10 results about "Visual sensor network" patented technology

The invention discloses an industrial safety production risk intelligent early warning system based on image recognition, and relates to the technical field of risk early warning, and the system comprises an acquisition module for deploying a multispectral visual sensor network, synchronously acquiring visible light, thermal infrared and depth image data, carrying out the real-time preprocessing, and generating a multi-modal image data stream, an extraction module, and a processing module. The multi-modal image data flow is input into a dynamic feature extraction network, risk features are extracted and optimized through a shared encoder and a task specific decoder, a risk feature map is output, an analysis module inputs the risk feature map into a causal reasoning mechanism, and the risk feature map is analyzed. Constructing a risk factor causal graph model, analyzing a risk propagation path through intervention calculation, and outputting a risk assessment report; according to the method, by constructing the dynamic feature extraction network based on the MAML framework, adaptive feature optimization of a multi-task scene is realized, and the limitation of lack of task specific optimization is effectively overcome.

The present invention discloses a method for optimizing the coverage of a directed vision sensor network, which includes: establishing a two-dimensional model according to the spatial structure of the target monitoring area, arranging vision sensor nodes in the established two-dimensional model, and establishing a vision sensor position model; constructing a perception model for each vision sensor according to the performance of each vision sensor to obtain the coverage result of each vision sensor; obtaining a vision sensor network coverage system for the entire target monitoring area according to the vision sensor position model and the perception model of the sensor; optimizing the vision sensor network coverage system so that the coverage intensity value of any target point in the target monitoring area is the optimal value in the vision sensor network coverage result. The present invention improves the problems of manpower consumption, weak timeliness and weak pertinence existing in the monitoring technology, reduces manual on-site operations, and greatly saves manpower and material resources.

The present invention discloses a method for estimating the target view angle K-coverage rate of a wireless visual sensor network. By constructing a system model of the wireless visual sensor network for complex environments; according to the system model of the wireless visual sensor network, the target view angle K-coverage estimator for randomly deployed WVSNs in complex environments is derived to predict the target view angle K-coverage rate; Matlab is used to simulate the application scenarios under study, and the fitting situation between the theoretical numerical results of the target view angle K-coverage rate and the experimental simulation results is verified through simulation experiments. The present invention assumes that all nodes are randomly deployed in complex environments to achieve target coverage. At the same time, in order to better identify and track targets, WVSNs need to effectively monitor the surface direction of the targets; through simulation experiments, the theoretical numerical results of the model are compared and analyzed with the experimental simulation results, and the results show that the root mean square error between them basically remains within 6%.

An autonomous vehicle including a microphone array of a plurality of microphones, a visual sensor network configured to receive visual signals, at least one processor, and at least one memory storing instructions is disclosed. The instructions, when executed by the at least one processor, cause the at least one processor to: (i) generate spatial beamforming maps locating a sound source using a beamforming model corresponding to acoustic signals received at the plurality of microphones of the microphone array; (ii) apply a synthetic aperture expansion to the acoustic signals to increase resolution of the spatial beamforming maps; and (iii) generate a future visual frame based at least partially upon temporal information extracted from the spatial beamforming maps and visualization maps generated based on the visual signals received by the visual sensor network.

The present invention discloses a cigarette package rejection warning data communication method based on visual inspection of the packaging machine's appearance. The method writes a CCD cigarette package appearance inspection rejection warning sending end program and a receiving end program, installs them on the CCD appearance visual inspection and OPC industrial computer respectively, and performs remote data communication with the CCD appearance visual inspection and the OPC industrial computer: when a cigarette package is rejected, a prompt will appear on the OPC industrial computer operation screen. Click to view the defect image and prompt the operator to reject the defective cigarette package. The operator can find and solve the equipment problem by corresponding to the locomotive part of the defect image, which makes it convenient for the operator to understand the equipment operation status. This application quickly connects the CCD and OPC networks to share data and capture accurate information, solves the problem of transmitting large amounts of data images in a visual sensor network with limited computing, storage and other resources, and can effectively control the amount of cigarette package rejection, prevent defective cigarette packages from flowing into the next process, and accurately control product quality.

This invention belongs to the field of information science and technology, specifically a method for optimizing the deployment of visual sensor networks in dynamic construction environments based on risk grading maps. It includes a building information model (BIM) module, a dynamic multi-obstacle visual node optimization deployment data modeling module, and an optimization algorithm and visualization module. The BIM module, the dynamic multi-obstacle visual node optimization deployment data modeling module, and the optimization algorithm and visualization module transmit data via a risk map. The invention also includes the following specific steps: extracting information such as space, structures, and obstacles from the BIM model and generating a risk map. This invention considers the dynamic construction process and the differences in coverage requirements across different areas, grading different areas to allocate resources and prioritize areas with high coverage requirements, thus deriving the optimal visual sensor network deployment scheme throughout the entire process; and achieving visualization of the optimization results.

The invention discloses a humanoid robot body design method and device and a medium, and relates to the technical field of kinetic energy control, and the method comprises the steps: constructing an interactive dynamics digital twin of a humanoid robot; the method comprises the following steps: simulating an interactive dynamics digital twin in a virtual environment, and obtaining real-time relative space relation data through a distributed visual sensor network; in the process that the humanoid robot executes the self-adaptive motion trail, environment interaction is monitored through a distributed visual sensor network, and an actual kinetic energy loss value is collected; and inputting the energy consumption deviation value between the expected kinetic energy loss value and the actual kinetic energy loss value into the reinforcement learning model, and iteratively optimizing the prediction precision of the contact event type and the motion planning parameters of the online motion planner. According to the method, the expected kinetic energy loss value serves as a dynamic optimization target and is embedded into the online motion planner to generate the self-adaptive motion track, the motion planning process can be optimized in real time, and therefore it is ensured that task constraint and dynamic limitation are met while the kinetic energy loss is minimized.

A multimodal manufacturing data collection system based on wearable smart glasses fusion for supplementing blind spots of fixed vision sensors, and a method for collecting multimodal manufacturing data are disclosed. The essence of the configuration of the multimodal manufacturing data collection system according to an embodiment of the present invention is to include: a fixed vision sensor network that monitors the entire manufacturing site; smart glasses worn by a worker and equipped with a camera, a microphone, and a display; a central control server that receives data from the fixed vision sensors and smart glasses and determines and controls the priority of data sources according to the recognition state; an AI processing engine that converts collected multimodal data into text and numerical data; and a collaboration relay unit that relays collaboration requests through communication between smart glasses worn by a plurality of workers, so that another nearby worker can collect or verify data that one worker failed to collect.

This invention discloses a multimodal perception-based embodied intelligent robot cargo loading and unloading collaborative control method, relating to the field of cargo loading and unloading collaborative control technology. The method includes registering and calibrating the status of multiple embodied intelligent robots to make them schedulable; detecting the objects to be transported in real time through a visual sensor network and determining their quantity; and performing transportation analysis based on a target time, including: obtaining the shortest path from the starting point to the target point, identifying congestion nodes and determining their allowable values ​​based on the relationship between the path width and the travel width of the transport object, and obtaining the number of simultaneous departures. This application significantly improves system throughput and efficiency in complex bottleneck environments. By introducing a congestion node identification and allowable value quantification analysis mechanism, the width constraint of the physical path is accurately transformed into scheduling parameters. Based on this, a batch departure and interval adjustment strategy is designed, preventing congestion in narrow areas from the source, enabling the multi-robot system to maintain efficient and orderly flow even in environments with inherent bottlenecks.

The application belongs to the technical field of image processing, and particularly relates to a construction site three-dimensional map construction method fusing multiple source visual sensors, which arranges a multiple source visual sensor network at key structural parts of a construction area, ensures that the fields of view of at least two sensors in the same monitoring area overlap with each other, and performs space-time synchronization and coordinate correction on collected image data, so that image sequences under a unified space-time reference are obtained, subsequent feature matching, disparity calculation and point cloud fusion are ensured to be performed under a consistent time and space framework, the accuracy and consistency of three-dimensional map construction are ensured, local sparse point clouds and local dense point clouds are generated and registered and fused, the problem of insufficient accuracy or missing details of a single reconstruction method is effectively solved, the fused point clouds are converted to an absolute coordinate system, a dynamically updatable construction site three-dimensional digital map is finally generated, and the visualization of a construction scene is realized.