10results about How to "Improve perceived efficiency" patented technology

The invention provides a field end sensing guide type autonomous battery swapping station system and an autonomous battery swapping method. A battery swapping station performs environment modeling on a related area in the battery swapping station by controlling the angle of a holder and acquiring and receiving laser radar data and camera data, performs license plate recognition on a vehicle, and performs modeling and positioning on the vehicle in control; other obstacles in the environment are detected; automatically generating a map according to the environment model, and performing queuing and scheduling control on vehicles in the battery swap station by using the map; during environment modeling, the holder is controlled to perform global scanning, and during parking control, the holder is controlled to enable the laser radar and the camera to keep following a controlled vehicle; generating a track of the controlled vehicle from the position of the controlled vehicle to the parking space or the battery replacement area, and obtaining a parking control instruction; and the vehicle receives the parking control instruction of the battery swap station for remote execution, and feeds back the state of the vehicle, so that the labor cost and the time cost are reduced, and an efficient and low-price field end sensing and control scheme is realized.

The application discloses a kind of based on multi-source fusion and reinforcement learning's composite material additive manufacturing adaptive control method, first in printing process Synchronous acquisition space position, infrared temperature, tension and compression force and multi-source sensor data such as visible light image;Through the lightweight perception module constructed to each road data is parallelly processed and feature extraction, to force data adopts second-order difference to carry out state discrimination, to temperature data adopts multistage threshold segmentation and extracts forming area statistical feature, and reward feature is generated in combination with image recognition and position mutation detection;The multi-dimensional state features after fusion are combined with reward function, and the agent is trained using reinforcement learning algorithm to learn the optimal printing parameter decision strategy;The trained agent is used for real-time control, and the best printing speed and layer thickness instruction are dynamically output to the mechanical arm according to the current state. The application realizes accurate description and real-time adaptive control of complex multi-physical field coupling process, effectively improves printing quality and process stability.

The embodiment of the present application provides a blockchain group collaborative perception method, processor and storage medium. The method includes: constructing a blockchain communication network based on a Kautz graph; determining multiple blockchain nodes generated according to the Kautz graph; perceiving a target through each blockchain node to obtain a perception value of each blockchain node; for each blockchain node, the blockchain node constructs a message body according to its own perception value and message forwarding quantity; determining the neighbor node of each blockchain node and sending the message body to the neighbor node; after receiving the message body, the neighbor node reduces the message forwarding quantity contained in the message body by a preset value to obtain an updated message forwarding quantity; when the updated message forwarding quantity is greater than a preset threshold, the neighbor node forwards the message body to the neighbor node of the neighbor node; until the message forwarding quantity is less than or equal to the preset threshold, stop forwarding the message body to improve the accuracy and efficiency of perception.

The invention relates to the technical field of intelligent driving, in particular to an environment perception enhancement method and system based on intelligent driving, and the method comprises the following steps: obtaining enhanced voxel features used for sensing the form of an obstacle and positioning in a driving environment in three-dimensional point cloud data from the three-dimensional point cloud data of the driving environment; enhanced pixel features used for sensing the texture and color of an obstacle in the driving environment in the visual image are obtained from the visual image data of the driving environment; and performing feature fusion on the enhanced voxel features and the enhanced pixel features to obtain multi-source environment features, and performing obstacle detection on the multi-source environment features to obtain target positions and target categories of obstacles in the driving environment. According to the invention, the effective features for sensing the environmental obstacles are extracted from the multi-source information, redundant features are eliminated, the efficiency of sensing the obstacles by using the multi-source information is improved, and the timeliness requirement of intelligent driving on environmental sensing is met.

Provided are a sensing method, a communication device and a storage medium, the method comprising: receiving a first parameter of a first target sent by a second network device, the first parameter comprising a first distance; determining a sensing distance range of the first network equipment based on the first distance of the first target; under the condition that the second reflection signal is received, sensing measurement is carried out in the sensing distance range, and a second parameter of the first target is determined; the second parameter comprises a target distance, the target distance of the first target is the sum of a second distance of the first target and a third distance of the first target, the second distance of the first target is the distance from the first network device to the first target, and the third distance of the first target is the distance from the first target to the second network device; the sensing measurement range of the first network equipment can be narrowed, so that the sensing measurement calculation complexity is reduced, the sensing duration is shortened, and the sensing efficiency is improved.

The invention belongs to the technical field of unmanned cluster systems, and particularly relates to a man-machine hybrid system sensing control method based on a large model, which comprises the following steps of: inputting cluster state information, an image with a target frame, a target category, target positioning information and a situation assessment complexity index into a multi-modal large model; the multi-modal large model extracts key information from a large amount of data collected by the unmanned cluster, a simplified natural language situation assessment is generated to a human operator, the human operator sends a cluster control voice instruction conforming to a human language habit, the voice instruction of the human operator is converted into a natural language text through a voice recognition algorithm, and the natural language text is stored in the unmanned cluster. The multi-mode large model outputs an advanced cluster control instruction meeting the autonomous control algorithm format of the unmanned platform, so that the unmanned cluster directly takes action according to the intention of a human operator, and the perception and control efficiency of the man-machine hybrid system is improved.

The utility model discloses a crane hoisted object advancing sensing device which comprises two oppositely-arranged stand columns, a signal transmitting end and a signal receiving end, the signal transmitting end and the signal receiving end are installed on the opposite side faces of the two stand columns, and a cleaning mechanism is arranged on the opposite side faces of the two stand columns. The cleaning mechanism comprises a sliding block, a sliding groove, a moving plate, cleaning layers and a driving part, the sliding block is fixed to the inner wall of the moving plate, the sliding groove which is vertically distributed and is in sliding fit with the sliding block is formed in the outer wall of the stand column, and the cleaning layers corresponding to the signal transmitting end and the signal receiving end are arranged on the inner wall of the moving plate. According to the crane hoisted object advancing sensing device, by arranging the cleaning mechanism, the surfaces of the signal transmitting end and the signal receiving end can be automatically cleaned, the signal transmitting end and the signal receiving end cannot be attached by impurities or dust in the using process, and the crane hoisted object sensing efficiency is improved; and the effect that the sensing effect is more sensitive is enhanced.

The application discloses a kind of vehicle-road cooperative perception method and device based on vehicle-mounted point cloud, belong to the field of automatic driving, the method includes: using each vehicle in current time slot in driving environment complexity and communication link data rate of cooperation perception, construct the optimization problem of maximum total perception quality, solve it to obtain the point cloud processing task priority and optimal configuration corresponding to each vehicle;Each vehicle uses corresponding optimal configuration to sample compression and back to roadside server, so that roadside server uses point cloud processing task priority to carry out target detection and issue to each vehicle to carry out environmental perception.The optimization problem in the application considers the problem of communication resources, processing priority and the cooperative perception accuracy of multiple vehicles and roadside server, thereby solving the technical problems of poor perception quality caused by large delay, low accuracy and limited resources of existing automatic driving vehicles.

The application provides a robot visual operation control method and system based on eye tracking, and belongs to the field of robot vision and artificial intelligence. The method comprises the following steps: a head-mounted device with eye tracking function is used to synchronously collect data streams when an operator performs an operation task, wherein the collected data streams comprise eye movement data streams and scene visual data streams when the operator performs the operation task; for the preprocessed data streams, a dynamic Bayesian network is used to extract gaze-intention coupling features; the preprocessed data streams and the gaze-intention coupling features are input into a robot operation model with a double-path attention fusion that has been constructed, so as to obtain online attention results, and the robot performs corresponding operations according to the online attention results. The method of the application can quickly focus on key areas in complex scenes such as occlusion and interference, and improves the task success rate.

The application relates to the technical field of intelligent application of automobiles, in particular to an automatic driving perception method and device of a vehicle, a vehicle and a storage medium, wherein the method comprises the following steps: acquiring initial picture data of the surrounding environment of the vehicle; inferring at least one target task according to the initial picture data, and detecting whether preset adversarial samples exist in the initial picture data in the inference process; if the initial picture data does not have the preset adversarial samples, obtaining a perception result of the initial picture data after the inference is completed; otherwise, the preset adversarial samples of the initial picture data are denoised by using a preset denoising strategy, first picture data after denoising is obtained, and the perception result of the initial picture data is obtained again based on the first picture data. Therefore, the problems that all data are usually denoised in the related art, the workload of data processing is large, the perception efficiency is low, and noise of input data of a perception task cannot be effectively avoided are solved.