map data collection coverage in an interested region, wherein real-time map data are collected by one or more consumer vehicles at locations of the consumer vehicles, determine whether one or more uncovered locations in the interested region are not covered by the locations of the one or more consumer vehicles, in response to determining that the one or more uncovered locations in the interested region are not covered by the locations of the one or more consumer vehicles, generate a route comprising the one or more uncovered locations based on a map of the interested region, and send an instruction to cause operation of a data-collecting vehicle to follow the route for real-time map data collection.

This invention relates to the field of IoT intelligent control technology and discloses an intelligent system for mosquito control equipment, including a time synchronization and control module, a dual-count fusion module, a multi-dimensional environmental perception module, a prediction and control module, a health assessment module, a regional collaborative optimization module, and a visualization management module. The time synchronization and control module obtains device control commands based on the fusion of BeiDou and GPS dual-channel time signals; the dual-count fusion module combines a weighing sensor and image recognition algorithm to obtain the accurate number of mosquitoes captured; the multi-dimensional environmental perception module collects environmental parameters and performs noise reduction processing; the prediction and control module uses transfer learning and incremental learning to build an activity prediction model; the health assessment module performs local prediction and optimizes the data upload strategy; the regional collaborative optimization module uses weighted Kriging interpolation and particle swarm optimization algorithms; and the visualization management module constructs a real-time map display. This invention enables intelligent management and precise control of mosquito control.

The subject technology provides for route identification and clustering for real-time mapping. Various clustering techniques can identify groupings of relevant prior routes that can later be mapped against a current route. Filtering and matching techniques can be applied to identify and cluster prior routes. Tracked activity can also be compared against applicable prior activities to provide movement metrics and real-time comparisons between current and one or more recorded activities.

This invention provides a cartographic method for optimizing real-time map rendering, belonging to the field of cartographic technology for real-time map rendering optimization. This invention pre-calculates the screen space projection area at the central processing unit (CPU) and repackages it into batches with varying loads, then asynchronously submits the batches to the graphics processor (GPU) via a drawing indirect instruction mechanism. It uses 3D Hilbert curves to map tile coordinates as linear indices, combining quadtree divide-and-conquer recursion with user motion prediction to drive tile prefetching. It reconstructs low-resolution tile textures using a bi-branch map texture super-resolution fusion model and adaptively adjusts the high-frequency gating weight inference scaling factor through a comprehensive quality adjustment value. It compresses the occlusion query overhead per frame through a bi-phase occlusion culling pipeline combined with temporal reprojection. This invention solves the technical problems of unbalanced geometric subdivision load, low tile scheduling cache hit rate, and difficulty in adaptively balancing texture super-resolution reconstruction quality and inference latency during real-time map rendering.

The application provides a kind of unmanned aerial vehicle autonomous inspection trajectory planning method and system based on channel environment real-time map.The method first constructs real-time map containing semantic information by using multi-sensor fusion data online and generates Euclidean signed distance field synchronously;Then, based on the distance field, a safe flight corridor is generated, and a planning algorithm guided by a dynamic fuzzy logic controller that integrates energy state perception is used to plan a global spatiotemporal trajectory within the corridor that meets the dynamics and energy constraints;Next, taking the global trajectory as a reference, a local planner that integrates a velocity obstacle mechanism is used to perform real-time trajectory optimization and dynamic obstacle avoidance under the constraint of the safe corridor;Finally, the control instructions are executed and the image quality is evaluated, and based on the evaluation results and the state of the unmanned aerial vehicle, a dynamic decision is made on whether to adjust the trajectory or execute an emergency strategy.The application significantly improves the safety, efficiency and data acquisition quality of the unmanned aerial vehicle in the complex distribution network channel.

The invention of smart asphalt with predictive, diagnostic, and anti-icing capabilities for improving safety and reducing accidents at urban intersections and intercity roads includes a network of sensors that collect data related to environmental conditions and road surface features. This data is then processed using AI algorithms to predict and detect the formation of black ice on the asphalt surface. Upon detecting or predicting the occurrence of black ice, a heating system embedded within the asphalt layers generates appropriate heat to prevent ice formation. Depending on the type and characteristics of each data, different AI algorithms are employed, and the accuracy of prediction and detection is improved using data fusion techniques. The system generates a real-time map of the location and extent of identified and predicted ice formation, enhancing the safety and maintenance of the road network.

This invention belongs to the field of artificial intelligence technology, specifically relating to a method and system for navigation and autonomous path planning of a composite robot based on a large task model. The method includes the following steps: 1. Constructing a vehicle-arm coordinated coupled kinematic or dynamic integrated model and identifying parameters; 2. Performing task-oriented coordinated control through behavior trees; 3. Performing task understanding and requirement modeling; 4. Collecting environmental data through sensors, performing environmental perception and real-time map construction; 5. Performing global path planning and dynamic execution, selecting the local optimal trajectory; 6. Performing motion control and feedback adjustment. This method improves the motion coordination and real-time performance of the composite robot, effectively avoiding joint position and speed exceeding limits.

A dynamic, graph-based anomaly detection system (100) for real-time map risk assessment, comprising: a data acquisition module configured to receive transaction data from one or more transaction sources in real time; a graph construction module configured to generate a dynamic graph comprising a variety of nodes representing entities such as cards, merchants, devices, accounts, and locations, as well as edges representing relationships between the entities; a graph update module configured to continuously update the dynamic graph based on incoming transaction data; an anomaly detection engine configured to analyze the dynamic graph to identify anomalous patterns that may indicate fraudulent activity; a feature extraction module configured to derive graph-based features from the dynamic graph; a machine learning module configured to process the graph-based features and generate a risk assessment associated with a transaction; and a real-time rating module configured to assign a risk rating to the transaction to enable fraud detection decisions.