1891 results about "Underwater robot" patented technology

The invention provides a comprehensive and intelligent mariculture net cage management and control system. The system integrates a multi-parameter sensor module, an underwater robot module, an underwater camera module, a central control module, a data storage and analysis module, an early warning module, a remote communication module and an automatic execution module. By monitoring multiple environmental parameters such as water temperature, salinity, dissolved oxygen and the like in real time and utilizing the underwater robot and the underwater camera to carry out autonomous inspection and fish behavior observation, the system can accurately master the culture environment condition. The AI algorithm and the intelligent model are combined, the system dynamically adjusts the bait casting amount, the oxygenation frequency and the net cage position, and the breeding benefits are effectively improved. Meanwhile, the early warning module carries out graded early warning on risk events such as typhoon and diseases, and the breeding safety is ensured. The remote communication and visual human-computer interface further enhances the remote monitoring and management capability of the system.

The invention relates to a coastal wetland ecological restoration effect evaluation method and system, and belongs to the technical field of ecological monitoring and restoration. Aiming at the problems of static evaluation index lag, insufficient data fragmentation integration and lack of causal diagnosis mechanism in the prior art, multi-source time sequence ecological data are acquired by constructing a three-dimensional monitoring network consisting of satellite remote sensing, an unmanned aerial vehicle, an Internet of Things sensor and an underwater robot; a fusion data set is generated based on space-time alignment and tidal phase marking, dynamic evolution characteristics are extracted by using an LSTM network, and a spatial distribution difference chart of ecological parameters before and after restoration is generated in combination with a GAN; and furthermore, through hot area clustering and historical data association mining, a degradation driving factor is positioned, and an optimization suggestion is generated. The method breaks through the space-time limitation of a traditional evaluation method, remarkably improves the degradation diagnosis precision and the regulation and control efficiency of restoration engineering, and is suitable for various ecological restoration scenes such as estuary delta and coral reefs.

The invention relates to an underwater robot navigation positioning method and system. The method comprises the following steps: S1, acquiring angular velocity and acceleration signals through an inertial measurement unit; s2, resolving a three-dimensional velocity observation value according to the beam radial velocity vector signal in combination with the angular velocity signal, and extracting environment feature point cloud data according to the acoustic image signal; s3, multi-source data time synchronization is carried out, and a fusion input signal with time-space alignment is generated; s4, constructing an adaptive factor graph optimization model, and dynamically adjusting an inertial navigation solution node based on a real-time weight coefficient; inputting the environment feature point cloud data into a closed-loop detection module to generate a loopback factor node, and adaptively correcting the weight of the node according to the feature matching degree; and S5, solving the adaptive factor graph optimization model through a nonlinear optimization algorithm. According to the underwater robot navigation positioning method and system, the problem that the fusion positioning precision of a multi-source heterogeneous sensor is insufficient in an underwater GPS-free environment can be solved.

The invention discloses an end-to-end underwater three-dimensional reconstruction method and system based on an underwater imaging model, and belongs to the technical field of underwater image processing. According to the method, deep learning pose estimation is combined with an underwater imaging model; firstly, a multi-frame underwater image sequence is collected as input, a deep neural network is constructed, and the network mainly comprises two core sub-modules: a pose estimation network; secondly, a three-dimensional reconstruction network is adopted, dense point cloud or voxel reconstruction is completed according to the predicted pose and image content, pose estimation and the three-dimensional reconstruction process are integrated in the same system, and overall joint optimization is achieved; and in combination with a self-adaptive underwater imaging model, modeling is performed on physical processes such as underwater illumination attenuation and scattering, so that the reality sense and the accuracy of a reconstruction result are improved. According to the method, high-quality three-dimensional reconstruction of images in a complex underwater environment is realized, and the method can be widely applied to ocean engineering, underwater robots, submarine topography surveying and mapping and underwater cultural relic protection.

The invention discloses a deep and far sea cage culture typhoon disaster prevention and after-disaster rapid recovery method, which comprises the following steps of: carrying a high-definition camera by using an unmanned aerial vehicle to aerially photograph a cage water surface structure before a disaster, acquiring an underwater image by an underwater robot through sonar hierarchical scanning and the camera, and generating structure data and fish school data before the disaster; weak points such as frames, nettings and anchor chains are analyzed through three-dimensional modeling and are repaired and reinforced, the stocking density is optimized in combination with typhoon information, the risk of facility damage and fish loss is reduced, structural data and fish school data are repeated after a disaster, and the pre-disaster standard serves as a reference to compare and evaluate the frame damage degree, the nettings damage degree, the anchor chain breakage degree and the fish school escape or death degree; and generating a graded repair scheme and a disaster relief plan. According to the method, the detection precision and the evaluation efficiency are improved through multi-device cooperation and data fusion, the damaged part is rapidly positioned, the recovery strategy is optimized, and an efficient disaster prevention and reduction and after-disaster rapid recovery solution is provided for deep and far sea cage culture.

The invention discloses an underwater robot multi-target dynamic resource scheduling method, and relates to the technical field of robot resource scheduling. The method comprises the following steps: constructing an underwater robot resource scheduling problem model, and establishing a target optimization model which comprises the steps of minimizing task maximum execution time, minimizing total power consumption and balancing load; generating reference points and distributing the reference points in a target space; a population is coded and initialized, a scheduling scheme is represented by adopting multi-segment chromosome coding, the scheduling scheme comprises a task allocation sequence and tasks allocated by a robot, and an initial parent population is generated in a random mode; a non-dominated solution set is obtained through evolutionary iteration, and the non-dominated solution set is obtained through reference point niche selection and combined evolutionary iteration of crossover and mutation operation based on an improved NSGA-III algorithm; and obtaining an optimal scheduling scheme, and screening an optimal compromise solution from the non-dominated solution set as the optimal scheduling scheme. According to the invention, a data-driven intelligent scheduling scheme is provided for collaborative operation of underwater robots in a complex marine environment.

The invention provides a hydro-junction water level adjusting method and system. Wherein the sediment deposition space-time atlas containing the sedimentary gradient is constructed and periodically corrected through scanning of an underwater robot and synchronous acquisition of satellite remote sensing data. A radar array is deployed at a bend section of a flood discharge channel, and vector distribution of a three-dimensional sediment flow velocity field is analyzed by using multi-frequency pulse signals. Through correlation analysis of the dynamic coupling sedimentary gradient and the flow velocity field, a correlation area of flow velocity sudden change and sediment deposition is accurately identified, and a constraint condition set including coordinate positioning and a flow-sediment matching rule is established. And in combination with real-time water level monitoring data, calculating and generating a dynamic adjustment parameter sequence of the opening degree of the flood discharge gate by using a constraint condition set, thereby realizing accurate regulation and control of the water level. According to the technical scheme, the flood discharge parameters are intelligently optimized, the water level of the hydro-junction can be accurately regulated and controlled, the sediment deposition problem of a high-sediment-content water area is effectively relieved, and safe operation and flood control efficiency of the junction are guaranteed.

The invention relates to the technical field of structure inspection, in particular to a bridge pier underwater structure inspection system based on an unmanned underwater robot, which comprises an inspection period adjustment module, an anomaly recognition module, an automatic supplementary shooting module, a damage evaluation module and a bridge pier anomaly evaluation module. According to the invention, bridge pier underwater structure inspection is executed through the unmanned underwater robot, the inspection period is dynamically adjusted according to the traffic flow, fine management of inspection work is realized, the inspection frequency can be adjusted according to actual use conditions and external influence factors, potential structure problems are effectively prevented and found, and the inspection efficiency is improved. The damage area can be quickly and accurately calibrated by image acquisition and automatic anomaly recognition performed by the unmanned robot, supplementary shooting is performed when necessary, the image quality is improved, the data reliability is ensured, and the influence of damage on the structural safety can be accurately evaluated by comparing the positions and overlap ratios of the damage area and the key bearing area of the bridge pier, so that the safety of the bridge pier is improved. And a scientific basis is provided for maintenance decision.

The invention discloses a cable underwater robot trajectory tracking control method for coping with ocean current disturbance, and belongs to the technical field of underwater robot trajectory tracking, and the method comprises the following steps: S1, constructing a kinematics and dynamics model of a cable underwater robot, and simplifying the kinematics model of the cable underwater robot; s2, combining speed information of a Doppler velocimeter sensor, designing a super-spiral expansion state observer, and compensating a cable underwater robot system; designing a self-adaptive super-spiral expansion state observer by combining a self-adaptive law; and S3, designing a self-adaptive super-spiral integral terminal sliding mode controller based on the self-adaptive super-spiral expansion state observer. The real-time dynamic performance and stability of the underwater robot in sliding mode control are improved, the buffeting problem existing in sliding mode control is solved, it is ensured that under the condition of ocean current disturbance, the ROV can still achieve accurate trajectory tracking, and the robustness is very high.

The invention discloses a drilling-grouting integrated underwater robot and aims to meet the requirement for precise operation in a complex underwater environment. The robot integrates the fin antenna, the hydraulic telescopic crawler belt, the hydrodynamic force wings, the bionic mechanical arm and the integrated drilling and injecting system, and has the multi-terrain adaptation, efficient navigation and autonomous operation capacity; through modular design and a high-strength pressure-resistant structure, the robot can complete tasks such as pipeline repair, resource exploration and infrastructure construction in a deep sea high-pressure environment; the core technology comprises a multi-mode propulsion system, intelligent sensing and navigation, AI optimization operation and dual-mode operation switching. Typical application scenes include submarine pipeline damage repair, deep sea mining area reinforcement sampling, cross-sea bridge pile foundation grouting and the like. The limitation that a traditional underwater robot is single in function and high in energy consumption is broken through, and the safety and efficiency of complex underwater engineering are remarkably improved.

The invention proposes an underwater target intelligent recognition system based on multi-sensor fusion, and relates to the technical field of underwater target recognition, and the system comprises an underwater robot which is provided with an optical sensor, an acoustic sensor, a three-dimensional laser sensor, and a water quality environment sensor; the data acquisition module is used for synchronously acquiring original multi-modal data and water quality parameters from the underwater robot; the data preprocessing module is used for generating preprocessed multi-modal data; the multi-modal feature fusion module is used for extracting each modal feature from the preprocessed multi-modal data and generating a fusion feature; the target recognition and positioning module is used for processing the fusion features by adopting a target detection network and outputting the category, the position and the confidence of the underwater target; and the intelligent analysis output module is used for comprehensively analyzing the information of the underwater target and generating an analysis result and visual data. According to the invention, the limitation of a single sensor can be overcome, and the underwater target can be accurately and stably identified by combining an intelligent algorithm.

The invention relates to the technical field of underwater robots and high-precision positioning, and particularly discloses a high-precision positioning system for collaborative operation of an underwater robot cluster. The system comprises a multi-source fusion positioning base station network module, a cross-medium cooperative positioning engine module, a cluster relative positioning subsystem module, a dynamic environment perception compensation module, an elastic positioning fault-tolerant system module and a cooperative positioning decision center module. An absolute positioning reference is constructed through a multi-source fusion positioning reference station network, a cross-medium cooperative positioning engine is combined to realize accurate pose calculation, a cluster relative positioning subsystem is utilized to establish a dynamic topological relation between robots, an environment perception compensation and elastic fault-tolerant mechanism is introduced, and finally resource configuration is optimized through a cooperative decision center. The method solves the problem of high-precision positioning of robot cluster collaborative operation in a complex underwater environment, and can be widely applied to the fields of ocean exploration, underwater engineering and the like.

The invention relates to a low-illumination underwater image enhancement method based on double-domain adaptive collaboration, and the method employs a multi-scale mixed attention mechanism to collaboratively integrate the spatial domain and frequency domain information of an image, and obtains the key feature information of the image. And on the basis of the obtained image key feature information, adaptive parametric convolution is used, convolution kernel parameters are dynamically adjusted by using a multi-branch convolution structure, and image dynamic key information is obtained. And a dynamic content perception Markov discriminator is used to cooperatively perceive space and frequency characteristics, and the authenticity of the enhanced image is discriminated, so that the fidelity of the texture is improved. And finally, optimizing the generator and the discriminator by using adversarial training, and finally outputting an enhanced underwater image by using the optimized generator. The method can be embedded into an underwater robot platform, image quality enhancement is achieved with fewer computing resources in a low-illumination complex water environment, image definition, contrast and color consistency are improved, the enhanced image is closer to human eye visual perception, and the visual perception capability and operation reliability of an underwater robot are remarkably improved.

The invention provides a multi-mode underwater detection robot. The robot comprises a body section assembly formed by connecting a plurality of cabin pipe assemblies and power cabin assemblies in series. Joints between the cabin pipe assemblies are connected through joint connecting pieces (7); a steering engine joint piece (3) is arranged at the joint connecting piece, a propeller propelling device (4) is arranged at the power cabin assembly, and when the underwater posture and the propelling working condition of the robot are adjusted, a steering engine of the steering engine joint piece applies force to the joint connecting piece to change the included angle of the joint connecting piece, so that the body section assembly is twisted, and the posture of the robot is converted into different modes; the propelling direction of the propeller propelling device is changed; the problems that an existing underwater robot is single in mode, incapable of adapting to complex environments, low in energy efficiency, difficult in structure maintenance and poor in function expansion capacity can be solved.

The invention discloses a method for improving channel traffic volume based on an underwater map, and belongs to the technical field of water transportation traffic, and the method comprises the following steps: S1, constructing an underwater high-precision map, deploying an unmanned ship and an underwater robot, and collecting underwater topographic data by using a multi-beam sounding technology to construct a high-precision underwater map; s2, establishing a dynamic channel planning model, collecting hydrological data and ship traffic data of different time and places of a channel by using a big data analysis and machine learning algorithm, and generating a dynamic channel use scheme; and S3, integrating a ship navigation and management system, developing ship navigation terminal equipment, and integrating the dynamic channel planning model with a monitoring system of a channel management department. According to the invention, channel resource utilization is optimized, intelligent dynamic planning is realized, ship passing efficiency, navigation autonomy, navigation safety and system stability and reliability are improved, channel management scientificity is enhanced, and cooperation of ships and management departments is enhanced.

The invention discloses an underwater robot real-time three-dimensional map reconstruction system and method fused with an SLAM algorithm, and belongs to the technical field of three-dimensional map reconstruction. By constructing a confidence coefficient system of three-dimensional points, a spatial trajectory fluctuation index and an inter-frame texture change rate are introduced as dynamic feature evaluation indexes; and a regression model is adopted to score and grade the severity of the false points, and differential processing of different grades of point clouds is realized in combination with a dynamic suppression mechanism, so that an unstable structure is effectively eliminated, and the stability and reliability of the sparse three-dimensional map are remarkably improved. Therefore, the positioning precision, the path planning capability and the task execution safety of the underwater robot in a complex environment are enhanced.

The invention discloses a multi-mode water-air-land cross-medium cooperative sensing system and method based on an MEMS (Micro Electro Mechanical System). The system comprises an underwater robot, a buoy station, an unmanned aerial vehicle and a ground control station, each node is provided with an MEMS sensor integrating acoustics, optics, magnetism, inertia and other modes, and data transmission and relay are achieved through acoustics, optical communication and radio links. And the ground control station carries out fusion processing, target identification and decision control on multi-source data through time synchronization and space registration, and issues a cooperative instruction to each node to realize a perception-decision-re-perception closed loop. The method comprises five steps of node deployment and calibration, multi-modal data acquisition, hierarchical transmission and sharing, adaptive weighted fusion and closed-loop cooperative control. According to the invention, global coverage, high-precision and multi-mode environment and target perception can be provided in a complex water-air-land environment, and the detection reliability and the communication efficiency are significantly improved.

The invention relates to a robot under-actuated motion control method under the condition of failure of partial propellers. The robot under-actuated motion control method comprises the following steps: acquiring multi-source sensor data of an underwater robot; based on the multi-source sensor data, a health state model of the propellers constructed in real time is adopted, the state of each propeller is monitored in real time, and fault information of the propellers is further obtained; based on the fault information of the thruster, regularly checking the state of the thruster, and further judging whether the thruster can move at six degrees of freedom or not, if so, obtaining a thrust output scheme without coupling torque through an improved particle swarm optimization algorithm to complete the under-actuated motion control process of the robot, and if not, obtaining a thrust output scheme without coupling torque through an improved particle swarm optimization algorithm to complete the under-actuated motion control process of the robot. If yes, the robot under-actuated motion control process is completed by coordinating the sine fluctuating motion of the joints of the robot. Compared with the prior art, the invention provides a fault-tolerant control scheme under various fault conditions, and has the advantages of higher fault-tolerant control precision and the like.

The invention provides a biologically inspired neural network affine transformation underwater robot cluster path planning method, and relates to the technical field of cluster underwater robot path planning. The method comprises the following steps: performing path planning on a cluster autonomous underwater robot AUV based on a biological inspiration neural network; based on the planned path, planning a cluster AUV formation pattern by using an artificial potential field method, and dynamically adjusting the formation pattern by using affine transformation; and carrying out path simplification on the planned path by adopting a Douglaspucker DP algorithm, and carrying out curve fitting on the simplified path by utilizing a third-order B spline. The method is suitable for the cluster AUV, and the path planning has the advantages of safe obstacle avoidance, short time consumption and short distance.

The invention provides an underwater multi-robot distributed elastic positioning method and system, and relates to the field of underwater robots. Establishing a kinematic model of the target robot and a measurement model of monitoring node fusion distance, depth and attitude information; when the state variation of the target robot exceeds a preset threshold value, communication is triggered; identifying a random delay state of a measurement signal by adopting a binary indicative function, dividing a scene according to a relationship between the current moment and the total time length of a task, and converting random delay information into a non-delay equivalent sequence; on the basis of the kinematic model, the measurement model and the non-delay equivalent sequence, delay channels are selected in different scenes to execute state prediction and elastic gain correction, and a state estimation value of the target robot is obtained; wherein the tight upper bound of the positioning error covariance matrix is constructed, and the optimal positioning gain is solved to iteratively optimize the elastic gain. By adapting time-varying delay and optimizing gain control errors, efficient cooperative positioning in a complex environment is ensured, and reliable pose information is provided for underwater tasks.

The invention provides an underwater robot obstacle avoidance system based on object capture. The method comprises the steps of collecting spatial visual information in a target area where the underwater robot is located in real time; acquiring multi-modal environment data in a target area where the underwater robot is located; constructing polarized light data in the multi-modal environment data into a water body scattering model by adopting a Mueller matrix to correct optical distortion in spatial visual information; acquiring a real-time motion state of the underwater robot through an inertial measurement unit; acquiring water flow disturbance data of the underwater robot, and correcting position drift caused by water flow disturbance in a real-time motion state by combining a water body flow model with the water flow disturbance data acquired in real time; constructing a dynamic risk field corresponding to the target area based on the spatial visual information, the corrected real-time motion state and sonar ranging data in the multi-modal environment data; and generating an obstacle avoidance strategy of the underwater robot in a future preset time period based on the dynamic risk field so as to realize dynamic obstacle avoidance.

The invention discloses an underwater propeller thrust distribution control method and system based on QP optimization, which are used for avoiding redundant power consumption caused by improper distribution under redundant propeller configuration and prolonging underwater operation time and endurance mileage of a robot. The method comprises the steps that a preset attitude control instruction of the underwater robot and actual attitude information fed back by a current sensor are acquired, total disturbance acting on the underwater robot is estimated in real time based on the active disturbance rejection control principle, and expected total control force and total control torque instructions are calculated; constructing a quadratic programming problem based on the total control force and the total control moment instruction; solving a quadratic programming problem to obtain a first thrust instruction of each propeller after optimization; whether a propeller fault exists or not is diagnosed in real time, and when the fault is detected, a control efficiency matrix or a physical limitation constraint in the quadratic programming problem is dynamically adjusted; and solving again based on the adjusted quadratic programming problem to obtain a second thrust instruction of each propeller under fault tolerance.

The invention discloses an underwater robot target identification and tracking evaluation method based on deep learning, and the method comprises the steps: obtaining a target optical image, a sonar echo signal and a flow field parameter, constructing multi-modal data, and carrying out the preprocessing and space-time alignment; target apparent features and flow field features are extracted, dynamic weight fusion is carried out, and a target identification result is obtained through classifier processing; constructing a trajectory prediction model based on the target identification result and the flow field dynamic characteristics, and obtaining an initial trajectory prediction result by combining historical trajectory data optimization; constructing an implicit flow field model, and performing joint training with the trajectory prediction model to obtain an optimized trajectory prediction model; and a trajectory prediction result output by the optimized model is obtained, deviation is calculated in combination with actual trajectory data, the physical constraint weight and the target identification parameters in the trajectory prediction model are dynamically adjusted according to the deviation, and high precision and strong robustness of underwater target identification tracking of the underwater robot can be effectively improved.

The invention discloses an underwater environment three-dimensional reconstruction method, device and equipment and a storage medium, and relates to the technical field of underwater robots, and the underwater environment three-dimensional reconstruction method comprises the steps: transmitting laser to a target object, and collecting a laser reflection image; extracting pixel coordinates of laser stripes from the laser reflection image; calculating a three-dimensional space coordinate of the laser point based on the pixel coordinate and a camera internal reference matrix; generating a control instruction according to the depth information of the three-dimensional space coordinates, and sending the control instruction to an underwater vehicle to enable the underwater vehicle to execute equidistant diameter circular motion and collect and feed back three-dimensional point cloud data under different poses; and fusing the three-dimensional point cloud data under different poses to obtain a target three-dimensional point cloud model, and completing three-dimensional reconstruction. According to the invention, three-dimensional reconstruction of the underwater environment can be efficiently and accurately realized at low cost, and powerful technical support is provided for underwater exploration and scientific research.

The invention discloses a fuzzy-control-based autonomous obstacle avoidance system for an underwater robot, and relates to the technical field of fuzzy control, which comprises the steps of realizing accurate perception and boundary correction of obstacles by analyzing sonar data, optical images and inertial navigation information, extracting a target position in combination with echo signal features, enhancing the accuracy of environmental data, and obtaining an autonomous obstacle avoidance result. A fuzzy K-nearest neighbor algorithm is adopted, target clustering, moving target classification and environmental impact factor calculation are carried out based on dynamic information of obstacles, so that obstacle avoidance path planning can adapt to different topographic conditions and obstacle distribution, the applicability of path calculation is improved, a Dikstra algorithm is adopted to demarcate a traffic area, and the obstacle avoidance path planning efficiency is improved. And combining path interference calculation and risk assessment, optimizing a path weight matrix, and ensuring that an obstacle avoidance path realizes an optimal decision at a balance point of safety and trafficability. The path optimization link is based on energy consumption calculation and propulsion power optimization, the navigation stability is improved, and the burden of a propulsion system is reduced.

The invention relates to the technical field of underwater robot cooperative control, and provides an elastic consistency control method and system of a multi-underwater robot system. The elastic consistency control method for the multi-AUV system comprises the following steps: discretizing and linearizing the multi-AUV system, introducing a performance index to describe the influence of communication topology on the dynamic behavior of the system, and calculating the elastic consistency of the multi-AUV system on the basis of a centralized optimal control method of a local decoupling system and a robust stability theory. And designing an elastic consistency controller and obtaining controller gain by solving a local algebraic Riccati equation so as to realize consistency control of multiple AUV system states. According to a sparse mode matrix spectrum of a distributed controller, a system stability condition is given, and efficient cooperation and stable operation of a multi-AUV system in a complex underwater environment are ensured. According to the method, the calculation and communication complexity is remarkably reduced, the method is suitable for a multi-AUV cooperative working environment, and a new technical solution is provided for cooperative control of the underwater unmanned system.

The invention relates to the technical field of underwater robots, in particular to an underwater robot cluster path planning method and system based on a dung beetle algorithm, and the method comprises the steps: obtaining a coordinate division region to construct an environment model, calculating and extracting an obstacle distance to fuse ocean current interference to generate a dynamic obstacle avoidance strategy, and evaluating the path speed similarity to generate a candidate path. And acquiring an offset angle difference, normalizing environmental factors to generate a correction path, and calculating a curvature fusion collision probability to generate a global path control instruction. According to the method, a dynamic model is constructed based on Cartesian coordinate division, the spatial analysis precision is improved, a sonar distance and ocean current interference are fused to generate a dynamic obstacle avoidance strategy, the trajectory smoothness is enhanced, candidate paths are screened by using vector similarity and Monte Carlo simulation, the collaboration is optimized, disturbance is reduced through coordinate offset and ocean current correction, and the accuracy of the dynamic obstacle avoidance strategy is improved. The curvature and collision probability are combined to balance safe energy consumption, and multi-dimensional parameter fusion improves cluster adaptability, precision, efficiency and energy utilization rate.

The invention discloses a water area ecological environment supervision system based on artificial intelligence, and relates to the technical field of ecological environment supervision, and the system comprises a pollution monitoring module which is used for obtaining the geographic information of a water area, building a plurality of water quality monitoring points, and obtaining pollution data; the reverse deduction module is used for acquiring a hydrological model and meteorological data of a water area and surrounding enterprise data and determining a suspected pollution source; the searching and reporting module is used for dispatching an underwater robot and an unmanned aerial vehicle to search the suspected pollution source according to the suspected pollution source, generating a searching report and sending the searching report to a supervision department; the ecological acquisition module is used for respectively calling the underwater robot and the unmanned aerial vehicle to carry out normalized patrol in a water area, constructing a biodiversity database and constructing a hydrology and water quality database; and the ecological visualization module is used for constructing a water area twinborn map and generating a visual ecological environment supervision map. The method has the effect of improving the efficiency and real-time performance of monitoring the ecological environment of the water area.

The invention discloses a variable-structure water-air amphibious robot and a motion control method. The robot comprises a water-air amphibious robot body, a variable-structure wing system and an electric control system. The robot body comprises a sealed cabin, a propeller fixing assembly, a fluid shell and a variable support tail cover. The variable-structure wing system comprises a driving gear transmission assembly, a two-section folding wing and a wing folding linkage assembly; the electric control system comprises a control core element, a visual module, a power supply module, a GPS module and an underwater depthometer module which are arranged in the cabin body; the motion control method is based on a deep reinforcement learning control method, control generalization ability is enhanced through teacher and student strategies and random domain, and the motion control method has more advantages in scenes such as inaccurate modeling and large external disturbance change. The structure of the variable-structure water-air amphibious robot integrates the structures of a rotorcraft, a fixed-wing aircraft, an underwater robot and the like, the modes of the wings can be switched, and efficient movement operation of the water-air amphibious robot is achieved.

The invention discloses an automatic deviation correction method and system for operation of an underwater robot, a medium and a product, and relates to the field of general control or adjustment systems.The method comprises the steps that a weld seam image is obtained, filtering parameters are adjusted according to the noise intensity of a local image in the weld seam image, and a noise reduction image is obtained; performing double-layer threshold segmentation on the noise-reduced image to obtain a binary image; setting a plurality of detection areas in the binarized image, determining an effective detection area, converting edge feature points in the effective detection area into coordinate data, and performing partition weighted fitting to obtain an initial weld direction; constructing a dynamic error band, taking the coordinate points outside the dynamic error band as candidate abnormal points, determining a final abnormal point in the candidate abnormal points in combination with continuity criteria of adjacent feature points, and re-fitting the remaining coordinate points to obtain a target weld direction; and the target welding seam direction is compared with the reference direction, and a graded deviation correction control signal is generated according to the deviation degree. According to the invention, the correction accuracy can be improved.