1206 results about "Navigation path" patented technology

The invention provides an autonomous navigation path planning method, device and equipment for a humanoid robot in a complex environment. A navigation task is determined by obtaining the current position and path requirement of the robot; constructing a constraint preposition map based on body size constraint, joint motion constraint and dynamic balance constraint, and calculating a feasible motion space of the robot in advance; acquiring environment sensing data for variation trend analysis, and predicting a future movement track of the dynamic obstacle to generate a dynamic environment prediction sequence; establishing a bidirectional planning mechanism, performing forward and reverse path search at the same time, and generating a plurality of candidate path hypotheses through meeting point detection and path fusion; analyzing a human behavior pattern to identify a potential passage conflict area, establishing a social negotiation strategy, and taking an intermediate meeting point as a social key node to adaptively adjust a candidate path; the optimal safety path is determined through multi-dimensional safety evaluation of the falling risk, the collision risk and the energy consumption risk, and intelligent autonomous navigation of the humanoid robot in a complex environment is achieved.

The invention discloses an intelligent traffic signal device and a remote control method, and relates to the technical field of intelligent traffic control. The method is used for solving the problems of low emergency traffic efficiency and global and local control imbalance under sudden traffic events. The method comprises the following steps: firstly, fusing an emergency vehicle navigation path, accident point vehicle motion abnormal parameters and pedestrian aggregation distribution data, constructing a multi-dimensional event evolution feature vector through a space-time encoder, and accurately describing a traffic situation; an event influence domain boundary is delimited based on vehicle trajectory and flow direction consistency analysis, a dynamic evolution model is constructed in combination with an acceleration abrupt change propagation path, and a space-time conflict probability matrix is generated; then executing a hierarchical control strategy, dynamically adjusting an intersection signal period starting time difference in a global level to form an emergency green wave coordination band, and inserting an adaptive full red phase in a local level according to a balance relation between a vehicle arrival rate and a dissipation rate; and finally, reversely correcting model parameters through an actual pass error, updating a conflict probability generation rule, and forming a closed-loop feedback mechanism.

A system and method for latent hyperspace navigation in spatiotemporal media using hierarchical and Lorentzian autoencoders. The system compresses spatiotemporal media into navigable latent representations while preserving geometric and semantic relationships through tensor structure maintenance. A latent hyperspace manager organizes compressed representations as geodesic trajectories within a geometric manifold structure based on differential geometry principles. A geodesic trajectory mapper computes optimal navigation paths through the high-dimensional space, while symbolic anchors positioned at semantically significant locations serve as persistent reference points. Spatiotemporal routing protocols manage navigation decisions across multiple temporal scales. A strategy caching system preserves successful navigation patterns for reuse, enabling continuous learning. The system generates synthetic content during navigation to support infinite zoom capability, allowing exploration beyond original media boundaries. Cross-modal fusion combines diverse input modalities into unified representations, applicable to immersive media exploration, scientific visualization, and surveillance analysis.

The invention relates to the technical field of intelligent robots and old-age care, and discloses a target navigation method and device based on environmental context, a robot and a medium, which can be applied to a household auxiliary robot intelligent medicine delivery scene, and the method comprises the following steps: receiving multi-modal input of a user, preprocessing the multi-modal input, and generating preprocessed multi-modal data; reading the current time and the environment state, and performing perceptual analysis based on a pre-constructed tetrad map, the current time and the environment state to generate a candidate search area list; determining a target candidate area from the candidate search area list; generating a target navigation path based on the SLAM map and the target candidate area, and moving the robot to the target candidate area according to the target navigation path; and obtaining a target candidate region image, and performing visual semantic verification based on the target candidate region image and the target object label in the structured text instruction. According to the invention, the robot navigation accuracy and the user experience are improved.

The invention discloses a wounded rescue life detection and positioning method based on deep learning. The method comprises the steps that S1, multi-view RGB-D images, laser radar point clouds, millimeter wave vital sign radar data and infrared thermal image data are collected and subjected to time synchronization; s2, outputting a pixel-level semantic mask; s3, generating a semantic point cloud of the wounded; s4, forming a metric-semantic double-map data set; s5, generating vital sign scores and writing the vital sign scores into a metric-semantic double-map data set; s6, updating wounded dynamic anchor point attributes in the measurement-semantic double-map data set; and S7, based on the updated metric-semantic double-map data set, calculating the three-dimensional coordinate and rescue priority sequence of each wounded dynamic anchor point, and generating an obstacle avoidance optimized navigation path for avoiding the risk semantic region. The rescue efficiency and safety are effectively improved.

The invention relates to the technical field of visual navigation, in particular to a robot navigation method based on vision, which comprises the following steps: acquiring a grayscale image to extract dynamic point locations, screening boundary features to reconstruct a feature set, calculating gradient change to generate candidate guide points, converting coordinates to construct a path track set, and outputting a navigation planning path. According to the method, rapid elimination of static backgrounds is realized through difference threshold selection, dynamic region extraction accuracy is effectively improved, combined screening of boundary line feature point frequency and average feature density is combined, key feature stability is enhanced, and the identification capability of image local structure mutation is enhanced by using a variance change rate of a gradient amplitude sequence. Robustness of path guide point extraction is improved through judgment of an abnormal section, dynamic construction of a path track is realized by means of continuous time integration of image coordinates, path identification precision and spatial positioning continuity are optimized, and continuity, precision and real-time performance of navigation path planning are improved.

The invention discloses an automatic control method and device for a thermal management system of an electric vehicle, relates to the field of intelligent control, and realizes multi-step prediction of battery and motor heat production and thermal load change in the future by fusing external information such as a navigation path and weather forecast, plans a thermal management strategy in advance, and improves the response perspectiveness of the system. A multi-source disturbance dynamic structured correlation equalization technology is adopted, the coupling relation between heat sources is modeled, cross disturbance is compensated through a joint state function, and multi-heat-loop cooperative control is achieved. On the basis of a model predictive control (MPC) framework, temperature tracking errors and energy consumption penalty are optimized, the energy consumption of actuators such as a compressor and a water pump is reduced while a safe temperature interval is met, and the endurance is prolonged. And in combination with a state estimator fusion model and observation data, the control precision and robustness under a complex working condition are enhanced, and finally global optimal thermal management considering the service life of parts, the comfort of passengers and the energy efficiency is realized.

The invention provides a cross-domain multi-dimensional query feature index selection method, which comprises the following steps of: judging a query field and a query purpose of a user through query subject classification and query intention identification according to historical query behavior data of the user, and obtaining a field tag and an intention tag of the query; a navigation path of knowledge links is optimized, semantic correlation between nodes and small world index algorithm characteristics are comprehensively considered, an optimal knowledge navigation path is recommended for a user, and an optimization strategy is dynamically adjusted according to query historical behaviors and feedback of the user; a knowledge navigation result is presented in a visual interaction mode, a multi-dimensional attribute screening and sorting function is provided, exploration type browsing and deep mining of a user are supported, and personalized knowledge recommendation service is provided according to a query scene and preference of the user.

The invention provides a GNSS (Global Navigation Satellite System) anti-interference receiver system based on radio frequency front end-INS (Inertial Navigation System) combined assistance, which adopts a dual-core ARM Cortex-A9 and a programmable logic unit for layered decoupling, and realizes real-time receiving and processing of a high-bandwidth data stream, closed-loop control of an interference suppression link and quick response of a navigation path and a strategy decision. All the modules efficiently cooperate through AXI-Lite register mapping, interrupt signals, a shared state buffer area and a timer synchronization mechanism. By controlling bus and state buffer mapping to keep consistency and supporting a module-level independent debugging and system-level soft reset mechanism under hot start, the expandability of subsystem cascade debugging, fault module positioning and online updating is realized. In the aspects of module architecture, data channels and cooperation mechanisms, the structure order, efficient communication and control closed loop are realized, and an embedded multi-module cooperation anti-interference platform which is suitable for a high-interference environment, multi-channel fusion processing and navigation task real-time response is constructed.

The invention relates to an indoor robot navigation method based on multi-modal feature fusion. The method comprises the following steps: constructing a semantic map based on visual observation environment information; the method comprises the following steps: acquiring an RGB image of an indoor scene object, converting the RGB image into point cloud data, and preprocessing the RGB image and the point cloud data; image multilayer semantic features and point cloud features in the RGB image and the point cloud data are extracted respectively, and initial fusion is carried out; performing weighted fusion on the multi-layer semantic features and the point cloud features of the image by adopting space-channel-cross-modal multi-attention dynamic cooperation; and predicting a long-term target in a map space from top to bottom based on the fused feature map and the semantic map, and performing navigation path planning based on the current position and the long-term target. Under low-cost hardware configuration, the robustness of environment perception, the real-time performance of decision response and the usability of system integration in a dynamic complex environment are comprehensively improved, and the method is particularly suitable for application scenes such as indoor service robots.

The invention provides a robot navigation method and system based on visual identification, and relates to the technical field of computer vision, firstly, a continuous visual information set of the surrounding environment of a robot is obtained, the continuous visual information set comprises environment visual images of different directions of the advancing direction of the robot, and the object appearance features and the spatial arrangement relation are recorded; then establishing association mapping between the continuous visual information set and a preset navigation area to obtain a double-association mapping result; generating a semantic anchor point navigation path based on a double-correlation mapping result, wherein the semantic anchor point navigation path comprises a semantic anchor point sequence from the current position to the target position; real-time visual semantic features in the navigation process are obtained through a real-time visual collection module, and a semantic anchor navigation path is dynamically updated; finally, a final navigation execution path is output according to the updated path, the robot is driven to complete navigation operation, and the navigation capability and the intelligent level of the robot in a complex dynamic environment are improved.

The invention discloses a routing inspection unmanned aerial vehicle autonomous navigation path planning and obstacle avoidance method and system. The method comprises the following steps: initializing; the reinforcement learning agent performs training through interaction with the environment, and in each time step, the current strategy network generates an action based on an inertial exploration strategy adopting OU noise enhancement; after the unmanned aerial vehicle executes the action, the environment updates the state, and an instant reward is calculated; storing the state, the action, the instant reward and the new state of each time step in an experience playback buffer area; updating double-Q network parameters based on the data in the buffer area and updating strategy network parameters according to a delay updating mechanism; and repeatedly training until the accumulated reward of the strategy network exceeds a threshold value, and outputting the trained strategy network to control the unmanned aerial vehicle in real time. The adaptive capacity of the unmanned aerial vehicle in a complex scene is improved, and the unmanned aerial vehicle is suitable for power equipment inspection tasks in complex terrains and dense obstacle environments.

The invention relates to a dynamic cooperative path planning and autonomous landing control method for an unmanned aerial vehicle and an unmanned ship, and the method comprises the following steps: collecting the state data of the unmanned ship and the unmanned aerial vehicle and the high sea condition environment data, and carrying out the data fusion processing; an improved fast extended random tree algorithm is adopted to plan a safe navigation path for the unmanned ship, the generated path of the unmanned ship is optimized based on an unmanned ship motion model considering environmental interference, and a model prediction control algorithm is adopted to plan a path for the unmanned ship to fly to a predicted landing point of the unmanned ship. Dynamic cooperation between the unmanned ship and the unmanned aerial vehicle is realized through information interaction between the unmanned ship and the unmanned aerial vehicle; the unmanned aerial vehicle recognizes an unmanned ship landing identifier through a visual servo guide system, calculates the position and attitude deviation relative to a landing point, adjusts flight control parameters according to interference conditions by using an adaptive control algorithm, and performs trajectory prediction compensation in combination with an unmanned ship motion model to realize autonomous landing. Compared with the prior art, safe landing of the unmanned aerial vehicle on the unmanned ship under the high sea condition is achieved.

The invention relates to the technical field of robot navigation, and discloses a safety inspection robot multi-mode navigation system fused with subconscious learning. The system obtains a visual image, a depth distance, an inertial measurement unit and other multi-dimensional perception data of an inspection environment, performs environment feature extraction to generate an environment semantic topological graph, and constructs a dynamic navigation cost map and a preliminary navigation path according to the environment semantic topological graph. During path execution, the system detects abnormal navigation behaviors in real time, extracts the features of the abnormal navigation behaviors and obtains a first correction coefficient. And the system judges whether the implicit environment semantic identifier is identified, and if the implicit environment semantic identifier is identified, the corresponding path key node is taken as a semantic navigation reference point, a final navigation path is generated through fusion, and a time stamp is recorded. And if the time stamp exceeds the preset tolerance, extracting a corresponding second correction coefficient. And the system combines the basic navigation performance evaluation value, the first correction coefficient and the second correction coefficient to output a multi-modal navigation decision result.

The invention relates to the technical field of wireless communication, in particular to an intelligent wireless communication control method applied to ships and boats. The method comprises the following steps: acquiring radar original point cloud data, an AIS ship attribute data set, a Beidou navigation state data set and night vision target feature data through a multi-dimensional sensor, and constructing ship multi-source standardized preprocessing data; therefore, the functions of multi-source heterogeneous data fusion, dynamic channel adaptive modulation, distributed relay communication guarantee, real-time air route dynamic planning, intelligent instruction generation and the like are realized; the problems of high data isolation, large transmission delay, rigid navigation path, slow obstacle avoidance response, insufficient formation collaboration and the like in the existing ship communication control technology are solved, and the communication stability, the navigation safety and the overall combat effectiveness of the ship formation in a complex dynamic environment are remarkably improved.

The invention relates to the technical field of navigation planning, and discloses an unmanned aerial vehicle automatic inspection nest path planning method and system, and the method comprises the steps: carrying out the multi-source data fusion of a target inspection region, and obtaining space measurement environment data; analyzing an airspace limiting condition in the spatial measurement environment data, and determining a navigation path measurement constraint condition in combination with an inspection task demand of the target inspection area; performing gridding measurement on the target inspection area to identify candidate waypoints; according to the spatial position information, analyzing a spatial relationship of the candidate waypoints to construct an initial navigation network; performing multi-constraint evaluation on the initial navigation network to obtain constraint weights corresponding to the candidate waypoints, and integrating the candidate waypoints with the constraint weights higher than a preset weight threshold into a global optimal waypoint set; carrying out passable connection on adjacent waypoints in the global optimal waypoint set to obtain a navigation path; according to the invention, the efficiency of automatic inspection nest path planning of the unmanned aerial vehicle can be improved.

The invention relates to the technical field of path planning, and discloses a real-time path data planning processing method and system based on SLAM. The method comprises the following steps: performing semantic-geometric joint feature extraction through a laser radar and an RGB camera to obtain joint observation data; performing covariance propagation on the SLAM state vector based on semantic constraint Kalman filtering to obtain a positioning covariance and a covariance ellipse; performing constraint propagation on the semantic object set to generate virtual feature points to construct an enhanced feature map; environment grid safety scoring is carried out according to the semantic risk weight and the obstacle distance; semantic cost and SLAM uncertainty processing is carried out through bidirectional A * search, and a real-time navigation path is generated. According to the method, the technical problem of insufficient comprehensive consideration of semantic security constraint and positioning uncertainty in real-time path planning based on SLAM in a feature sparse environment is solved.

The invention discloses a robust autonomous navigation and obstacle avoidance method for an underwater detection robot, and relates to the technical field of underground space intelligent identification and navigation.The method comprises the steps that through the multi-sensor fusion technology, a three-dimensional point cloud data set and a depth image data set of an underground environment are synchronously collected through a sonar and a stereoscopic vision system; the method comprises the following steps: constructing original geometric morphology data of an underground space, providing basic information for subsequent data processing, constructing a dynamic updating mechanism according to an initial navigation path data set, monitoring underground environment change and structural state through real-time sensor data, and regularly updating risk level values. If the risk level change exceeds a preset range, triggering path recalculation to ensure adaptation to underground environment change; the robust autonomous navigation and obstacle avoidance method of the underwater detection robot has important application value in underground engineering fields such as mines and tunnels, and provides technical support for intelligent management and safe operation of underground space.

The invention provides an agent visual language navigation method and system based on task completion prediction. The method comprises the step of constructing a dual-drive structure composed of a self-adaptive mixed pooling mechanism and a task completion analysis module. Firstly, in the visual information processing process, a dynamic weight distribution strategy is adopted to carry out multi-scale adaptive mixed pooling on panoramic features, so that the fusion effect of local and global semantic information is optimized, and the retention capability and semantic integrity of navigation historical information in a dynamic topological map are improved. And then, inspired by a human navigation cognitive behavior mechanism, a task completion analysis module is designed and introduced, and the task execution progress is dynamically estimated based on the recognition condition of a key landmark in a navigation path, so that an intelligent agent is driven to preferentially select a key path node and invalid exploration is reduced. And finally, realizing efficient understanding and execution of the natural language instruction by the intelligent agent through a multi-round cyclic cross-modal reasoning and action prediction mechanism.

The invention discloses a sea condition perception and path prediction navigation method and system for a high-speed unmanned ship, and relates to the technical field of multi-source path decision optimization, and the method comprises the steps: collecting storm real-time perception data through a sensing device, carrying out the preprocessing, outputting a standardized sea condition state vector, and providing an experience reference for real-time path planning through ship-borne historical data; constructing a multi-target navigation prediction algorithm model to predict and generate a path candidate scheme set on the basis of the storm real-time sensing data and the ship-borne historical data, and performing evaluation through a conditional random field in combination with path candidate schemes generated by the storm real-time sensing data, the ship-borne historical data and target navigation prediction; and carrying out multi-dimensional safety risk scoring on an evaluation result through a fuzzy analytic hierarchy process by combining the wave height and direction influence, the hull stability and the obstacle avoidance complexity, and determining an optimal sailing path. According to the invention, fusion of multi-source sea condition perception and dynamic path optimization is realized, and the navigation stability, the adaptability and the risk control capability of the high-speed unmanned ship are improved.

The invention provides a scene reconstruction and semantic understanding method and system based on a visual large model, and relates to the field of computer vision and three-dimensional reconstruction, and the method comprises the steps: obtaining multi-view image data of a target scene, inputting a pre-training visual large model, and outputting a joint feature representation and attention weight matrix; generating three-dimensional coordinate values and semantic probability distribution of spatial sampling points in a three-dimensional space according to the joint feature representation, and constructing a spatial semantic field; clustering the spatial sampling points by using the attention weight matrix, performing semantic consistency enhancement, and converting the spatial sampling points into deterministic semantic tags; constructing a geometric optimization objective function, and adjusting three-dimensional coordinate values; and extracting continuous space sampling points with the same semantic tag to form an object boundary, constructing a scene topological graph, deducing a scene functional structure, and generating a navigation path. According to the method, the unification of accurate geometric reconstruction and deep semantic understanding of the scene is realized, the three-dimensional reconstruction precision and semantic analysis accuracy are improved, and reliable support is provided for intelligent navigation.

The invention relates to a ship trajectory data mining and macro-micro fusion route prediction method and system, and the method comprises the steps: S1, extracting ship trajectory data near an inland waterway from the data of an automatic ship recognition system, and carrying out the cleaning, filtering, sorting and preliminary analysis; s2, performing clustering analysis by using the features of the ship trajectory data, preliminarily mining trajectory features in combination with manual annotation, and establishing a ship trajectory database; s3, on the basis of historical data, constructing a deep learning model aiming at the inland river driving key path nodes of the ship from a macroscopic level; s4, training a deep learning model combining a convolutional neural network and a long-short-term memory network based on historical data, and predicting a ship position track from a micro level; and S5, performing macro-micro fusion prediction on the new ship data to obtain a ship navigation path and trajectory prediction coupling result. According to the method, coupling of macro and micro prediction means is adopted, and a certain guarantee is provided for the precision of ship trajectory prediction.

A navigation route planning apparatus includes processing circuitry configured to receive a planned route indicating a route and a current course direction of a movable body, receive movable body information that includes a position, a travelling direction, and a speed of the own ship, receive obstacle information that includes a position, a traveling direction, and a speed of an obstacle, determine a collision risk value associated with the planned route along the current course direction based on the movable body information and the obstacle information, and determine whether the movable body requires to at least one of evade the planned route and continue traversing on the planned route based on the collision risk value.

The invention discloses a photovoltaic cleaning control method fusing RTK positioning and visual compensation, and belongs to the technical field of photovoltaic module cleaning. According to the invention, through the RTK track points acquired offline, the least square method is combined with the RANSAC algorithm to carry out segmented straight line fitting, and the original points are projected to the corresponding straight lines to realize collinear correction, so that the measurement error is eliminated, and a continuous and collimated navigation path can be generated; edge lines (photovoltaic module grid lines) with geometrical characteristics in the environment are detected in real time through a visual sensor, the line segment direction is extracted through Hough transform, the line segment direction is compared with the target course calculated through RTK, course deviation is estimated, dynamic correction is conducted through a control strategy, and it can be ensured that the photovoltaic cleaning robot operates along the expected path with high precision; and on the basis of a lightweight YOLOv7 model, the photovoltaic panel is subjected to pollution category division and mapped to optimal water pressure and brush speed parameters, cleaning parameters are dynamically modified, and then cleaning strategy control is completed.

The invention discloses a mobile robot control device giving consideration to field navigation patrol and transportation load, and relates to the field of robot control, and the device comprises a sensor data collection module which forms multi-source sensor data; the environment understanding module is used for predicting terrain risks and weather influences based on the feature vectors and outputting environment feature data and risk prediction; the navigation control module is used for navigating a path based on the environment characteristic data and generating a robot motion control instruction; the load management module adjusts parameters of the navigation control module based on the load state; the feedback adjusting module is used for dynamically adjusting control parameters according to the load state and feeding back the control parameters to the navigation control module to optimize and modify a robot motion control instruction; and the intelligent detection module is used for detecting environment abnormity and dangerous case events and outputting an alarm signal. The method has the advantages that accurate prediction of environmental risks is realized through multi-sensor layered feature extraction and attention weighted fusion, and motion parameters are dynamically adjusted in combination with a moment balance model.

The invention discloses a dynamic water quality monitoring system based on an unmanned ship and a data acquisition and distribution algorithm. Comprising an unmanned ship platform module, an initial navigation path setting module, a water quality data acquisition module, a dynamic adjustment and optimization module, a control interaction module and a dynamic adjustment and optimization module, wherein the dynamic adjustment and optimization module dynamically adjusts the navigation strategy and sampling frequency of the unmanned ship in a target to-be-detected water area through a second branch of a first neural network model; the unmanned ship can sail autonomously, continuous and global water quality monitoring is achieved, through an efficient data processing algorithm, the accuracy and reliability of data are improved, a scientific basis is provided for water quality management, a distributed algorithm model is achieved to conduct predictive analysis on each specific target sampling point, the accurate predictability of unmanned ship monitoring is improved, and the water quality monitoring efficiency is improved. The monitoring strategy is dynamically adjusted according to actual requirements, optimal configuration of monitoring resources is achieved, the monitoring cost is reduced, the water quality change trend is found in advance through prediction analysis of the neural network model, and powerful support is provided for early warning and emergency response.

The invention relates to the technical field of robot navigation and artificial intelligence, in particular to an autonomous navigation robot system based on multi-modal perception, which comprises a visual perception module, a laser radar module, a voice instruction module, a central processing unit and an execution control unit, the visual perception module is used for collecting an environment RGB-D image; the laser radar module is used for acquiring an environment distance point cloud; the voice instruction module is used for receiving and analyzing a user voice instruction; the central processing unit generates a navigation path and an obstacle avoidance strategy through a multi-modal data fusion algorithm and a path planning algorithm; the execution control unit drives the robot chassis to move according to the navigation path and the obstacle avoidance strategy.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of intelligent auxiliary navigation, financial science and technology, medical health and the like, and discloses a navigation instruction generation and path updating method, device, equipment and medium. And fusing the obstacle category information and the environment depth information, generating spatial position information and direction information of the obstacle, generating a navigation instruction containing scene description information, obstacle orientation and distance description information and action suggestion information based on the spatial position information, the direction information and the environment image information, and updating a navigation path. According to the method, the obstacle category information and the environment depth information are fused, the obstacle recognition result with the semantic level is generated in combination with the environment image information, the navigation instruction containing the orientation and the distance is output, and the accuracy of obstacle perception and the practicability of navigation guidance can be improved.

The invention relates to the technical field of vehicle navigation and path planning, and provides a marine inertial navigation path planning method and system, and the method comprises the steps: obtaining environment risk information of a path corresponding to the driving direction of an autonomous underwater vehicle; adjusting a deviation tolerance threshold value corresponding to an inertial navigation system on the autonomous underwater vehicle according to the environmental risk information; acquiring a position calculation deviation of the inertial navigation system; the position calculation deviation is compared with a deviation tolerance threshold value, and triggering of a communication correction action is decided according to the backlog condition of the emergency event data; and when the communication correction action is triggered, planning and executing a communication correction track to realize position correction of the autonomous underwater vehicle. The method has the advantages that the navigation strategy is dynamically adjusted according to the environment risk and the data backlog condition, the communication correction opportunity is intelligently decided, and the autonomous navigation capability and the task adaptability of the autonomous underwater vehicle in the complex marine environment are improved.

The invention relates to the technical field of intelligent fishing, in particular to an intelligent fishing method for effectively improving fishing efficiency, which comprises the following steps: S1, constructing a fish school calculation sub-model: recording historical fish school data, environment data and fishing data, and constructing a fishing calculation model according to the data; s2, ecological protection and optimization: setting a dynamic threshold value and a trapping forbidding rule, monitoring environmental data in real time, and embedding the data and a fishing policy rule into a calculation model to realize strategy self-adaption of different water area scenes; on the basis of the fish school calculation sub-model and the environment prediction sub-model, satellite remote sensing and sonar detection are combined, the positioning error of the fish school gathering area is greatly reduced, the navigation path optimization time of the fishing boat can be shortened, the fish catch per unit time is greatly improved, and through the S-shaped navigation path and the electronic pulse driving technology, the fish school gathering efficiency is improved, and the fishing efficiency is improved. Edge computing equipment is matched to coordinate multiple ships to form a surrounding net, and the cluster fishing success rate is greatly increased.