166 results about "Safe path" patented technology

The invention relates to a dynamic planning method, device and equipment for a low-altitude route of an unmanned aerial vehicle and a medium. The method comprises the following steps: acquiring real-time dynamic environment change data and obstacle distribution information of a low-altitude flight area, constructing a preliminary data set, and generating an initial flight path; according to the initial flight path, predicting energy consumption data, and if the energy consumption data exceeds a threshold value, performing optimization to obtain an optimized path; performing risk assessment quantification, combining a safety margin adjustment mechanism to obtain a safety index, and if the safety index is lower than a threshold value, performing safety distance adjustment to generate a safety path; monitoring flight conditions, generating a condition monitoring update report, acquiring new obstacle position information, and generating a dynamic obstacle avoidance path; and generating an execution instruction set of the unmanned aerial vehicle according to the dynamic obstacle avoidance path. By adopting the method, the dynamic environment real-time adaptability of the unmanned aerial vehicle can be improved, and the route meeting the task requirement can be planned in time when the unmanned aerial vehicle faces sudden obstacles or environment changes.

The invention relates to the technical field of mechanical arm obstacle avoidance path planning, in particular to a multi-degree-of-freedom mechanical arm obstacle avoidance path planning method based on three-dimensional reconstruction, which comprises the following steps: step 1, three-dimensional environment perception and dynamic modeling; preferentially offsetting and expanding the near-obstacle nodes towards the concave area or the hole center to generate a candidate node set; and 4, three-dimensional grid collision verification and safe path correction are conducted, specifically, the working space of the mechanical arm is divided into three-dimensional voxel grids, and collision detection is achieved by judging whether path nodes fall into obstacle object elements or not. According to the method, the laser radar and the depth camera are adopted to synchronously collect data through hardware triggering, statistical filtering denoising and three-dimensional grid modeling are combined, geometrical characteristics of static obstacles and motion parameters of dynamic obstacles are restored, and the collision risk caused by environmental perception errors of the mechanical arm is effectively avoided.

The invention relates to the technical field of machine vision and perception, in particular to a ton bag lifting unmanned control system based on a binocular vision camera and a laser radar, which comprises an intelligent control unit, a lifting appliance executing mechanism, a sensing unit and a special ton bag, the sensing unit comprises a binocular vision camera and a laser radar and is used for collecting depth vision and three-dimensional point cloud information of an operation area; the intelligent control unit fuses multi-source data, locates a lifting lug by improving a weighted multi-feature fusion algorithm, plans a safety path and generates a staged instruction; the lifting appliance executing mechanism lifts and pulls a collapsed lifting lug through an electromagnetic adsorption module, a mechanical gripper module clamps the lifting lug, and reliable operation is achieved in cooperation with a verification mechanism; the special ton bag is matched with a sensing and executing module through a high-contrast color and a pre-embedded metal piece. The full-process unmanned operation is achieved, the robustness and operation safety of the complex environment are improved, and the ton bag hoisting requirements of multiple industries are met.

The invention discloses an automatic driving vehicle environment sensing system based on deep learning, relates to the technical field of artificial intelligence, and solves the problem that it is difficult to identify a driving scene and a road type where a current vehicle is located. The scene migration probability in the future 3 seconds is difficult to predict, and whether the driving state of the vehicle on the predicted driving scene migration path is safe or not is predicted; obstacles and lane lines in the road type where the current vehicle is located are difficult to analyze; a movement track of an obstacle is difficult to predict and a driving path is planned; and the risk coefficient of the current vehicle is difficult to comprehensively evaluate. According to the invention, real-time identification of a driving scene and migration prediction in the next three seconds are realized through a deep learning model, a safe path is generated by identifying and positioning an obstacle and a track and applying a path optimization algorithm, and a risk coefficient of a current vehicle is comprehensively evaluated.

The invention discloses a safety-enhanced trajectory planning method based on an Astar algorithm, and belongs to the technical field of intelligent robot navigation and path planning, and the method comprises the following steps: 1, constructing a grid map, and marking an obstacle region; 2, establishing a security evaluation mechanism, evaluating the surrounding environment of the path node, and quantifying a security index; 3, in a node expansion process, combining a heuristic item and a security index in a cost function to realize priority search for a security region during path selection; 4, key nodes of the obtained initial safety path are extracted based on a Douglas-Peucker algorithm to generate a rectangular safety corridor, and smoothing processing of the initial path is achieved based on a segmented Bezier curve. On the basis of guaranteeing path optimality, safety is used as an auxiliary guiding basis in the path searching process, and the path searching efficiency is improved. Therefore, consideration of the path length and the environmental risk is realized, and the navigation path with higher execution safety is generated.

The invention discloses an end-side safe path planning method based on a small language model, which comprises the following steps: generating a task prompt by fusing the small language model SLM with an A * algorithm and a structured map token, storing the task prompt in a vector database, retrieving historical map information similar to the current task from the database when the path planning task is executed, and performing path planning according to the historical map information. The method comprises the following steps: constructing a COT logic chain prompt, guiding a small language model to gradually generate path key nodes and weights thereof in combination with the constructed COT logic chain prompt, then executing greedy path search by an A * algorithm by taking the key nodes as local targets, and finally generating a safe and efficient path. The method provided by the invention has end-side deployment capability, can be operated in scenes without network, resource limitation and the like, greatly reduces node traversal and improves planning efficiency compared with the existing A * algorithm, is particularly suitable for path planning tasks in a large-scale or high-density obstacle environment, gives consideration to intelligent reasoning and planning safety, and has wide application prospects. The method has good interpretability and expansibility, and has a wide application prospect.

The invention relates to an unmanned aerial vehicle cluster safety path planning reinforcement learning method and device. The method comprises the following steps: inputting unmanned aerial vehicle group states, task targets and environment observation information into a deep reinforcement learning strategy of a continuous action space, and generating a global guidance quantity; an explicit security constraint set between the unmanned aerial vehicles and between the unmanned aerial vehicles and obstacles is constructed, through graph structure modeling and a GNN adaptive mechanism, the unmanned aerial vehicles / obstacles are represented by dynamic graphs, and neighborhood and relative position speed information is utilized to predict CBF parameters; and performing safety projection correction on the guide quantity based on the constraint and the CBF parameter to obtain an actual control instruction corresponding to the safety path. After the instruction is executed, feedback information such as state deviation and constraint tensity is collected and used for adjusting a reinforcement learning strategy, loop iteration is carried out to continuously output an optimization path, and whole-course collision-free planning of the cluster is achieved. By adopting the method, stable and reliable safety protection in a complex dynamic environment can be realized.

The invention relates to the technical field of automatic driving and artificial intelligence, in particular to a driving track planning method based on a visual language model and an intelligent driving system. The trajectory planning method comprises the steps of multi-modal data acquisition and preprocessing, visual language model reasoning, execution control and the like. The intelligent driving system comprises a multi-modal data acquisition and preprocessing module, a visual language model reasoning module and an execution control module. The visual language model reasoning module is integrated with a Patch selection module, a token compression module and a speculation decoding module; according to the invention, an end-to-end driving planning reasoning framework is constructed based on the visual language model, the vehicle trajectory planning is directly generated from the original multi-view perception data and the high-level driving intention by a single model, the architecture of an automatic driving planning system is simplified, and the response speed and robustness are improved. The method can effectively meet the requirement of the automatic driving vehicle for generating the safe path in real time in a vehicle-mounted embedded environment, and has important practical value.

The invention relates to the technical field of unmanned aerial vehicle path planning, in particular to an unmanned aerial vehicle path planning method and system based on laser scanning. The method comprises the following steps: scanning a roadway environment in real time by using a laser scanner to obtain original radial section point cloud data; analyzing a structural stability index and a dangerous rock potential energy threat value based on the original radial section point cloud data to determine a risk attribution area of the current flight path of the unmanned aerial vehicle; judging whether a high-risk detour area exists or not according to the risk attribution area, and if yes, dynamically planning a risk detour path so as to adjust a single pre-scanning voyage and generate an intelligent safety execution route; and instructing the unmanned aerial vehicle to fly to the terminal point along the intelligent safety execution route, and taking the terminal point as the starting point of the next round of path planning until the iterative exploration of the target underground mine space is completed. According to the method, the structural stability and potential energy threat of the surrounding rock are quantified in real time to realize safe path planning for actively avoiding surrounding rock instability.

The invention discloses an intelligent robot navigation system with a body based on a multi-modal large model, and the system comprises an RGB-D camera module which captures a color image and a depth image of an environment in real time, and carries out the preprocessing of the color image and the depth image, and outputs the preprocessed image; the laser radar SLAM module provides a basis for subsequent coordinate calculation and navigation path planning; the multi-modal VLM module is used for calculating the three-dimensional coordinates of the target location in the map; and the navigation control module controls the robot to move. The beneficial effects of the invention lie in that the system realizes efficient environmental perception and natural language understanding through multi-modal fusion, and can accurately identify a target location, calculate a three-dimensional coordinate and plan a safe path, thereby improving the autonomous navigation precision and reliability of the robot, and being suitable for intelligent movement control in a complex scene.

Data is the “fuel” that powers the machine learning “engine” for Artificial Intelligence. However, identifying high quality data that can catalyze smarter AI, AGI, and SuperIntelligent systems is becoming an increasingly challenging bottleneck for machine learning. This invention not only describes novel methods for identifying the most valuable data, but it also presents an entirely new framework for understanding the information content of AI-relevant datasets. The methods can be used by intelligent systems autonomously or in collaboration with humans. Novel methods for accelerating AI learning, and for updating the knowledge of AI systems in real-time, are also disclosed. Consistent with the view that human survival may depend on the fastest path to AGI also being the safest path, the invention describes catalysts which help maximize alignment between the values of AGI and humans. These innovative catalysts increase not only the intelligence, but also the safety, of AI systems.

The invention relates to the technical field of scheme design of an unmanned aerial vehicle automatic fly-around no-fly zone, in particular to a method and system for an unmanned aerial vehicle to automatically fly around a no-fly zone. The method comprises the following steps: identifying a mountainous area environment based on topographic features (roughness, elevation standard deviation and a continuous climbing section), and constructing a safe path skeleton; generating an initial path by adopting an A * algorithm fused with an energy consumption factor, and optimizing a climbing rate through a dynamic gradient control model (in combination with a real-time pitch angle, a roll angle and a terrain gradient); a Bezier curve is used for achieving track smoothing; and responding to the no-fly zone change based on the local re-planning strategy. By means of terrain feature recognition and dynamic constraint cooperative control, mountain area low-climbing-rate path optimization is achieved. According to the method, the problems of insufficient path physical feasibility, dynamic environment response lag and the like under complex terrains are solved, and the safety and energy efficiency of the unmanned aerial vehicle flying in a no-fly zone and a mountainous area are remarkably improved.

According to the motion planning method and device for the multi-mechanical-arm cooperative task, a large number of potential conflicts are actively avoided in the early stage of task allocation through division of a virtual wall and a safety area and a danger area, the difficulty and the calculated amount of follow-up track collaboration are remarkably reduced, and the success rate and the efficiency of planning are improved. Cost and load balance are considered in the task allocation stage; in the path point sorting stage, minimizing the total movement distance and the total completion time is taken as a target; and track generation adopts a time optimal algorithm. The layered decoupling design avoids the huge calculation overhead of centralized planning, and meanwhile, the quality of a final solution is ensured through the optimization strategy of each stage. The innovative cost function takes collision distance into consideration, and guides the planner to select a safer path. Through layered and ordered collision solution strategies, such as deceleration, local re-planning and optimal waiting, it is ensured that a collision-free solution can be always found in a complex dynamic environment, and robustness is high.

The invention discloses a multi-degree-of-freedom mechanical arm obstacle avoidance path planning method based on industrial vision, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: collecting and preprocessing an initial environment image of a current working scene of a mechanical arm, obtaining a standardized working scene image data set, carrying out dynamic object detection, and obtaining an environment perception evaluation report; identifying an unobserved area of the working scene based on the environment perception evaluation report, performing collision path calculation, and generating a visual angle adjustment action instruction; the safety path sequence is issued to a mechanical arm joint and executed, images in front of the mechanical arm are continuously collected during execution, and if an unpredicted sudden obstacle is detected, the four-dimensional risk map is dynamically updated, and path re-planning is conducted; and when the tail end of the mechanical arm successfully completes the task, obstacle avoidance path planning data of the mechanical arm are recorded and stored persistently, and an obstacle avoidance path planning record is generated. According to the method, the real-time problem of path planning of the mechanical arm is solved through construction of the four-dimensional dynamic risk map.

The invention discloses a breast surgery puncture positioning system, which relates to the technical field of image positioning, and is characterized in that a preoperative medical image and an intraoperative ultrasonic image of a patient are acquired to generate a three-dimensional model, an optimal puncture path is planned by receiving a needle insertion point of a user, and the spatial pose of a puncture needle is tracked; the optimal puncture path and the position and posture of the puncture needle are displayed on the real-time ultrasonic image in an overlapped mode, real-time deviation is calculated, the puncture needle is guided and adjusted to the target focus, and real-time compensation is carried out based on the respiratory signal and / or the displacement of the reference datum. Through the multi-modal image fusion and real-time dynamic tracking technology, the puncture precision and hit rate are remarkably improved, meanwhile, the system intelligently plans a safe path and provides an AR navigation interface, the operation difficulty and dependence on doctor experience are greatly reduced, physiological displacement interference is overcome through respiratory gating and a motion compensation mechanism, and the system has the advantages of being high in accuracy and high in reliability. Accuracy and reliability in the whole operation process are guaranteed, and finally standardized and safe mammary gland puncture diagnosis and treatment operation is achieved.

The invention relates to the technical field of mobile robot autonomous navigation path planning, and discloses a complex orchard scene-oriented hybrid sampling global path planning method, which comprises the steps of adopting a Gaussian hybrid sampling strategy, switching between global uniform sampling and Gaussian offset sampling through a probability threshold, and considering global exploration and key area guidance; a dynamic node expansion strategy is adopted, the expansion step length is adaptively adjusted according to the distance between the expansion step length and a target point, and the expansion direction is optimized by fusing vectors facing a sampling point and the target point and introducing an obstacle influence factor; a layered collision detection strategy is adopted, and step-by-step filtering is carried out through rough detection based on a quadtree, secondary screening based on an axis alignment bounding box and accurate geometric judgment in sequence so as to improve the efficiency; and a dynamic iteration termination mechanism is adopted, and the minimum number of iterations and the path quality convergence state are combined as composite termination conditions. The method can quickly generate a smooth and safe path, and is especially suitable for structured scenes such as orchards.

A deep neural network(s) (DNN) may be used to perform panoptic segmentation by performing pixel-level class and instance segmentation of a scene using a single pass of the DNN. Generally, one or more images and / or other sensor data may be stitched together, stacked, and / or combined, and fed into a DNN that includes a common trunk and several heads that predict different outputs. The DNN may include a class confidence head that predicts a confidence map representing pixels that belong to particular classes, an instance regression head that predicts object instance data for detected objects, an instance clustering head that predicts a confidence map of pixels that belong to particular instances, and / or a depth head that predicts range values. These outputs may be decoded to identify bounding shapes, class labels, instance labels, and / or range values for detected objects, and used to enable safe path planning and control of an autonomous vehicle.

The invention belongs to the field of navigation of an unmanned vehicle in a field unstructured scene, and particularly relates to an unmanned vehicle field scene passable area segmentation and navigation method based on air-ground cooperation, and the method comprises the steps: respectively generating a global point cloud map and a local point cloud map through employing a double-SLAM algorithm; according to the global point cloud map and the local point cloud map, using a CHSM-based passable pavement segmentation method to construct a global passable point cloud map; taking the global passable point cloud map as the input of UGV navigation, and generating a global safe path by adopting a PLA-RRT algorithm; in combination with real-time environment data, using an NMPC algorithm to locally optimize the global security path so as to realize real-time obstacle avoidance and path smoothing, and obtaining a final planning path; and then the motion instruction is transmitted to the controller to guide the UGV to complete navigation. According to the invention, the application limitation of the existing UGV navigation framework in a field unstructured environment is overcome.

The invention discloses a multi-AUV safe path planning method based on deep reinforcement learning. According to the method, a deep reinforcement learning algorithm model based on an MATD3 method is adopted, a policy network and a value network are updated by adopting security constraints, the security of policy learning is improved, the expected reward revenue is maximized under the condition that the expected security cost constraints are met, a frequent minimum and maximum optimization process is avoided by adopting first-order penalty optimization, and the security of policy learning is improved. Meanwhile, safety correction based on a safety layer is added in the training process to guarantee safety in the early stage of training, and exploratory and safety balance is brought to strategy optimization of reinforcement learning through safety constraint and safety correction. According to the multi-AUV path planning method, the time cooperation constraint and the space cooperation constraint of multi-AUV path planning are comprehensively considered, the centralized training and decentralized decision-making architecture is applied to multi-AUV path planning, the path planning method capable of ensuring cooperation safety is provided for a multi-AUV system, and the safety and the reliability of path planning are improved.

The invention discloses a well repair robot control algorithm fusion method, and relates to the field of oil and gas field well repair. Comprising the following steps: collecting a video stream through a high-resolution camera, carrying out target detection and multi-dimensional feature extraction by using an improved YOLOv7 and Res-Net-50 network, and generating an interactive three-dimensional model in combination with a classifier result; fusing the point cloud density and the image texture features, and training a lightweight semantic segmentation model to realize a target object category; selecting an optimal grabbing point and strategy according to categories and features, constructing a working environment map by combining an A * algorithm and multi-sensor data, and planning a safe path; dynamic track adjustment and pose control are realized through coordinate system alignment and real-time pose feedback; and finally, according to the grabbing strategy, all finger joints of the mechanical arm are controlled to grab the target object, the comprehensive index influence score is calculated, whether grabbing needs to be conducted again or not is judged, accurate grabbing of the robot is controlled, and the stability and reliability of the system are improved.

An orchard picking robot path planning algorithm comprises the following steps: (1) constructing a two-dimensional grid map according to an orchard top view or a planning map; (2) classifying obstacle areas and customizing a picking sequence; (3) acquiring a peripheral angular point coordinate set of the picking area; (4) determining a connection point set of adjacent regions; (5) performing regional path planning by using an improved A * algorithm and integrating a preliminary path; (6) optimizing the path through an arc fitting smoothing method; and (7) drawing a final path. According to the method, a complex orchard environment can be effectively modeled and analyzed, regional planning and integration are carried out by means of the improved A * algorithm, and complex conditions of numerous obstacles, irregular layout and the like in an orchard can be handled. And an arc fitting smoothing method in path optimization processing further improves the performability and smoothness of the path in a complex environment. The method can adapt to a complex orchard environment, and provides efficient and safe path planning for the picking robot.

The invention relates to the technical field of intelligent control of fire-fighting robots, and discloses a fire-fighting robot capable of autonomously moving and automatically detecting and extinguishing fire and a control method. The method comprises the following steps: acquiring original detection data including a visible light image, a thermal infrared image, a laser point cloud and a sound signal; after feature extraction is carried out on the data sources, mapping the data sources to a unified environment three-dimensional coordinate system according to a preset space alignment rule, and generating fusion environment data; a fire source is identified and positioned based on the data, and the three-dimensional coordinate, the combustion intensity and the expansion rate of the fire source are obtained; in combination with fire source information and obstacle contours, performing global path planning by adopting an improved fast exploration random tree algorithm fused with a fire scene safety evaluation index; and finally, according to the planned path, the safety index and the state of the robot, a task execution instruction sequence containing a moving and fire extinguishing strategy is generated. According to the invention, the precision and consistency of multi-modal sensing are improved, and safe path planning under the dynamic fire scene risk is realized.

The invention relates to the technical field of logistics, in particular to an RGV dolly avoidance system and method.The RGV dolly avoidance method is characterized in that a multi-mode sensing module integrates data of a laser radar, a camera and a millimeter wave radar to form an environment sensing report, and an intelligent decision-making module rapidly assesses threats and generates an avoidance strategy based on a deep reinforcement learning algorithm; the dynamic path planning module adopts an improved ant colony algorithm and plans a safe path in combination with obstacle parameters and kinematic characteristics of a trolley, the precise control module drives a steering system to execute the path through a motion control algorithm, and the redundant communication module adopts wireless / wired multi-link to guarantee real-time communication, map the running state in real time and early warn faults. Data support is provided for system optimization, and all the modules cooperate to achieve the functions of environment perception, decision planning, accurate control and state monitoring.

A deep neural network(s) (DNN) may be used to perform panoptic segmentation by performing pixel-level class and instance segmentation of a scene using a single pass of the DNN. Generally, one or more images and / or other sensor data may be stitched together, stacked, and / or combined, and fed into a DNN that includes a common trunk and several heads that predict different outputs. The DNN may include a class confidence head that predicts a confidence map representing pixels that belong to particular classes, an instance regression head that predicts object instance data for detected objects, an instance clustering head that predicts a confidence map of pixels that belong to particular instances, and / or a depth head that predicts range values. These outputs may be decoded to identify bounding shapes, class labels, instance labels, and / or range values for detected objects, and used to enable safe path planning and control of an autonomous vehicle.

The invention discloses a railway maintenance field operation protection card control method and device and a medium, and relates to the technical field of rail transit equipment, and the method comprises the steps: carrying out the spatial diffusion of a comprehensive risk index value, generating a dynamic risk field, carrying out the field intensity gradient vectorization and path integration of the dynamic risk field, and generating a safety path coordinate sequence; performing spatio-temporal trajectory matching on the safe path coordinate sequence to obtain a path execution deviation degree, performing spatio-temporal causal analysis on the path execution deviation degree, and constructing an event causal diagram; and obtaining the violation contribution degree of the event causal graph through anti-fact query, and carrying out strategic compiling on the basic permission rule base according to the violation contribution degree to generate an incremental control strategy and a visual decision report. According to the method, the dynamic risk field is generated and the event causal graph is constructed, so that the risk root is accurately positioned, and the rule dynamic adaptability and the decision interpretability are enhanced.

The invention provides a safety partition and safety path control method and system for a series press line, and belongs to the technical field of press control. The method comprises the steps that a mode selection instruction is received, a whole line operation mode is recognized, and safety partition configuration is generated; collecting safety signals and moving trajectory data in real time, and establishing a whole line safety control model and a stamping unit safety sub-model in a bus safety controller; calling a whole-line safety control model and a safety sub-model to compare and evaluate a safety state in real time, and triggering safety operation according to a whole-line operation mode when the safety state is abnormal; in the teaching or running process of the carrying path, a safety motion state machine is established for the carrying equipment in the activated partition, and safety control is conducted on the carrying path; and when a person needs to enter the line body, responding to a maintenance request, automatically planning a fault maintenance small partition, executing energy locking and activating a safety scanner to dynamically monitor the position of the person according to a safety partition configuration mode. According to the invention, accurate matching of the security resources and the production requirements is realized.

The invention relates to the technical field of computer vision and intelligent navigation, in particular to an intelligent obstacle avoidance method, system and device based on monocular pure vision. The method comprises the following steps: generating a global cost map containing all static obstacles based on a processed original image sequence; generating a global path capable of bypassing all static obstacles according to the global cost map; when the user advances according to the global path, generating a local cost map containing dynamic obstacles based on the processed current image sequence; fusing corresponding areas in the local cost map and the global cost map to obtain a final cost map; according to the final cost map and the global path, generating a target path which accords with the direction of the global path and bypasses the dynamic obstacle; and converting the target path into a navigation instruction and feeding back the navigation instruction to the user to guide the user to safely arrive at the destination position. According to the invention, efficient and safe path planning is realized while low cost is ensured, and the travel safety of a user is ensured in a complex environment.

The invention discloses a vehicle-road collaborative obstacle avoidance path optimization method, system and device, and relates to the technical field of obstacle avoidance path optimization, and the method comprises the steps: obtaining vehicle-mounted sensing data and road test sensing data; obstacle avoidance planning is carried out, and a safe path and a steering decision instruction are determined; meanwhile, the traffic weights of the road nodes under different time granularities are dynamically distributed; introducing a risk coefficient to dynamically correct the path cost; performing global search, and configuring an extreme obstacle avoidance window meeting distance following perception; and executing driving safety response optimization of the safety path and the steering decision instruction. The technical problem that in the prior art, vehicle-road collaborative obstacle avoidance path planning does not fully fuse vehicle-mounted sensing data and road test sensing data, so that the obstacle avoidance safety and the passing efficiency are insufficient is solved, and the effects of fusing the vehicle-mounted sensing data and the road test sensing data to perform obstacle avoidance planning are achieved; the technical effect of improving the safety and passing efficiency of the obstacle avoidance path under vehicle-road cooperation is achieved.

The invention provides a parking lot space management and control method and system, and relates to the technical field of intelligent transportation, and the method comprises the steps: inputting calibrated parking space state mapping data, a final parking space guiding path scheme and updated special parking space state data into a space-time prediction algorithm, and carrying out the multi-source data fusion and parking space demand trend analysis, a parking space demand prediction map is obtained; based on the map, carrying out dynamic division and parking space resource allocation on a parking lot area to obtain a dynamic area allocation strategy; and based on a dynamic area distribution strategy, carrying out data synchronization on continuously updated parking space state data and a payment processing process, automatically completing payment verification when the vehicle leaves the parking lot, releasing path planning resources after the verification is passed, and updating the parking space state database. According to the invention, through closed-loop management of multi-technology fusion, accurate sensing of parking space states, safe path guidance, dynamic optimization of resources and efficient billing of departure are realized.

The invention provides an underwater robot intelligent inspection method based on adaptive flexible net cage deformation prediction, and the method comprises the steps: collecting multi-source heterogeneous environment data around a net cage in real time, and carrying out the preprocessing of time synchronization, denoising, normalization and the like; predicting the three-dimensional deformation attitude and uncertainty of the net cage on the basis of introducing a self-adaptive ConvLSTM model of'intelligent fusion priori knowledge 'and'channel attention', and constructing a safety potential site map; an improved A * algorithm and a TSP algorithm are utilized to generate a global optimal safety path which can completely traverse the whole three-dimensional deformation net wall and has the lowest total cost; the global optimal safety path is sent to the underwater robot for execution, and errors are corrected in real time in the execution process until inspection is completed; according to the method, the adaptability of the underwater robot to dynamic, flexible and uncertain environments can be improved, and full-coverage, high-precision and high-reliability automatic inspection of the underwater robot in a complex dynamic marine environment is realized.