1280 results about "Vision sensor" patented technology

A multimodal intelligent agent system for dynamic environmental monitoring and user-centered support, consisting of: a multimodal sensor module configured to continuously acquire environmental and behavioral data from multiple input modalities, including at least one visual sensor, at least one acoustic sensor, at least one environmental conditions sensor, and at least one proximity or motion detection sensor, each generating modality-specific data streams representing visual images, audio waveforms, physical environmental parameters, and motion signatures within a monitored environment; a data preprocessing and fusion subsystem that is operationally coupled with the multimodal sensor module and configured to normalize, temporally align, and transform the modality-specific data streams into high-dimensional feature embeddings using a variety of encoders, wherein the visual encoder uses convolutional or vision transformer architectures, the audio encoder uses a spectral-temporal feature extractor, and the sensor encoder transforms raw analog data into context vectors suitable for multimodal alignment; a multimodal processing unit consisting of a transformer-based large language model (LLM) trained on paired multimodal datasets and configured to perform semantic fusion, context abstraction, and inference across the aforementioned aligned multimodal feature embeddings to generate a contextual understanding of environmental and behavioral states; an adaptive agent controller coupled to the multimodal inference processing unit and configured to instantiate, manage, and terminate a variety of task-specific intelligent agents, each agent being a software unit configured to perform a specialized function selected from meeting summarization, behavioral analysis, misplaced object detection, or environmental anomaly identification, with the agents dynamically interacting with the inference engine to retrieve contextually relevant multimodal embeddings for task execution; a personalization and adaptive learning subsystem consisting of a user preference database and a neural memory structure configured to update and refine model parameters based on user-specific interaction history, thereby enabling personalized output generation, prioritization of recommendations, and long-term behavioral adaptation; and An output generation interface is operationally connected to the adaptive agent controller and configured to produce multimodal output in textual, visual, and auditory form. The interface is capable of displaying human-readable summaries, notifications, and visual reconstructions of identified entities or environmental states.

The present disclosure provides a method for generating annotation data for robotic training using a hierarchical transformer-based model with multiple layers. The transformer-based model includes Alpha models generating low-level control outputs and Beta models generating high-level control outputs. The method receives multimodal input data comprising visual sensor data and natural language instructions, processes this data through the hierarchical transformer-based model to generate annotations at different abstraction levels, wherein Beta models create semantic annotations describing task objectives and Alpha models generate motor command annotations specifying robotic actions, and stores these annotations with the input data to create annotated training data for robotic control systems.

The invention discloses an intelligent welding control system and method based on multiple sensors and electronic equipment, and belongs to the technical field of automatic welding, the system comprises a sensing layer, a decision-making layer and an execution layer, through cooperative work of a front laser vision sensor, a rear laser vision sensor and a molten pool image sensor, a groove three-dimensional model can be accurately established before welding, and the welding precision is improved. And dynamic information of the molten pool is continuously collected in the welding process, a dual monitoring mechanism for the geometric characteristics of the welding line and the state of the molten pool is formed, and multi-source data collection and fusion in the whole welding process are achieved. The control method comprises the steps of pre-scanning modeling, feedforward parameter planning, real-time feedback adjustment of a molten pool, data fusion optimization and the like, and high-precision self-adaptive control over welding parameters is achieved by combining deep learning and the Kalman filtering technology. According to the method, the welding adaptability, the control precision and the intelligent level can be remarkably improved, and the method is suitable for high-precision welding scenes such as pipelines, pressure containers and steel structures and has remarkable engineering application value and popularization prospects.

The invention relates to the technical field of industrial robot control, and discloses a robot end trajectory optimization method and system based on industrial vision, and the method comprises the steps: obtaining a workpiece surface image in real time through a vision sensor, and obtaining deformation data through image enhancement and feature extraction; when the deformation quantity exceeds a threshold value, calculating a multi-axis coordination parameter by adopting an optimization algorithm to generate a trajectory correction instruction; integrating speed constraints by updating a control model, and determining a final trajectory by using Kalman filtering to fuse sensor feedback data; and the control parameters are iteratively adjusted in the deformation feedback loop, and a stable machining track is formed. The method can solve the problem that in the prior art, the robot tail end track precision is low.

The present disclosure provides a method for calibrating a humanoid robot, comprising obtaining a humanoid robot with original kinematic biasing values, controlling the humanoid robot through predetermined poses, capturing image data of body parts using vision sensors mounted on the humanoid robot while moving through the poses, determining revised kinematic biasing values by processing the image data using a bipedal spatial perception model trained using synthetic image data containing keypoints, and replacing the original kinematic biasing values with the revised kinematic biasing values. The bipedal spatial perception model processes captured image data to generate observed keypoint locations on robot components, which are compared with kinematic-based locations from joint encoder measurements to minimize discrepancies through optimization algorithms.

The invention provides a multi-agent collaborative visual completion method and system based on semantic communication, and the method comprises the steps: a first agent collects an occluded overall original image through a visual sensor, generates an occluded region mask through a lightweight semantic segmentation network, extracts mask semantic information, and transmits the mask semantic information to a second agent; the second intelligent agent matches the global semantic feature of the second intelligent agent with the received mask semantic information, locates a sheltered area, then cuts the sheltered area to obtain a semantic feature vector of the area, and returns the semantic feature vector to the first intelligent agent; and after the first intelligent agent receives the image, performing high-fidelity reconstruction on the occlusion area by using a conditional diffusion model to obtain a complemented image with consistent semantics, and performing weighted fusion and local color matching and splicing on the complemented image and the original image to obtain a global perception result. According to the scheme, perceptual information sharing and occlusion area reconstruction with low bit rate, high robustness and high real-time performance can be realized under limited communication resources.

The invention relates to the technical field of biological process intelligent control, and discloses a pomegranate leaf fermentation process optimization and intelligent control system based on digital twinning. According to the system, the Internet of Things and a visual sensor are integrated through a multi-source data sensing module, so that synchronous acquisition and fusion of temperature, pH, dissolved oxygen, flora density and image data are realized; the intelligent data preprocessing module extracts high-quality features by adopting a self-adaptive filtering technology; the digital twin simulation engine module drives the virtual model to dynamically update based on real-time data; the deep prediction module accurately predicts the fermentation trend through a deep learning algorithm; the intelligent diagnosis module realizes abnormal root cause analysis by using a knowledge graph; the reinforcement learning optimization module autonomously generates an optimal control strategy based on the diagnosis result; and the self-adaptive execution module accurately regulates and controls fermentation parameters. According to the system, multi-dimensional perception, high-precision prediction and adaptive optimization control of the fermentation process are realized, and the accuracy and stability of process control are effectively improved.

The invention discloses a position and posture compensation method for guiding installation of a contact network cantilever based on 3D vision and an automatic installation system. The position and posture compensation method comprises the steps that the spatial transformation relation between a 3D vision sensor coordinate system and a robot coordinate system is determined; the robot is controlled to move, and the three-dimensional point cloud data of the to-be-installed cantilever component and the three-dimensional point cloud data of the field installation foundation are collected; the collected three-dimensional point cloud data is registered with a corresponding preset CAD model, and a first real-time pose of the cantilever component in a robot coordinate system and a second real-time pose of the installation foundation in the robot coordinate system are obtained through calculation; calculating a pose compensation transformation quantity based on a pre-stored theoretical mounting pose of the cantilever, the first real-time pose and the second real-time pose; and according to the pose compensation transformation amount, planning and executing a motion track of the robot, and completing butt joint installation. The method is high in compensation precision, robustness and adaptability and high in construction efficiency, and the problem of uncertainty in automatic installation is fundamentally solved.

The invention relates to a heading machine pose measurement and control method and system based on visual direction finding and communication distance measurement. The system comprises a base station end measuring system in a roadway, a mobile end measuring system on a heading machine body and a motion control module. The method comprises the following steps: S1, establishing a base station end coordinate system; s2, establishing a mobile terminal coordinate system; s3, the base station side identifies and measures the vector information of the characteristic light source of the mobile terminal through a visual sensor; s4, the base station end and the mobile end measure the distance between the base station end and the mobile end through communication ranging equipment; s5, calculating the three-dimensional position of the mobile terminal in the coordinate system of the base station terminal based on the vector information and the distance information; s6, according to the three-dimensional position and the inertial measurement data of the mobile terminal, carrying out combined navigation calculation based on an EKF algorithm; and S7, performing pose control based on a combined navigation calculation result. According to the invention, a means of combining visual direction finding, communication distance measurement and inertial measurement is adopted, so that the position measurement precision is improved.

The invention relates to the technical field of equipment control, in particular to a feeding and discharging machining production control system and method based on a PLC, and the system comprises a multi-mode data sensing module which collects a three-dimensional point cloud and image sequence of a working scene through a laser radar and a visual sensor, and combines a vibration signal and a current signal of a machining main shaft; and after feature extraction, outputting a comprehensive state vector including the workpiece pose and the equipment health state. And the digital twinborn collaborative planning module receives the comprehensive state vector, drives a digital twinborn model to be synchronized with a physical environment in a virtual space, performs Monte Carlo tree search by applying a reinforcement learning algorithm, and generates a flexible control strategy with a time sequence mark. And the digital twinborn collaborative planning module adjusts a task allocation strategy according to the early warning signal, and corrects digital twinborn model parameters based on the execution log. According to the operation process corresponding to the modules, collaborative optimization of the feeding and discharging system and the machining rhythm is achieved, and the equipment utilization rate and the production efficiency are improved.

The invention discloses an autonomous exploration method based on multi-modal data fusion and a large language model, solves the problems of low autonomous exploration efficiency, insufficient decision intelligence, poor environmental adaptability and the like in the prior art, and belongs to the technical field of robots. The autonomous exploration method comprises the following steps: S1, deeply fusing laser radar geometric information and visual semantic information through cooperative work of a three-dimensional laser and a visual sensor, and constructing an accurate semantic map; s2, performing code conversion on the semantic map to enable a large language model to understand and perform reasoning; and S3, based on an output result of the large language model in the S2, selecting semantic-oriented exploration or geometric frontier exploration until the task is completed or all regions are explored. According to the method, accurate and detailed environment perception is realized, the intelligence and task correlation of exploration decision making are improved, and the method has wide application potential.

A strip mine unmanned vehicle advancing obstacle detection method based on thermal infrared and visible light fusion comprises the following steps: firstly, acquiring advancing obstacle image data in two modes in a strip mine area by adopting two visual sensors, namely a thermal infrared camera and a visible light camera; secondly, realizing alignment of the two types of images in space and time by using a method for matching local features between the images based on a deep neural network; and inputting the registered image into a double-branch network to extract features, and based on the obtained multi-scale image features, obtaining position information and a classification result of a detection target by using a positioning classification model. According to the method, feature extraction is carried out on two kinds of modal data through the double-branch network, complementation is realized through cross-modal multi-level feature fusion, feature fusion is realized in the middle stage, the unmanned vehicle can accurately recognize the advancing obstacles on the road in the complex environment of low visibility, low illumination and the like of the strip mine area, and the accuracy of the road obstacle recognition is improved. And the safety and the high efficiency of unmanned mine car operation are greatly improved.

The invention discloses a man-machine collaborative additive repairing and remanufacturing method based on multi-modal perception, and belongs to the technical field of intelligent manufacturing and additive manufacturing, and the man-machine collaborative additive repairing and remanufacturing method comprises the following steps: S1, based on an additive repairing environment of a damaged part, establishing a three-dimensional grid map; s2, constructing a three-dimensional damage model based on the damage morphology data acquired by the multi-modal data acquisition module; s3, based on the three-dimensional damage model, a planned repair path and matched technological parameters thereof are obtained; and S4, based on the planned repair path, the repair equipment performs additive repair on the damaged part. According to the method, the additive repair environment three-dimensional grid map system is constructed, the repair environment and the damaged part are subjected to multi-angle scanning, and the visual sensor is synchronously adopted to collect the three-dimensional shape data of the damaged part, so that the sub-millimeter-level cooperative positioning of the repair equipment operation space and the damaged area is realized; and an accurate three-dimensional space reference is provided for subsequent repairing operation.

The invention discloses a map updating method and device, a server, a system and a medium, and is suitable for updating and maintaining a high-precision map, and the method comprises the steps: collecting the track information of a vehicle and a synchronous road environment image collected through a vehicle-mounted visual sensor; recognizing and extracting perception elements including lane lines and ground marks from the image; converting the identified perception elements from an image pixel coordinate system to a geographic coordinate system, and generating perception elements of geographic reference; correlation matching is carried out on the perception elements of the geographic reference and corresponding map elements in a map database, and the quality of each time of matching observation is evaluated to generate a corresponding matching probability value and a quality weight; based on the matching probability value and the quality weight, calculating the support degree of single observation on map element state hypothesis; fusing the observation support degrees from a plurality of vehicles and a plurality of moments, and calculating a posterior probability of map elements needing to be updated; and deciding whether to update the map elements according to the posterior probability.

The invention discloses a time-space separation pulse Transform-based target recognition method, electronic equipment and a storage medium. The method comprises the steps of obtaining event stream data which is output by a target neuromorphic visual sensor and is used for target recognition; inputting the event stream data into a pre-trained target pulse neural network, performing pulse coding on the event stream data through a coding module, and outputting pulse coding features; performing local space-time feature extraction on the pulse coding features through a feature extraction module, and outputting local space-time pulse features; performing global spatio-temporal feature extraction on the local spatio-temporal pulse features through a spatio-temporal separation pulse Transform module, and outputting global spatio-temporal enhancement features; and performing linear mapping on the global space-time enhancement features through a task output module, and outputting a target recognition result. According to the invention, while event-driven sparse calculation and low power consumption characteristics are maintained, the discrimination stability of weak, small, sparse and slow moving targets is significantly improved.

The invention discloses a power dispatching master station robot autonomous inspection method and system based on SLAM and multi-mode perception. The method comprises the following steps: performing SLAM joint estimation through data fusion of a 3D LiDAR, a visual sensor and an IMU, performing inter-frame pose solution based on three-dimensional point set matching by adopting coordinate alignment and scale recovery, and introducing the IMU to perform cross-sensor consistency verification and adaptive pose optimization so as to solve the problem of positioning drift in a complex scene; meanwhile, natural language instructions are coded through a GRU network, visual features are extracted in combination with a residual network with an attention mechanism, time sequence fusion and modeling are carried out, and autonomous navigation decision making in a dynamic environment is achieved. The system comprises a corresponding SLAM estimation module, a multi-modal fusion decision module and an execution mechanism. According to the method, the positioning robustness, the navigation precision and the task adaptive capacity in the complex environment of the power dispatching master station are remarkably improved.

The invention discloses an automatic high-precision assembly system and method based on visual feedback, belongs to the technical field of automatic assembly, and solves the problem that an existing scheme is insufficient in the aspects of automatic integration and actual usability. Comprising an embedded control unit, an XYZ Cartesian sliding table, a limiting sensor, a visual sensor, a visual mark and an upper computer processing unit, the embedded control unit is connected with the upper computer processing unit, and the embedded control unit is used for receiving a control instruction and feeding back state information. The embedded control unit is connected with a motor driver of the XYZ Cartesian sliding table and used for sending a motion control signal, and the embedded control unit is connected with the limiting sensor and used for receiving a switch signal. Working space boundary recognition and automatic calibration of the camera-robot hand-eye relation are achieved, the configuration and deployment process of a visual guidance system on the XYZ sliding table is greatly simplified, the requirement for professional skills of operators is lowered, and deployment efficiency is improved.

The invention discloses a pig rib cutting path planning method based on 3D point cloud, and belongs to the technical field of intelligent cutting and robot automation. High-precision original point cloud of a rib area of a pig carcass is obtained in real time through a 3D visual sensor, and then a segmentation threshold value is dynamically adjusted by introducing a self-adaptive growth factor based on local point cloud density, so that the preprocessed point cloud is robustly and accurately segmented into rib and rib meat areas, and a centerline of a spine and a rib gap are synchronously positioned. And based on the segmentation result, a plurality of independent costal meat communication components are automatically identified and separated through voxelization clustering and graph theory connectivity analysis. For each component, the main extension direction of the component is determined by constructing an axis alignment bounding box, and a series of discrete path points closely attached to the actual surface of the point cloud are generated in the direction. Spatial fitting is carried out by utilizing a cubic polynomial curve to generate a smooth and continuous cutting path which is highly fit with an anatomical structure, and the cutting path is directly converted into an executable track of the robot.

Disclosed are a method, system, and apparatus of a skyward facing helmet camera and associated methods with artificial intelligence. In one embodiment, a low profile, concavely curved camera housing includes a skyward facing visual sensor, an artificial intelligence model communicatively coupled with the skyward facing visual sensor, and a responsive device. The skyward facing visual sensor on a top surface of the low profile, concavely curved camera housing captures an ambient sky above a wearer of a helmet. The artificial intelligence model detects an object of interest (e.g., a drone, a bird, a plane, a missile, a satellite, an ambient threat, and a celestial object) appearing above the wearer. The responsive device haptically notifies the wearer when the artificial intelligence model detects the object of interest.

Disclosed are an apparatus, system, and method of a visual sensor of a tactical gear to use facial recognition technology to identify a person of interest and to cause a responsive device on the tactical gear to notify a wearer. In one embodiment, a personal protective equipment includes a responsive device integrated in a tactical gear and a visual sensor. The visual sensor of the tactical gear identifies a target person using an identity artificial intelligence model. The responsive device notifies a wearer of the tactical gear when the visual sensor identifies the target person. Further, the responsive device notifies an additional person when the visual sensor identifies a threat to a protectee of the wearer and the additional person. The responsive device may vibrate when the visual sensor of the tactical gear detects an ambient threat to the protectee of the wearer.

The invention provides an intelligent photovoltaic glass surface cleaning system and method based on a multi-axis mechanical arm and a magnetic attraction track, and relates to the technical field of photovoltaic power station operation and maintenance. The magnetic attraction track unit, the moving platform unit, the multi-axis mechanical arm, the multifunctional end effector and the sensing and navigation unit are in control connection with the central control unit. The sensing and navigation unit comprises a visual sensor which is mounted at the tail end of the multi-axis mechanical arm or on the mobile platform unit and is used for identifying the boundary, the stain degree and the stain type of the photovoltaic glass; the distance sensor is used for detecting the distance between the tail end of the mechanical arm and the glass surface; and the positioning module is used for determining the accurate position of the mobile platform unit on the magnetic attraction track unit. According to the photovoltaic power station cleaning device, efficient, safe and high-adaptability cleaning capable of covering complex surfaces and reducing water resource dependence can be carried out on the photovoltaic modules, and therefore full-automatic and intelligent photovoltaic power station cleaning operation and maintenance are achieved.

The invention discloses an automatic material carrying device and method for a chemical robot, focuses on a robot automatic material carrying technology, and solves the problems of low manual material tray carrying efficiency, easy injury of workers and high cost in chemical production. The device takes a connection box body as a core bearing unit, and integrates an adjustable telescopic structure, an adjustable lifting structure, an electric roller, a visual sensor, a controller and a storage battery; the adjustable telescopic structure drives the adjustable lifting structure to extend into or retreat from the bottom of the tray, the distance of the adjustable lifting structure can be adjusted to adapt to trays of different specifications, and the height of the adjustable lifting structure can be adjusted to lift up or put down materials. During use, all the components are linked through a controller, and the automatic processes of positioning movement, distance adjustment, stretching into the bottom of the tray, material lifting and carrying to a target place are sequentially completed. Full-process automation is achieved, multiple sets of trays can be carried synchronously, different specifications and feeding heights are adapted, the labor intensity and the enterprise cost are reduced, the efficiency and safety are improved, and the device is suitable for various chemical engineering scenes.

The invention relates to a visual scale adaptive unmanned aerial vehicle detection method based on HRRP physical perception, and belongs to the technical field of mobile communication. The method comprises the following steps: constructing a cooperative monitoring architecture of an ISAC base station and a visual sensor, and establishing a space-time mapping relation between a radio frequency sensing coordinate system and a visual sensor coordinate system; performing macroscopic parameter estimation based on echo signals received by the ISAC base station, resolving spatial position coordinates of a target, generating a servo control instruction to drive a visual sensor holder to rotate, and locking the target at the center of a visual image field of view; extracting a frequency domain channel response of the echo signal, generating a time domain HRRP through IDFT, and estimating a radial physical span of a target in combination with a constant false alarm detection algorithm; constructing a self-adaptive image slice model based on physical structure constraint, and performing resampling on slices by using a scale normalization scaling factor; and inputting the normalized image block into the feature fusion detection network to complete fine detection of the unmanned aerial vehicle target.

A humanoid robot system comprises vision sensors for capturing image data, a computing architecture with processing hardware and memory, and a bipedal spatial perception model. The model includes a feature extractor that extracts hierarchical feature maps from input images, a robot data module that detects robot parts, and a robot vector data module that calculates three-dimensional spatial position and orientation data for each detected robot part. The feature extractor uses a feature pyramid network generating multi-scale feature maps through bottom-up and top-down pathways with lateral connections. The robot vector data module predicts 2D-to-3D point correspondences and solves perspective-n-point problems to obtain final position and orientation vectors, enabling real-time robot self-awareness and closed-loop visual servoing for precise object interaction.

The invention discloses an autonomous mobile robot path planning obstacle avoidance method and system, relates to the technical field of autonomous mobile robot path planning and obstacle avoidance, and is used for solving the problems that in a deep mine multi-layer roadway, multiple environmental risks are difficult to quantify, and once a key bottleneck channel is blocked, task failure is easily caused, and the reliability of path planning is insufficient. After an autonomous mobile machine is powered on, sensor calibration parameters are loaded, pose fusion is completed through extended Kalman filtering based on a multi-source odometer, an inertia sensor and a laser vision sensor, and an occupation grid map with static obstacles, dynamic candidates and passable marks is constructed. The method comprises the following steps: constructing a simultaneous multi-cause environmental risk feature vector to obtain an environmental comprehensive risk cost, performing layered screening and comprehensive cost sorting on search nodes in combination with task blocking sensitivity of off-line calculation on a mine laneway traffic map, and then generating a time parameterized speed reference trajectory; the Mecanum wheel omni-directional drive mechanism is guided to pass through the high-risk area along the safe and smooth path.

The invention discloses a target image recognition and target detection method based on a video enhancement algorithm, and relates to the technical field of image processing. The method comprises the following steps: firstly, receiving a rain, snow and fog scene video stream through a visual sensor, extracting a video frame target image, performing video enhancement processing, eliminating rain and snow shielding, fog blurring and noise, and generating an effectively enhanced image which is complete in target contour, clear in details and adaptive to subsequent detection; inputting the image into an improved YOLO model of the rain, snow and fog scene, completing feature extraction and category recognition through an optimized feature extraction network, outputting a preliminary target bounding box and a category label, and judging whether a target to be detected and a specific category exist or not; and finally, if the target exists, counting the detection data and carrying out validity verification, thereby realizing high precision, low misjudgment and strong real-time performance of target detection in severe weather of rain, snow and fog, and further effectively solving the problem of high detection result misjudgment rate caused by parameter adjustment lag of adaptive filtering in the prior art.

The invention belongs to the technical field of laser engraving, and particularly relates to an efficient laser engraving device for stainless steel, the efficient laser engraving device comprises an engraving machine, the engraving machine comprises a machine base, a connecting block and a laser engraving instrument, a push block is integrally formed at the upper end of the laser engraving instrument, and push rods are arranged on the front, back, left and right sides of the push block; the push rods are respectively connected with the output ends of external driving cylinders; a ball is arranged on the outer side of the middle of the laser engraving instrument, the ball is embedded into a connecting block and is in rolling connection with the connecting block, the connecting block is fixedly installed on the machine base, a visual sensor is arranged at the bottom of the connecting block, and the visual sensor is electrically connected with an external driving air cylinder. The visual sensor is used for recognizing character images engraved by the laser and controlling opening and closing of the external driving air cylinder, and the device solves the problems that an existing conventional engraving device is complex in adopted structure, poor in engraving quality and low in efficiency.

The invention discloses a welding machining method for a drawer sliding rail, and belongs to the technical field of welding machining. S1, a middle rail workpiece is conveyed to a welding platform; s2, the clamp is controlled, so that the separation blade workpiece is attached to the welding position of the middle rail workpiece; s3, the compressed to-be-welded area is scanned through a visual sensor, and detection data of the gap and the surface state are obtained; s4, based on the detection data, the output power, the welding speed and the focus position parameters of a laser welding head are set; and S5, according to the set parameters, the laser welding head is controlled to weld the joint part of the separation blade workpiece and the middle rail workpiece, the visual sensor is used for detecting the assembly gap and the surface state in real time, and it is guaranteed that the weld penetration depth is consistent, and connection is firm; and secondly, a low heat input process is combined, so that the welding thermal deformation is greatly inhibited, the flatness of the separation blade and the middle rail is effectively maintained, and the welding precision, the structural strength and the sliding smoothness of the sliding rail are remarkably improved.

The present disclosure discloses a product recognition apparatus and method, in which the apparatus includes: a vision sensor configured to obtain and transmit image information of a product to an arithmetic processing unit; the arithmetic processing unit configured to deal the image information of the product based on an image recognition model, to obtain and transmit category information of the product to a data transceiver; and the data transceiver configured to transmit the category information of the product to a backend server of a seller, so that the backend server of the seller makes a settlement based on the category information of the product and preset price information of different categories of products. By adopting the product recognition apparatus, it is possible to quickly recognize the categories of various products, increase efficiency and accuracy of the cashing, and improve the user experiences.

The invention relates to a rice mill control method and device and rice mill equipment. The rice mill control method comprises the steps that a to-be-milled object is milled according to a first rotating speed; wherein the to-be-milled object comprises cereals and / or brown rice, and the first rotating speed is the rotating speed corresponding to the cereals milled by the rice milling machine equipment; periodically acquiring target image data of the to-be-milled object through a visual sensor arranged on a circular processing channel of the rice mill equipment; determining the real-time type of the object to be ground according to the target image data and the reference image data; and according to the real-time type, the rotating speed of a motor of a grinding assembly of the rice mill equipment is dynamically adjusted until the to-be-ground object is ground into polished rice. According to the technical scheme, visual detection is combined with rotating speed self-adaptive switching, the rotating speed can be automatically switched according to the type of the to-be-milled object, it is ensured that grain husking and brown rice grinding are both in the optimal rotating speed state, and the rice milling effect and the processing quality are improved.