331 results about "Environmental perception" patented technology

The invention relates to an indoor autonomous navigation method for a micro unmanned aerial vehicle, belonging to the technical field of autonomous navigation of micro unmanned aerial vehicles. The method comprises the following steps: estimating the motion state of the micro unmanned aerial vehicle based on an RGB-D camera and an MEMS inertial sensor; performing real-time three-dimensional environment modeling based on fusion of the RGB-D camera and the MEMS inertial sensor; and performing real-time trusted path planning and path tracking control, implementing position control and attitude control through the three steps, and guiding the unmanned aerial vehicle to track the preset path to fly. According to the method, the unmanned aerial vehicle positioning accuracy and the motion state estimation accuracy are improved; the indoor autonomous environmental perception capability of the micro unmanned aerial vehicle is improved; according to the generated path, the positioning accuracy can be effectively guaranteed on the basis that the path feasibility is met; and the autonomy capability of the micro unmanned aerial vehicle is effectively improved.

The present invention provides an intelligent road facility system. The system comprises: a road side unit having perception, communication, control, driving area calculation functions; a traffic control unit and a traffic control center; a vehicle unit and a vehicle interface; a traffic operation center; and a calculation and information service center based on cloud. The road side unit is configured to provide real-time vehicle environmental perception and traffic state prediction and send an instant control command to a corresponding vehicle unit; the traffic control unit and the traffic control center and the traffic operation center are configured to provide short-item and medium-and-long-term traffic state prediction and management and planning decision and employ the calculation andinformation service platform based on cloud to collect and process traffic information to obtain an instant control command; and the vehicle unit is configured to collect data generated by the vehicles, send the data to the road side unit and receive information and a control command from the road side unit. The intelligent road facility system provides an efficient path for rapid achieving of grounding application such as unmanned driving and intelligent network connection traffic system.

This invention is related to a deep multi-sensor fusion system for inter-radar interference-free environmental perception comprising of (1) polystatic Multi-Input Multi-Output (MIMO) radars such as radio frequency radar and laser radar; (2) vehicle self-localization and navigation; (3) the Internet of Vehicles (IoV) including Vehicle-to-Vehicle communication (V2V), Vehicle-to-Infrastructure communication (V2I), other communication systems, data center / cloud; (4) passive sensors such as EOIR, and (5) deep multi-sensor fusion algorithms. The self-localization sensors and V2X formulate cooperative sensors. The polystatic MIMO radar on each vehicle utilizes both its own transmitted radar signals and ones from other vehicles to detect obstacles. The transmitted radar signals from other vehicles are not considered as interference or uselessness as conventional radars, but considered as useful signals to formulate a polystatic MIMO radar which can overcome the interference problem and improve the radar performance. This invention can be applied to all kinds of vehicles and robotics.

A ground navigation planning control method of a rover comprises the steps of sequential image splicing, three-dimensional terrain recovery, terrain traversability analysis, path planning and moving strategy generating. By means of the ground navigation planning control method of the rover, ground navigation planning control over the rover is conducted on the basis of a binocular stereoscopic vision system, a plurality of images comprising detection targets can be selected to be processed according to the positions of the detection targets of the rover with combination of practical situations, unnecessary calculation is reduced, and planning efficiency is high; an automatic noise reduction and manual denoising mode is provided so as to process three-dimensional terrain data, environmental perception accuracy is improved, and high-precision navigation planning control over the rover is facilitated; a data fusion mode is provided so as to splice multiple sets of terrain data, the environmental perception range is enlarged, and safety of navigation planning is improved; navigation planning control schemes of terrain traversability analysis, path planning and moving strategy generating provided in the ground navigation planning control method are still suitable for rovers provided with other sensors for environmental perception.

The invention provides an intelligent connected vehicle in-the-loop simulation and test and verification system and method. Simulation of the intelligent trolley operation scene is performed mainly byIPG CarMaker, CarSIM and other tools. The system is composed of the functions of Simulink data simulation calculation, transmission of the simulation scene parameters to the intelligent trolley for simulation of the real scene, intelligent trolley image acquisition and display and test analysis. The intelligent trolley side is mainly responsible for environmental perception and control algorithmverification of the intelligent connected vehicle and is composed of a trolley and a control module, a driving module, a communication module and a sensor module to act as the carrier of loading the test algorithm software and the sensor and the communication module required to be tested. According to the system, the cost and the dangerousness of the intelligent connected vehicle test can be reduced, onsite verification of the intelligent vehicle system can be performed in the real site, the research work of modeling, simulation and control and other aspects for vehicle-road coordination and other application in different simulation traffic scenes and thus a lot of data support can be provided for the research of the intelligent connected vehicle.

The invention provides an automatic guided vehicle scheduling system based on global wireless precise positioning. The system comprises a real-time positioning layer, an application layer man-machine interface, a distributive vehicle control layer and a wireless communication layer; the real-time positioning layer comprises a to-be-positioning mobile tag, a wireless ranging fixed base station, a UDP (user datagram protocol) datagram transmission module and a coordinate calculation module; the application layer man-machine interface comprises a task management and publishing module, a position monitoring and scheduling module and an environmental modeling and electronic map downloading module; the distributive vehicle control layer comprises a distributive route planning module, an electronic map receiving and storing module, a line patrol and route switching module and an environmental perception module; and the wireless communication layer comprises a wireless transceiver host module, a wireless transceiver slave module and a wireless peer-to-peer communication network. According to the system and the method provided by the invention, as the global real-time wireless accurate timing technology is introduced, the positions of all AGVs (automated guided vehicles) in the system are monitored in real time, and are fed back to a centralized main control computer, so that the computer can perform uniform scheduling on all equipment in the system.

The invention provides a deep learning-based traffic sign automatic identifying and marking method, which is used in the technical field of environmental perception of intelligent vehicles. A semantic segmentation structure is adopted for locating and detecting traffic signs, and candidate regions with the traffic signs are obtained, wherein the semantic segmentation structure comprises two parts: an encoding network; and a decoding network and a pixel-based classification layer. Then, the traffic signs in the candidate regions are subjected to classified identification and locating through a convolutional neural network of a quick region. Based on the traffic sign automatic identifying and marking method, the invention furthermore correspondingly provides an effective map navigation information updating method. According to the method, the candidate regions with the traffic signs are located by using the semantic segmentation method, so that a new idea is provided, a training parameter quantity is reduced, the storage space is saved, and the computing time is shortened; and the method is high in identifying accuracy, and can perform relatively accurate traffic sign information updating on map navigation information, thereby better serving drivers conveniently.

The invention discloses a ground-air heterogeneous multi-robot search and rescue system, which comprises air flying robots, a plurality of ground rescue robots, an image processing computer, an image acquisition card, a monitoring computer, search and rescue command personnel, a wireless data transmission radio, a wireless image transmission radio, obstacles, and objects to be rescued. The air flying robots are responsible for carrying out rapid and systematic large-area reconnaissance on the rescue site so as to more accurately judge the rescue site conditions, find out the positions of the obstacles or objects to be rescued, and help the search and rescue command personnel formulate detailed rescue plan. The ground rescue robot group, according to control instructions issued by the search and rescue command personnel, cooperatively executes the search and rescue work of the objects to be rescued at the rescue site. The robots in the system all adopt the control mode combining the autonomous intelligent control with remote control of the search and rescue command personnel; by the coordination of a plurality of ground-air robots with different structures, the system improves the reliability and accuracy of rescue site environmental perception and object recognition, reduces the search spread time and improves the rescue efficiency.

The invention discloses design and implementation of a multi-person indoor environment state monitoring system based on WiFi channel state information. According to the design and implementation provided by the invention, whether people come in and go out from a room is mainly monitored, and the number of people in the room is determined. According to the design and implementation provided by the invention, WiFi signals are applied to multi-person environmental perception for the first time, CSI (Channel State Information, channel state information) is obtained by use of the widely used WiFi signals, the CSI data are analyzed and processed, an effective denoising method is adopted to remove the environmental noise and extract the waveform, an indoor access monitoring model is established by using a hidden Markov method, a people number recognition model is established by using a neural network, and the people number recognition model is corrected by the access monitoring model, thereby further improving the people number recognition accuracy and effectively monitoring the indoor environmental state.

The invention discloses a small portable automatic gravity center control vehicle, and relates to a small highly intelligentized portable automatic gravity center control vehicle. The whole vehicle has the characteristics of small volume and light weight; and due to a folded structure, a handle can be folded into a lifting handle, the vehicle can be carried by people and parking space and the possibility that the vehicle is stolen are reduced. The running of the vehicle is controlled by detecting the gravity center shift state of a driver and the vehicle; and the vehicle is flexible to run even in a narrow space. The vehicle has a ladder chassis structure, so driving is more comfortable and safer; and the vehicle is safe, stable and comfortable to run by adopting an intelligent control mode and environmental perception technology.

The invention discloses a simultaneous localization and mapping (SLAM) method for an unmanned aerial vehicle based on mixed vision odometers and a multi-scale map, and belongs to the technical field of autonomous navigation of unmanned aerial vehicles. According to the SLAM method, an overlooking monocular camera, a foresight binocular camera and an airborne computer are carried on an unmanned aerial vehicle platform; the monocular camera is used for the visual odometer based on a direct method, and binocular camera is used for the visual odometer based on feature point method; the mixed visual odometers conduct information fusion on output of the two visual odometers to construct the local map for positioning, and the real-time posture of the unmanned aerial vehicle is obtained; then theposture is fed back to a flight control system to control the position of the unmanned aerial vehicle; and the airborne computer transmits the real-time posture and collected images to a ground station, the ground station plans the flight path in real time according to the constructed global map and sends waypoint information to the unmanned aerial vehicle, and thus autonomous flight of the unmanned aerial vehicle is achieved. Real-time posture estimation and environmental perception of the unmanned aerial vehicle under the non-GPS environment are achieved, and the intelligent level of the unmanned aerial vehicle is greatly increased.

The invention discloses a human and artificial intelligence cooperative working service system and method. The human and artificial intelligence cooperative working service system comprises a robot cluster, a customer service seat end and a server; the robot cluster comprises more than one robots; and the server associates the customer service seat end with more than one robot. The robots are deployed in the use fields of clients and provide a business consultation service for the clients. The customer service seat end is deployed on a working computer of customer service personnel; and the customer service personnel connects with the server by using the customer service seat end so as to connect with the robots. According to the human and artificial intelligence cooperative working service system and method disclosed by the invention, in the aspects of human-computer interaction, environmental perception, motion control and the like, the service is provided in a mode that the customer service personnel and the serving robots cooperatively work; the defects generated when complex problems are handled by artificial intelligence are made up by the customer service personnel; the defect of memory ability of the customer service personnel is made up by artificial intelligence; and reusability of the customer service personnel is improved so as to provide a better service and promote user experience. Meanwhile, a use amount of the customer service personnel is saved for an enterprise so as to save cost.

The invention relates to a vehicle collision warning and active control method based on vehicle communication. The method is realized through an early warning control device which is arranged on a vehicle. A front vehicle collision warning and active control method, a rear vehicle tailgate warning and active control method and an adjacent vehicle lane changing warning and active control method are comprised. According to the invention, a vehicle communication collaborative way is used to exchange the environmental perception and state data of two vehicles; safety distance and brake device can be adjusted by integrating the conditions of the vehicle and an adjacent vehicle; warning delay of the vehicle and the adjacent vehicle is reduced; and the vehicle and the adjacent vehicle can collaborate to make a proper active vehicle control decision and operation. The problems that the information of the adjacent vehicle is unavailable; safety distance and brake device cannot be adjusted; and vehicle collaborative collision warning and collaborative active safety operation cannot be carried out for the state of the adjacent vehicle in the prior art relying on a collision avoidance system of the vehicle are solved. The reliability and the accuracy of vehicle collision and active control are effectively improved.

Provided is a multi-target driving behavior evaluation analytical method based on environmental perception information. The environmental perception information inside and outside a vehicle during the vehicle travelling process is collected through a vehicle environmental perception information collection system, obtained multi-source data are fused and analyzed to obtain specific parameters under different working conditions, and driving behaviors of a driver are evaluated in aspects of driving safety, driving legality, driving efficiency and driving handiness. According to the multi-target driving behavior evaluation analytical method, by collecting and fusing the multi-source data, the driving behaviors of the driver are evaluated, and the multi-target driving behavior evaluation analytical method is applied to insurance companies for obtaining the evaluation information of different drivers to conduct insurance assessment of different grades according to the driving behaviors of the specific driver.

The invention discloses a simultaneous positioning and mixed map construction method for a dynamic parking environment. By introducing environmental semantic information for data association, a dynamic target in a feature point map for realizing positioning is removed, and meanwhile, static state semantic information such as parking spots is introduced during the positioning, so that the positioning precision in the dynamic environment is improved; a full-view stereoscopic vision platform is taken as a unique environmental perception source of the system and is combined with the advantages offull-view cameras and vision, so that the environmentally accurate dimension information is realized; meanwhile, the view is wide, and the standing time of feature points in the map is long, so that the positioning precision can be increased; and the semantic information such as the parking spots can be fused with a topologic road network structure in the environment, so that the route planning efficiency can be effectively improved, and the instantaneity of an autonomous parking system is further improved.

This invention is related to a cognitive Multi-Input Multi-Output (MIMO) radio frequency (or laser) radar with large-area synchronized multi-dimensional hopping spread spectrum and Interference-Free Windows (IFWs) for autonomous vehicles comprising of (1) analog component; (2) digital baseband component; (3) multi-dimensional hopping code generator; (4) large-area time synchronization; (5) cooperative IFW; and (6) cognitive engine. The new MIMO radar can provide interference-free environmental perception intelligently by the multi-dimensional IFWs formulated by beat-frequency hopping, beat-time hopping, discrete sequence (DS), cooperative IFW, array processing, and image denoising and fusion, etc. This invention increases the frequency efficiency greatly, and be applied to autonomous vehicles and robotics under sparse, dense, mixed autonomous and human driving, or completely autonomous driving environments.

The invention belongs to the field of intelligent robots, particularly relates to a robot motion decision-making method, system and device introducing an emotion regulation and control mechanism, andaims to solve the problems of robot decision-making speed and learning efficiency. The method comprises the following steps: generating a predicted state value of a next moment according to a currentaction variable and a state value by utilizing an environmental perception model; updating state-based on action variables, state values, immediate rewards An action value function network; obtaininga prediction track based on an environmental perception model, calculating a local optimal solution of the prediction track, carrying out differential dynamic programming, and obtaining an optimal decision based on the model; acquiring a model-free decision based on a current state and strategy as well as minimized state-motion functions and based on the state prediction error, the reward prediction error and the average reward value, generating an emotion response signal through an emotion processing computable model, and selecting a path decision according to a threshold value of the signal.The decision-making speed is gradually increased while learning efficiency is ensured.

The invention discloses a collision-prevention control method for a vehicle. The speed of the vehicle itself, the accelerated speed of the vehicle itself, the speed of the front car, the accelerated speed of the front car and the information of the spacing between the vehicle itself and the front car are obtained through the environmental perception capacity of the vehicle, and a steering / brake collision-prevention action decision-making mechanism is constructed. If the current vehicle collision risk degree meets the expression shown in the description and the vertical spacing Dadj between thevehicle itself and the front car is not smaller than the minimum vertical spacing DM-Fd between the vehicle itself and the front car, steering collision prevention is conducted; if the current vehicle collision risk degree meets the expression shown in the description or the current road condition does not meet the steering collision prevention condition, vertical brake collision prevention control is conducted. Through the combination of the steering collision prevention and the brake collision prevention control, it is ensured that the brake collision prevention action or the steering collision prevention action which is highest in safety benefit is selected when the vehicle is in danger, the collision-prevention capacity of the intelligent vehicle is greatly improved, and the road traffic safety level is improved.

The invention relates to the field of passive radar, in particular to a software radio conception based broadband cognitive passive radar system architecture which has an environmental perception capability, a broadband sampling capability, a high-performance digital signal processing capability and a dynamic receiver parameter regulation capability. The method includes: step one, establishing a signal model which comprises non-orthogonal transmission signals, fading channels and direct wave and target echo receiving signals; step two, unifying a fuzzy function, Cramer-Rao bound and detection performances to a general detection theoretical framework; step three, assessing results based on a performance bound given by an assessment system; step four, improving signal quality of a direct wave channel and a target echo channel according to own characteristics of a mobile communication irradiation source. The method is mainly applied to design and manufacture of passive radar systems.

The invention discloses a health service robot capable of achieving electroencephalogram detection. The health service robot comprises a robot body, a main control unit, a motion control unit, a binocular vision capture unit, a man-machine interaction unit, an environmental perception sensor unit, a power supplying unit and a medical treatment detection unit; the medical treatment detection unit comprises an electroencephalogram detection device independent of the robot body; the main control unit is connected with the motion control unit, the binocular vision capture unit, the man-machine interaction unit, the environmental perception sensor unit and the medical treatment detection unit through bus communication protocols and serial communication protocols; the power supplying unit is used for supplying power for the main control unit, the motion control unit, the binocular vision capture unit, the man-machine interaction unit and the environmental perception sensor unit. The health service robot is combined with the electroencephalogram detection device, a robot moving technology and a medical treatment detection technology, and therefore the movable and portable properties of the electroencephalogram detection device can be improved to a large extent.

The vehicle mounted perception sensor gathers environment perception data from a scene using first and second heterogeneous (different modality) sensors, at least one of the heterogeneous sensors is directable to a predetermined region of interest. A perception processor receives the environment percpetion data and performs object recognition to identify objects each with a computed confidence score. The processor assesses the confidence score vis-à-vis a predetermined threshold, and based on that assessment, generates an attention signal to redirect the one of the heterogeneous sensors to a region of interest identified by the other heterogeneous sensor. In this way information from one sensor primes the other sensor to increase accuracy and provide deeper knowledge about the scene and thus do a better job of object tracking in vehicular applications.

The invention relates to a traffic light lane level control relationship extraction method and device. The method comprises the steps that laser point cloud data and high definition real image data ofa road intersection in the forward direction are collected, and on the basis of a data fusion technology, two kinds of data are matched; by means of an image semantic segmentation technology, boundary contour coordinates of the traffic lights are extracted from a laser point cloud; characteristic information of traffic light images is extracted from the high definition real image data, and the types of the traffic lights are judged; the traffic lights and the corresponding lanes are matched according to the types, and the traffic light lane level control relationship is obtained. A self-driving car runs according to the lane level path, when the self-driving car passes through the intersection, position and attribute information of controlled signal lights is obtained from high definitionmap data, environmental perception and vehicle body positioning are conducted together with other types of sensors in a matched mode, and then traffic signals are interpreted in a targeted mode; theprocessing efficiency and accuracy degree of self-driving cars on the traffic signals are greatly improved.

The invention discloses a blockchain-based unmanned aerial vehicle (UAV) group decision-making method. According to the scheme of the invention, in the process of performing a task, as the ability ofa single UAV to obtain information is limited, the overall environmental perception capability is not strong; therefore, with the cooperative control of multiple UAVs, decision-making and consensus mechanisms for tasks and targets can be achieved. When a UAV node finds a suspicious target during the task, a group decision can be initiated, the remaining UAVs gather around the target, voting is performed according to collected information, and voting results and the collected information are simultaneously written into a blockchain to provide a transparent, secure and traceable group decision-making process and reach a consensus. On the other hand, according to historical information in the blockchain, the authenticity of data collected by the UAVs can be identified, and the validity of theinformation interaction within a UAV network can be ensured.

A driverless tractor control system and control method comprises: a satellite positioning module, an environmental perception module, a telecontrol interference module, a cloud platform module, a pathplanning module, a decision-making control module and a security module. The satellite positioning module, the environmental perception module, the telecontrol interference module, the cloud platformmodule and the path planning module are separately connected to the decision-making control module. The satellite positioning module, the environmental perception module, the telecontrol interferencemodule and the cloud platform module are separately connected to the security module. The satellite positioning module is connected to the path planning module. Because a multisensor of a laser radar, a millimeter wave radar and a machine vision camera is used, reliability is high, and a type and a distance of a barrier are identified more accurately and fast. There are the telecontrol interference module and the cloud platform module, so that security and usability of the driverless tractor are significantly improved. Two path planning modes of a nesting mode and a shuttling mode are developed, which are convenient and flexible, so that efficiency is improved, and a requirement of collaborative operation among multiple machines can be met.

The invention discloses an auxiliary driving obstacle detection method based on binocular stereoscopic vision, and belongs to the technical field of environmental perception. The method mainly comprises the steps of synchronously collecting two frame images output by two CCD cameras respectively, wherein the two CCD cameras are arranged right ahead of a vehicle; carrying out distortion correction on the collected images; calling a stereo matching sub-process to carry out stereo matching on the two corrected images to obtain the corresponding relations of all pixel points; respectively calculating the projection matrixes of the two CCD cameras according to camera parameters; finding all corresponding pixel point pairs in the two corrected images; carrying out scene three-dimensional reconstruction on the images collected in real time according to the projection matrixes to obtain the space coordinate of an obstacle scene, screening obstacle areas, and outputting an obstacle position information image. The method achieves the function of automatically detecting road obstacles in the static or advancing process of the vehicle, and has the advantages of being capable of achieving the unattended operation and high in detection result precision.

The invention relates to a service function chain low-cost intelligent deployment method based on environmental perception, and belongs to the technical field of mobile communication. The method comprises the following steps: S1, taking a wireless rate reachable by a user wireless terminal as a basis of SFC resource allocation, jointly considering SFC deployment and wireless subcarrier allocation,and establishing an SFC deployment cost minimization model under user delay requirements, wireless rate requirements and resource constraints; S2, converting the optimization problem into a Markov decision process with a continuous state space and a high-dimensional action space in discrete time; and S3, finally, considering the high dimensionality of the MDP problem, and obtaining a joint optimization strategy of SFC deployment and subcarrier allocation by utilizing a DDPG reinforcement algorithm, thereby achieving the purposes of reducing resource consumption and reducing SFC deployment cost. According to the method, the total cost of SFC deployment can be effectively reduced while the user delay requirement and the wireless rate requirement are ensured; and subcarrier allocation and SFC deployment strategies enabling the deployment cost to be lowest can be made in each time slot according to the current environment.

The reliable perceptual data source can be provided for environmental perception of an unmanned vehicle by three-dimensional laser radar. The invention discloses a method for calculating absolute speed of surrounding vehicles in the area of interest of the unmanned vehicle in real time based on the three-dimensional laser radar. The method comprises the steps that the massive space point cloud data of the surrounding environment of the unmanned vehicle are acquired by using the three-dimensional laser radar; a certain layer of point cloud data representing the vehicle characteristics are extracted in a way of being adaptive to the distance and projected to an xoy plane; the moving vehicles in the area of interest of the unmanned vehicle are identified by applying the cluster merging and minimum bounding rectangle method; the relative speed of the surrounding vehicles is calculated by using distance information of the first point projected to the foremost layer and the last layer of the xoy plane in relative motion between the vehicles; and the speed is synthesized with the speed of the unmanned vehicle so as to obtain the absolute speed of the surrounding vehicles. The method is simple and reliable in algorithm and high in timeliness so that the basis can be provided for route planning and tracking and lane changing and other driving strategy of the unmanned vehicle on the road.

The invention provides a dynamic compensation method for three-dimensional laser data in a moving process, and belongs to the technical field of autonomous environmental perception of a mobile robot. The method is mainly characterized in that a three-dimensional laser data acquisition manner in the moving process is improved, and a reasonable data compaction method is adopted to improve algorithm execution efficiency. Therefore, the dynamic compensation method adopts a dynamic data matching method to correct robot position and posture errors caused by a mileage recorder, and improve the accuracy of the three-dimensional laser data obtained in the moving process; on the other hand, a plane extraction method is adopted to filter out disordered laser data, so as to reduce matched laser point quantity, and ensure that the dynamic compensation method can be carried out on-line in real time. The dynamic compensation method can be used in the fields such as structured scene reconstruction of the mobile robot.

The invention discloses a system and a method for routing inspection. The system comprises a routing inspection execution subsystem for acquiring routing inspection point information, a local intelligent near-field communication device for reading the routing inspection point information and carrying out filing storage, a cloud computing server for storing the routing inspection point information and transmitting a routing inspection instruction, and remote monitoring terminals for reading the routing inspection point information stored by the local intelligent near-field communication device and issuing the routing inspection instruction. The routing inspection execution subsystem comprises two-dimensional code labels, a two-dimensional code reader, a video monitor for acquiring area video information, and an environmental perception device for acquiring area environmental parameter information. The system for routing inspection has the advantages that 1, the routing inspection execution subsystem realizes routing inspection point information acquisition so that efficiency is improved; 2, routing inspection point information is stored by the cloud computing server; and interaction of data of a remote monitoring center and a local routing inspection scene, and instruction issuing are finished in a cloud terminal so that a system integration level is improved; and 3, labels pasted on routing inspection points are the two-dimensional code labels and the two-dimensional code reader has an anti-explosion function so that the system is especially suitable for routing inspection of oil depots.