181results about How to "Accurate perception" patented technology

The invention relates to a control method and device for an intelligent agent with a body and a readable storage medium. The method comprises the steps of obtaining an instruction input by a user; performing intention recognition on the instruction to generate a current task; calling a large language model, and generating a planned path conforming to the current task based on the large language model and the knowledge graph; decomposing the current task to generate an operation instruction comprising a series of executable action sequences; calling a rule base to ensure that the generated action sequence conforms to various preset rules and operation standards included in the rule base; in the task execution process, environment data fed back by different sensors in the multi-mode perception sensor are collected in real time; and inputting the environment data into the large language model, understanding and processing the environment data by adopting the large language model, and adjusting an operation instruction or planning a path in real time according to a current task and an analysis result of the environment data. The control method provided by the invention is more intelligent, safer and highly adaptive, and can adapt to a more complex task environment.

The invention provides a distributed computing resource smart evolution method and system based on digital twinning, and relates to the technical field of resource evolution.The distributed computing resource smart evolution method comprises the steps that a smart sensing probe is deployed at a computing node to collect operation data, a double-layer attention mechanism is used for extracting features, and a digital twinning model is constructed based on a graph neural network; a resource allocation strategy and a scheduling decision are optimized in combination with a hierarchical reinforcement learning framework, a prediction sequence is generated through an attention-enhanced sequence prediction network, and finally an optimal configuration scheme is solved by adopting a particle swarm algorithm. According to the method, the utilization rate of distributed computing resources can be improved, the energy consumption of the system is reduced, and intelligent scheduling and optimal configuration of the computing resources are realized.

The invention discloses a park sweeper positioning and environment mapping method based on multi-sensor fusion. The method comprises the following steps: S1, preprocessing multi-modal data acquired by a plurality of sensors; s2, extracting and fusing semantic features and geometric features in the multi-modal data preprocessed by the plurality of sensors; s3, performing geometric feature extraction and recognition on the point cloud data by using a VoxelNet point cloud processing algorithm; s4, performing pose estimation and track plotting by fusing multi-sensor data; s5, identifying places where the motor sweeper repeatedly passes through through loopback detection, and correcting accumulated errors; and S6, constructing a three-dimensional environment map by fusing the laser radar, the visual features and the IMU data, performing global optimization by applying a map optimization technology, correcting errors, improving map precision, and updating the map in real time. According to the invention, the autonomous navigation capability of the sweeper is enhanced, the sweeping efficiency and safety are improved, and the sweeper is suitable for various complex and dynamic park environments.

The invention discloses a quantum dot enhanced dynamic power consumption management method, a quantum dot enhanced dynamic power consumption management system and related equipment. Aiming at the problem of insufficient optimization caused by difficulty in comprehensively, accurately and dynamically sensing the internal energy distribution and conversion process of a chip by a traditional method, the method comprises the following steps of: acquiring the quantum state of the chip in real time by utilizing a quantum dot sensing array, and constructing an adaptive power consumption prediction model by combining multi-source data fusion and feature extraction; and an optimal management strategy is generated based on a multi-objective optimization framework, an execution feedback and self-learning mechanism is introduced, and finally a complete closed-loop control system is formed. The method can be widely applied to various processor chips in the fields of high-performance calculation, the Internet of Things, artificial intelligence and the like, dynamic power consumption management of the chips is achieved through the quantum dot enhancement technology, the performance is guaranteed, meanwhile, energy consumption is minimized, the battery endurance time is prolonged, the heat dissipation cost is reduced, and the energy efficiency of the chips can be remarkably improved.

The invention discloses an unmanned aerial vehicle control method and system capable of keeping precision and enhancing generalization ability. The method comprises the following steps: acquiring multi-modal data acquired by various sensors carried by an unmanned aerial vehicle; based on the multi-modal data, constructing a reinforcement learning model, and generating an action strategy in a simulation environment; updating the multi-modal data in real time according to environment change, and updating the action strategy; the action strategy is migrated from the simulation environment to the real environment, the migrated action strategy is adjusted by using the evaluation data, and the unmanned aerial vehicle action sequence in the real environment is generated by using the adjusted action strategy, so that the generalization of the unmanned aerial vehicle control system can be improved while the precision of the unmanned aerial vehicle control system is maintained.

The utility model discloses a flexible dexterous hand touch sensing electronic skin based on an FPC (Flexible Printed Circuit), which comprises a palm FPC carrier plate, and a palm longitudinal electrode and a palm transverse electrode which are arranged on the palm FPC carrier plate, and the staggered points of the palm longitudinal electrode and the palm transverse electrode form a palm touch sensing capacitor unit. The finger longitudinal electrodes and the finger transverse electrodes are arranged on the finger FPC carrier plate, staggered points of the finger longitudinal electrodes and the finger transverse electrodes form a finger touch sensing capacitor unit, the electronic skin has a touch sensing function and can simulate the touch of a human body, so that the mechanical arm has more intelligent and flexible operation capability, and the mechanical arm is more flexible to operate. The external environment and object characteristics can be sensed more accurately, the operation of the manipulator can be controlled more accurately, and the operation precision and safety are improved.

The invention discloses a fusion obstacle avoidance system for a safety inspection robot, which obtains real-time mobile data in sensors arranged around the safety inspection robot, receives the real-time mobile data from each sensor, carries out fusion processing on the real-time mobile data by using a data fusion algorithm, and carries out real-time obstacle avoidance on the basis of mobile environment information. Constructing a mobile map by using an ORB-SLAM2 algorithm, and determining real-time position information of the robot in the map; the real-time position information is received, a Dijkstra path planning algorithm is used for carrying out inspection path planning on the real-time position information, surrounding moving obstacle information is monitored in real time, when an obstacle is detected, an initial planning path and the real-time position information are combined to calculate an obstacle avoidance path, and the accuracy of obstacle detection and recognition is improved. Stable and reliable positioning information can be provided in different environments, and it is ensured that the robot accurately knows the position of the robot.

The invention belongs to the technical field of the Internet of Things, and discloses a situation awareness method and system based on the Internet of Things, and the method comprises the steps: collecting environment awareness data, and extracting environment features and mineral features from the environment awareness data; storing the environment features and the mineral features in a block chain, and respectively judging whether the environment features and the mineral features meet preset standards or not; respectively predicting the environment situation and the mineral situation based on the judgment results of the environment characteristics and the mineral characteristics; and comprehensively evaluating the ore exploitability through a pre-established decision cluster on the basis of the prediction results of the environment situation and the mineral product situation. According to the system and the method, through a mechanism of sensing surrounding environment parameters and extracting specified mineral signs and other attributes, the environment parameters and the mineral signs can be accurately sensed and collected, and powerful data support is provided for accurate monitoring and resource management.

The invention provides an intelligent blind guiding method and device, electronic equipment and a storage medium, and relates to the computer technology, in particular to the technical field of intelligent blind guiding. According to the specific implementation scheme, the method comprises the steps of obtaining multi-modal sensing information; the multi-mode sensing information comprises images, videos, audios and pose information; preprocessing and fusing the multi-modal sensing information to obtain fused information; identifying the fusion information to obtain an identification result; the identification result comprises at least one of the following information: user surrounding environment information and user motion state information; and generating feedback information based on the identification result.

The invention belongs to the technical field of building robots, and particularly discloses a self-propelled reinforcing steel bar binding robot moving chassis and method, the moving chassis comprises a cross-layer inverted-T-shaped rack assembly, an environment sensing and automatic control assembly is arranged at the top end of the cross-layer inverted-T-shaped rack assembly, and a step-by-step lifting transverse propelling assembly is arranged at the bottom end of the cross-layer inverted-T-shaped rack assembly; self-adaptive high-position longitudinal moving assemblies connected with the cross-layer inverted-T-shaped rack assembly are arranged on the two sides of the environment sensing and automatic control assembly at intervals. Two pairs of driven double-hub wheel assemblies connected with the cross-layer inverted-T-shaped rack assembly are arranged on the two sides of the step-by-step lifting transverse propelling assembly in a clearance mode. The mobile chassis of the self-propelled reinforcing steel bar binding robot can automatically and stably walk on complex reinforcing steel bar mesh surfaces with different sizes and specifications, so that the construction efficiency of reinforcing steel bar binding operation is greatly improved, and the construction period is remarkably shortened.

The invention provides a multi-modal fusion sensing robot interaction system, and relates to the technical field of multi-modal fusion sensing, and the system comprises an information sensing unit activation module; a target area scanning module; the target position information determining module is used for determining target position information of the to-be-fetched article according to the regional image and the regional point cloud set; the article grabbing module is used for generating an article grabbing path based on the target position information to grab articles; and the article carrying module is used for collecting weight information of the grabbed articles through a pressure sensor, and if the weight information is within a preset weight interval, the grabbed articles are carried to a preset storage area. The technical problems that a traditional carrying robot usually depends on a single sensor for detection, accurate recognition and positioning cannot be effectively achieved in a complex environment, and the object grabbing precision and reliability are insufficient can be solved, more comprehensive and accurate object sensing and grabbing can be achieved, and the object grabbing efficiency is improved. And the article grabbing accuracy and reliability are remarkably improved.

The invention relates to an emergency response and repair method for an underwater pipeline route. The emergency response and repair method comprises the following steps that S1, firstly, a detection device is placed into a pipeline in the flowing direction of a fluid medium; s2, starting a measuring instrument of the head mechanism, carrying out data fusion, and transmitting the data to a control center of the head mechanism in real time; s3, opening a fan plate, and then starting propellers on the head mechanism and the propelling mechanism: the two mechanisms work together to realize the swimming and steering movement of the detection device in the pipeline, and detecting the condition in the pipeline by using a sensor; s4, when the head mechanism detects that the flow velocity in the pipeline changes suddenly, taking back the fan plate, closing the spiral propeller and the axial spiral propeller, and starting the descending spiral propeller to enable the detection device to be attached to the inner wall of the pipeline; s5, the crawling mechanism drives the detection device to creep; s6, when the flow velocity trend is changed from rising to falling, the device is fixed to the inner wall of the pipeline; s7, the repairing mechanism works; and S8, starting the monitoring ring. The pipeline can be detected and repaired autonomously, efficiently and automatically.

The invention relates to a traction type pipeline emergency repair mobile platform which comprises a head mechanism, a propelling mechanism, a crawling mechanism and a repairing mechanism which are sequentially connected through universal couplings and connecting pipes. The head mechanism is in a bionic snake head shape, and a main body part is a head cabin with a detection function; the propelling mechanism integrally comprises a propelling cabin and a spiral propeller; the crawling mechanism is in a bionic caterpillar shape and comprises a crawling front cabin, a connecting hose and a crawling rear cabin, the crawling front cabin, the connecting hose and the crawling rear cabin wrap an internally communicated space structure, and a crawling frame is arranged in the space structure; the front crawling cabin and the rear crawling cabin are provided with self-control adsorbers; one end of a breakable cable box is connected with a connecting disc, the other end of the breakable cable box is connected with the front end of a front fitting block, an electromagnet is arranged at the rear end of the front fitting block and connected to a telescopic cable box, the other end of the telescopic cable box is connected with a rear fitting block, an electromagnet is arranged at the front end of the rear fitting block, an umbrella stand structure is arranged in the rear fitting block, and the rear end of the rear fitting block is connected with a monitoring ring. The pipeline can be automatically detected and repaired.

The invention discloses a construction method, system and equipment of a high-precision map and a medium. The construction method comprises the following steps: S1, generating a bird's-eye view feature map based on a static map with a standard resolution and sensor data; s2, further extracting a static feature Fsd and a dynamic environment feature Ft based on the bird's-eye view feature map; s3, based on a cross attention mechanism, fusing the static feature Fsd and the dynamic feature Ft; and S4, decoding the fused features into a high-precision map through a decoder. According to the invention, when the environment structure changes, the road information of the current frame can be reasoned by combining the high-definition map information of the previous frame with the standard map information, and the depth feature extracted by the convolutional neural network and the original standard map information are combined. The system can more accurately perceive and understand the environment around the vehicle, so that the overall performance and decision-making efficiency of the automatic driving system are improved.

The invention relates to the technical field of sensing data fusion, in particular to a multi-sensor data fusion method for an unmanned pre-controller. The method comprises the following steps: acquiring environmental obstacle position data, environmental object distance data and environmental object motion state data through unmanned driving environment multiple sensors, performing time sequence synchronization and standardization processing, and performing feature space vector conversion at the same time; carrying out multi-sensor particle filtering fusion analysis and multi-sensor uncertainty inference adjustment analysis to obtain an environment multi-sensor uncertainty adjustment coefficient; and performing multi-sensor uncertainty fusion optimization and environment space modeling processing according to the environment multi-sensor uncertainty adjustment coefficient, performing environment state driving control prediction analysis, and generating an unmanned driving environment state change driving control strategy so as to execute a corresponding unmanned driving environment change pre-control function. According to the invention, rapid fusion analysis and utilization of unmanned multi-sensor data can be realized.

The invention discloses a power distribution network topology identification method based on ARAM graph convolution, and the method comprises the steps: 1, taking lines in a power distribution network as nodes, taking the lines with power connection between the lines as edges, and abstracting the power distribution network as a line graph structure; 2, generating various types of topological structures according to the basic topological structure of the power distribution network system, and establishing a data set by taking electrical quantities such as voltage difference of a power distribution network line as node characteristics of a line graph and taking an on-off state vector of the line as a label; 3, constructing an ARMA-based graph convolutional network model for power distribution network topology identification; 4, training an ARMA-based graph convolutional network model and storing the trained model; and 5, collecting real-time measurement data of the power distribution network, and inputting the normalized data into the trained optimal ARMA graph convolutional network model to carry out real-time identification on the topology of the power distribution network. According to the method, accurate and real-time topology identification can be realized in a large-scale power distribution network with uncertain scenes such as distributed renewable power supply fluctuation.

The invention discloses a dynamic obstacle avoidance method and system for a substation inspection robot. The position of the robot is positioned based on laser point cloud data, and static and dynamic obstacle features are extracted at the same time. An environment association graph is constructed through perception information in the advancing process, and feature information of each node serves as an attribute of the environment association graph; calculating the distance, the relative speed and the relative angle between the edge weights of the nodes according to the nodes; on the basis of the environment association graph, aggregating all-graph obstacle node information to generate real-time feature representation for robot nodes, and correcting a calculation mode of attention weight according to edge weight between the nodes; and finally outputting a real-time feature vector of the robot node. And defining a reinforcement learning framework and executing path planning. According to the invention, through combination of GAT and dynamic planning, efficient obstacle avoidance and path planning of the robot in a substation environment are realized, and the decision and execution capability of the robot in a dynamic complex scene can be improved.

The invention discloses an energy station-oriented multi-source heterogeneous data fusion twinborn model construction method, which belongs to the technical field of model construction and comprises the following steps of S1, acquiring static physical space parameterized model data of an energy station; s2, acquiring non-parametric model data of the dynamic energy flow of the energy station; s3, extracting the characteristics of the static physical space parameterized model data of the energy station and the dynamic energy flow non-parameterized model data of the energy station through a multi-dimensional scale transformation method; s4, multi-source heterogeneous data fusion expression; s5, constructing a digital twinborn fusion model, training the digital twinborn fusion model through the data of the multi-source heterogeneous data fusion expression until convergence, and obtaining the digital twinborn fusion model; according to the method, multi-source heterogeneous data can be effectively fused to accurately perceive and analyze the operation situation of the energy station, so that the geometrical characteristics, physical characteristics and energy operation rules of the energy station can be comprehensively described to support mapping and interaction between a virtual space and a real space.

The invention relates to a wearable flexible vibration sensing sensor for a humanoid robot and a preparation method of the wearable flexible vibration sensing sensor. The sensor structure comprises an upper packaging layer, a lower packaging layer, a moisture absorption layer, a hydrogel layer, a graphene layer, an electrode layer and a flexible circuit board. Wherein the upper packaging layer and the lower packaging layer protect the internal structure, the moisture absorption layer enhances the moisture absorption capacity in the environment, the hydrogel layer maintains moisture and forms a concentration gradient, the graphene layer enhances the sensitivity of the sensor, the electrode layer collects ion migration to generate voltage, and a signal amplification circuit and a wireless transmission module are embedded in the flexible circuit board. The method is characterized in that a vibration signal is converted into voltage change through ion migration between the hydrogel layer and the graphene layer, and accurate vibration sensing is achieved. The sensor has high sensitivity, quick response and good flexibility, is suitable for being integrated into the humanoid robot, and improves the environmental perception and interaction performance of the humanoid robot.

The invention provides an ancient building adaptive composite fire detection alarm model construction method, and relates to the field of fire detection, and the method comprises the following steps: firstly, carrying out on-site survey to obtain an ancient building structure and internal decoration combustibles and layout, and employing a cone calorimeter to measure the combustion performance parameters of building materials and decorations; then, FDS software is adopted for conducting three-dimensional modeling and computational grid division, boundary and condition setting, fire point and fire source power setting, detection point position setting and the like on the internal space and display of the historic building, and data sets of CO concentration, CO2 concentration, O2 concentration, smoke shading rate and temperature parameter changes in the fire occurrence and development process are obtained through numerical simulation calculation; and finally, learning and training the initial multi-characteristic parameter data of the fire through a BP neural network, continuously correcting and optimizing in application, and constructing a self-adaptive fire alarm model, thereby more accurately and comprehensively sensing the fire of the ancient building, and realizing accurate early warning of the early fire.

The invention relates to the technical field of off-grid photovoltaic disturbance rejection, in particular to a method for enhancing off-grid photovoltaic disturbance rejection capability based on an intelligent filtering technology. Firstly, known disturbances such as illumination, temperature and load abrupt change in the system are analyzed, and a model is established. Designing an intelligent filter, monitoring and estimating unknown disturbance by using a self-adaptive algorithm, obtaining direct current and harmonic disturbance observation values, and fusing the direct current and harmonic disturbance observation values; and then an active disturbance rejection controller based on intelligent filtering is constructed, unknown disturbance and known disturbance are added to obtain a concentrated disturbance observation value, and parameters are designed to respond to suppression disturbance. And finally, obtaining a voltage preliminary control instruction, and combining with a filter observation value construction instruction to suppress current harmonic disturbance. Through the steps, the stability and reliability of the off-grid photovoltaic system are improved.

The invention discloses a power distribution network bad data identification state estimation method based on digital twinning, and relates to the technical field of power system state estimation, and the method comprises the steps: collecting real-time measurement data based on a physical power distribution network, and transmitting the real-time measurement data to a digital twinning server through a communication protocol; identifying and correcting bad measurement data in a digital twin database of the server, and complementing the removed measurement data through a BILSTM neural network; and fusing the multi-source measurement data in the aspects of data volume and time scale, and performing linear state estimation to reflect the real-time state of the power distribution network. According to the method, bad data are identified and corrected through the BILSTM neural network, and the data integrity is ensured; by adopting the linear state estimation method based on multi-source data fusion, the problem of time scale difference is solved, accurate and real-time power distribution network perception is realized, and better effects are achieved in the aspects of bad data identification and correction of the power distribution network and the precision and efficiency of state estimation.

The invention discloses an underwater image quality enhancement method based on coupling degradation processing. The method comprises the following steps: acquiring an underwater image data set; constructing a combined degradation processing model, and inputting the underwater image data set into the combined degradation processing model for training to obtain a trained combined degradation processing model; and obtaining a to-be-detected underwater image, and inputting the to-be-detected underwater image into the trained joint degradation processing model for underwater image enhancement to obtain a target underwater quality enhanced image. According to the method, the problem that the image quality cannot be effectively recovered when a traditional image processing method faces a complex coupling degradation problem is solved, efficient and accurate perception of the underwater robot to the environment is achieved, and therefore the operation safety of the underwater robot is improved.

The invention provides an unmanned aerial vehicle battery health monitoring method and system based on an intelligent charging management cabinet, and relates to the technical field of battery monitoring, and the method comprises the steps: arranging a magnetic flux sensing detection unit in a charging cabin of the intelligent charging management cabinet, and recognizing a current density distortion region in an unmanned aerial vehicle battery; monitoring the stress distribution state of the battery shell of the unmanned aerial vehicle in real time, and calculating the internal damage degree of the battery of the unmanned aerial vehicle according to a stress-current coupling model; when the internal damage degree exceeds a preset threshold value, an active balance repairing mode of the intelligent charging management cabinet is triggered, uniform deposition of active substances in the unmanned aerial vehicle battery is promoted by generating mechanical vibration in the charging bin, and current in the unmanned aerial vehicle battery is guided to be redistributed through an alternating magnetic field generated by the three-axis magnetic induction coil; and dynamic repair and balance of the battery performance of the unmanned aerial vehicle are realized.

The invention discloses a landslide rock-soil body internal stress monitoring device, and relates to the technical field of rock-soil body internal stress monitoring, the landslide rock-soil body internal stress monitoring device comprises a stress monitoring instrument, the outer side of the stress monitoring instrument is fixedly connected with a monitoring sensor, and the interior of the stress monitoring instrument is slidably connected with a moving shaft; an auxiliary assembly used for reducing the damage risk of the stress monitoring instrument is arranged outside the moving shaft, a soil loosening assembly used for enabling the stress monitoring instrument to be taken out and maintained easily is arranged outside the stress monitoring instrument, and the moving shaft moves downwards to drive triangular spines to be inserted into soil downwards. The triangular spine is of a triangular structure formed by combining three spines, the stability of the stress monitoring instrument in soil can be enhanced, the stress monitoring instrument can be firmly fixed in a rock-soil body, the possibility that the stress monitoring instrument moves when rock-soil landslide occurs is reduced, the triangular spine can be in better contact with the rock-soil body after being inserted into the soil, and the stress monitoring instrument is more stable. Therefore, the internal stress of the rock-soil body can be monitored more effectively.

The invention discloses a driving area visualization method, device and equipment for automatic parking and a storage medium, and relates to the technical field of parking, and the driving area visualization method for automatic parking comprises the following steps: obtaining vehicle-mounted camera data, radar point cloud data, vehicle size and safety distance, a target obstacle recognition model based on an encoder-decoder and a self-attention module is used for analyzing camera data, and the initial position, size and type of an obstacle are determined. Then, the positions are adjusted through radar data to obtain accurate real-time obstacle positions, and a drivable area is constructed in combination with the vehicle size and the safe distance. And finally, the system sends camera data, radar data and drivable area information to a vehicle controller, the vehicle controller reconstructs an automatic parking scene by using a scene restoration model, and a result is transmitted to a vehicle display to present an automatic parking view. According to the invention, the automatic parking scene view can be displayed, the driving area can be displayed, and the collision risk is reduced.

The invention discloses a path planning method and system based on multi-modal joint integration, and the method employs an image processing and deep learning technology to automatically recognize and eliminate the influence of a dynamic object, and guarantees that SLAM and VIO systems are only matched with a stable static background object. According to the technical scheme, firstly, feature points in an input image are preliminarily screened and matched through the image feature point matching technology; and then introducing a deep learning network to carry out refined segmentation and instance identification. The deep learning network can accurately distinguish a static object from a dynamic object, and accurately identify the existence of the dynamic object in a complex scene. After the dynamic objects are identified, the dynamic objects are eliminated from the feature matching process by adopting an intelligent elimination strategy, so that the SLAM and VIO systems are ensured to perform feature matching and positioning calculation only based on stable static background objects, the negative influence of the dynamic objects on the positioning precision is avoided, and the stability and applicability of the system in a complex dynamic environment are improved.

The invention provides an unmanned aerial vehicle real-time path planning system based on dynamic environment perception, and the system comprises the steps: firstly carrying out environment perception and data acquisition: employing a sensor to obtain the data of a surrounding environment in real time, carrying out the edge calculation and cloud data processing, and guaranteeing the real-time performance and accuracy; and then path planning and decision support are carried out: path planning is carried out by combining machine learning and an optimization method based on environment perception data, and an optimal flight path is rapidly and dynamically generated. And finally, performing real-time execution and control: after the path planning is completed, enabling the unmanned aerial vehicle to accurately fly according to a preset path through an autonomous navigation and control system, and performing real-time adjustment when needed. According to the invention, the advanced sensing technology and the real-time data processing capability are utilized, dynamic flight path planning and optimization are provided for the unmanned aerial vehicle in a changeable and unpredictable environment, and the safety and efficiency of task execution are ensured.

The invention discloses an intelligent network connection vehicle cloud cooperation automatic valet parking system and a control method, and the system comprises a cloud system which is used for completing the scheduling and management; the vehicle end system comprises a data fusion processing unit and a planning control unit, and the data fusion processing unit is used for carrying out fusion processing on various data acquired by the vehicle and providing the data to the planning control unit; the planning control unit is used for planning a parking path and controlling the vehicle to execute a parking action according to the data obtained through fusion processing; and the field end system comprises a field end visual perception unit, a laser radar and an MEC unit, the field end visual perception unit, the laser radar and other sensors are used for perceiving the surrounding environment of the vehicle, the MEC unit is used for processing real-time data, the data perceived by the field end are processed and analyzed in real time through the MEC unit, and a decision result is generated. The control method is implemented based on the system. The method has the advantages of being simple in principle, easy and convenient to operate, low in cost, high in intelligent degree, wide in application range and the like.

The invention relates to the technical field of intelligent navigation, and discloses an intelligent navigation system based on image recognition, comprising: an image data acquisition module for acquiring an image of an environment, the image data acquisition module comprising a binocular camera for generating a depth map, and processing the acquired image data through a data preprocessing module; and the data preprocessing module is connected with the image data acquisition module, and the data preprocessing module comprises a denoising unit, a contrast enhancement unit and an HDR processing unit. A multi-modal data fusion technology is adopted, images, laser radar and inertial measurement unit data are integrated, high-precision fusion of the data is realized in combination with an extended Kalman filter, the accuracy and stability of environmental perception are enhanced, the limitation of a single sensor in a complex environment is overcome, and the accuracy and stability of environmental perception are improved. The system can accurately sense the surrounding environment under the conditions of severe weather, illumination variation and the like, and the overall reliability and adaptability of the system are remarkably improved.