83 results about "Underwater noise" patented technology

The invention discloses an ocean wind power underwater noise monitoring method and system, and belongs to the technical field of ocean environment monitoring, artificial intelligence and autonomous systems.The ocean wind power underwater noise monitoring method comprises the steps that a digital twinborn model fusing multi-mode perception is constructed; deducing future states of the system and the environment based on a digital twinborn model to generate a predictive state sequence; solving and generating a cooperative control strategy according to a joint optimization target of data quality and energy consumption; and analyzing the cooperative control strategy to synchronously regulate and control the physical system. According to the method, a digital twinborn driven cognitive decision and autonomous control architecture is adopted, a high-fidelity digital twinborn body is constructed to carry out prospective and uncertainty deduction on a physical system and a marine environment, and a closed-loop decision is carried out by taking risk-income optimization as a principle; the monitoring system can have long-term, autonomous and online self-improvement and risk avoidance capabilities, and the reliability of monitoring data in a complex marine environment and the full-life-cycle autonomous operation level of the system are remarkably improved.

The invention relates to an underwater target detection method based on a pulse neural network, which can realize underwater target detection with high precision and low power consumption. By fusing the cross-stage partial network and the YOLO architecture, the problem of pulse degradation is effectively solved, and the feature extraction capability of the model is enhanced; in order to solve the problem of underwater noise interference, the pulse-based underwater image denoising method is designed, only integer addition is used in the method, a pulse neural network structure can be seamlessly embedded, and the quality of a feature map is enhanced; in order to solve the problem that a traditional normalization method is low in precision in the spiking neural network, separate batch normalization is provided, by independently normalizing a feature map in multiple time steps and optimizing a residual structure, the time dynamic state of the SNN can be effectively captured, and the detection precision is improved. The network shows excellent performance in underwater target detection, and compared with an artificial neural network of the same scale, the network has higher performance and lower energy consumption.

The invention provides an underwater plosive sound analysis method and device, and relates to the technical field of underwater noise analysis, and the method comprises the steps: obtaining a plurality of sound wave signals received by a monitoring point; respectively analyzing the actual propagation time and the actual signal intensity of each sound wave signal; based on a preset sound velocity profile model, calculating sound wave propagation velocities of different depths of a preset water area; performing prediction path analysis based on the sound wave propagation speed to obtain a plurality of prediction paths; according to the actual propagation time and the actual signal intensity, performing propagation time matching and signal intensity matching on the plurality of prediction paths, and determining a target prediction path; based on the target prediction path, performing blasting source position calculation to obtain a predicted blasting source position; and if it is judged that the predicted blasting source position is consistent with the preset blasting source position, determining that the target predicted path is a blasting sound propagation path. According to the method, the propagation path of the underwater plosive sound can be analyzed.

The invention provides a multi-parameter-based offshore piling underwater sound source level prediction method and device.According to the method, multiple sets of historical monitoring data are obtained, each set of historical monitoring data comprises multi-parameter information and a corresponding single impact sound exposure level, and the multi-parameter information comprises impact energy, hammer weight, pile diameter and water depth; establishing a prediction relation model according to the multiple groups of historical monitoring data; and acquiring multi-parameter information corresponding to the single impact sound exposure level of the to-be-predicted project, and substituting the multi-parameter information into the prediction relation model to obtain the single impact sound exposure level of the to-be-predicted project, so that the impact energy, hammer weight, pile diameter and water depth of the to-be-predicted project, which are easy to acquire, are input, and the single impact sound exposure level of the to-be-predicted project is obtained. And the calculation is simple and convenient, so that the sound source level of the underwater noise generated by piling can be quickly predicted in the early stage of a project.

The invention discloses an underwater target tracking method based on multivariable deflection Laplace distribution modeling, and the method comprises the steps: collecting the motion state data of an underwater target through a sonar sensor, and obtaining the target position measurement information through a coordinate system conversion mode; the method comprises the following steps: establishing a motion model of an underwater target, determining a state-space equation of the underwater target, analyzing mathematical characteristics of underwater noise, and establishing a measurement model of the target under non-Gaussian noise; the method comprises the following steps: modeling non-Gaussian noise on the basis of multivariable skew lapace distribution, solving a mixed parameter, a shape parameter and a scale matrix of the noise under a variational Bayesian framework, iteratively updating a target state and a noise covariance matrix, iteratively updating an estimated target motion state by using Kalman filtering, and after the number of iterations is reached, calculating the target motion state according to the estimated target motion state. And outputting the estimated values of the underwater target position and speed and the estimated value of the covariance matrix. The method has better robustness and estimation precision, does not need to select a degree-of-freedom parameter, and can be better applied to target tracking.

The invention relates to an underwater multi-AUV (Autonomous Underwater Vehicle) attack and defense game method based on deep reinforcement learning under imperfect information, and the method comprises the steps: firstly, carrying out an attack and defense game based on multi-AUV cooperation under an incomplete information condition of a marine environment; secondly, considering underwater noise interference, and establishing an underwater environment model; then, AUV dynamics and a six-degree-of-freedom equation are established; and then, setting a mixed mode of underwater acoustic communication and short-distance optical communication in the AUV cluster, based on an MADDPG method, improving an experience playback mechanism, optimizing a reward function, and establishing a learning framework based on an Actor-Critic structure, thereby solving the game problem under imperfect information. According to the method, the intelligence, informatization and unmanned operation of ocean protection can be effectively improved, and a theoretical foundation is laid for underwater applications such as underwater acoustic engineering protection and sea battlefield combat.

The invention belongs to an image processing technology, and particularly relates to an underwater image enhancement method and system based on combination of multiple convolution models and an attention mechanism, and the technology is formed by combination of a DIAM model and a Shallow-UWnet improved model. The DIAM model is mainly used for carrying out color restoration in a mode of complementing hue weakening and non-uniformity of the underwater image, and can solve the problems of underwater noise, atomization and the like. According to the Shell-UWnet improved model, on the basis of Shell-UWnet, a highest pooling layer and an Inc multi-convolution module are added, the highest pooling layer effectively reduces network parameters and calculation overhead through dimension reduction operation, the operation efficiency is improved, the key feature extraction capacity is enhanced, detail loss is avoided, Inc can extract features from different scales, and the algorithm is simple and convenient to operate. And the feature capture capability of the model is enhanced.

The invention relates to the technical field of underwater robot sonar data processing, in particular to an underwater robot sonar data processing method and device based on a convolutional auto-encoder, and the method comprises the following steps: S1, building a database, S2, carrying out standardization processing, S3, constructing a convolutional auto-encoder module, S4, training a preprocessing network, and S5, extracting features and reducing noise. According to the method, the convolutional auto-encoder model is adopted, the internal structure of the data can be automatically learned from the original data, good denoising and feature extraction capabilities are achieved, underwater noise, echo and other irrelevant information can be effectively removed, and therefore the definition and precision of the data are improved; space and depth information can be effectively enhanced, and obstacles and dangerous areas in the underwater environment can be identified more accurately; the method provided by the invention is an unsupervised learning method, can adapt to automatic optimization and processing flows of different underwater environments, and further enhances the adaptability, robustness and generalization ability of the underwater robot.

The invention relates to the technical field of underwater noise simulation, and discloses an array system for simulating ship noise based on a gas explosion sound source, which comprises a unit console, a display console, an air compressor unit, a valve control box, a gas cylinder group, a comprehensive controller, a gas explosion sound source array and a ship power station, and is characterized in that the ship power station provides power for the air compressor unit and the comprehensive controller; the unit console is used for controlling the air compressor unit, and the display console is used for performing interface display and operation parameter setting on the comprehensive controller; the air compressor unit receives a control signal of the unit console and generates compressed air flowing to the valve control box; the valve control box receives a control instruction of the comprehensive control instrument and conveys compressed gas to the gas explosion sound source array through the gas cylinder set. The gas explosion sound source array further receives a control signal of the comprehensive control instrument and works according to the control signal. According to the invention, the impact noise of the ship engine can be simulated underwater, and the purpose of simulating the real working process of the ship diesel engine is achieved.

The invention relates to the technical field of noise evaluation, and discloses a method, device and equipment for evaluating the influence of underwater noise on marine organisms and a medium, and the method comprises the steps: extracting underwater sound data to obtain a wind power plant operation characteristic signal and a marine organism sound signal; performing spectrum analysis on the marine organism sound signals; if the target marine organism judged according to the spectral analysis result is the preset marine organism, determining the distance between the target marine organism and the preset monitoring point according to the marine organism sound signal; acquiring initial noise intensity corresponding to the wind power plant operation characteristic signal; determining target noise intensity according to the distance and the initial noise intensity; and evaluating the influence of the underwater noise generated by the operation of the wind power plant on the target marine organism according to the target noise intensity. By determining the distance between the target marine organism and the preset monitoring point and correcting the initial noise intensity, the accuracy of evaluating the influence of the underwater noise on the marine organism is improved.

The invention discloses a needle head bubble generator device and a sound insulation measurement method, which can reduce noise propagation by generating a bubble curtain in a needle head device and utilizing the sound absorption characteristic of the bubble curtain. The method mainly comprises the following steps: determining parameters of a needle device, and constructing a bubble curtain model of the needle device; a high-speed camera is used for shooting clear pictures of the bubbles, and an edge detection algorithm is used for solving a bubble diameter distribution rule; the emission frequency is changed, and sound insulation optimization is carried out on the resonance radius of the bubble curtain; the layer number and the inflation flow of the needle head device are changed, and sound insulation optimization is carried out on the volume concentration of the bubble curtain. The method has the advantages of being easy and convenient to operate, wide in application range, remarkable in noise reduction effect and the like, and has application value for various underwater noise control occasions.

A device and operating method for gliding and charging a UUV are disclosed. The device includes a UUV with a lifting system installed mid-hull, which is connected to a rotatable folding wing. When not in operation, the wing conforms to the shape of the UUV's hull; when in operation, it extends out of the UUV's hull via the lifting system and unfolds. Addressing the issues of UUVs being limited in their operational range by energy consumption during underwater operations, the low charging efficiency of UUVs using self-contained solar panels, the ease with which they can be detected during charging, underwater noise from various propulsion methods, and speed limitations when using unpowered gliding, this device enables unpowered gliding through sensitive waters and navigation in non-sensitive waters, reducing the probability of target detection. Equipped with a retractable folding solar charging panel, it ensures efficient energy replenishment and maximizes the single-unit endurance, enabling long-term independent underwater operation. Compared to traditional UUVs, it offers advantages such as energy saving, high stealth, and high maneuverability.

The invention provides a low-flow-resistance underwater sound insulation device which is characterized by comprising a plurality of air bags with the longitudinal sections in the shape capable of being densely laid. A set gap is kept between every two air bags, and the air bags are perpendicular to the water surface The device has the advantages that the broadband sound insulation performance is excellent, the main frequency band of underwater noise measurement is covered, and the influence of various noise sources on the test environment can be effectively inhibited; the hydrodynamic compatibility is good, the through-flow design of air bag array arrangement can maintain low flow resistance while realizing sound isolation, and significant disturbance to a test environment is avoided; and the modular design of the air bag is convenient to install, maintain and expand, and large-scale deployment in various water area environments is facilitated.

The invention provides a forecasting system and method for forecasting underwater radiation noise after ship equipment replacement. The system comprises a data acquisition module, a data processing module and a noise forecasting module, and the data acquisition module acquires various related data before and after equipment replacement to provide comprehensive support for subsequent calculation; the data processing module performs a series of calculation processing on the acquired data to ensure that the data is accurate and available; the ship underwater radiation noise prediction system is compact and reasonable in structure, and the existing technical problems can be effectively solved. According to the method, through the steps of experimental testing before equipment replacement, transfer function construction, acceleration admittance matrix acquisition and the like, the influence of various factors on noise after equipment replacement is comprehensively considered, and specific operations of each step, such as construction of a base-underwater noise transfer function, construction of an equipment-base transfer function and the like, are explained in details, so that the noise level can be accurately forecasted, and the noise level can be accurately predicted. And powerful support is provided for making a ship equipment replacement scheme.

This invention discloses a method for enhancing weak underwater signals using stochastic resonance, belonging to the field of underwater acoustic engineering and signal processing technology. It is used for enhancing weak underwater signals, including constructing and preprocessing an integrated detection and communication signal; constructing a linear potential well wall piecewise tristable stochastic resonance system; inputting the preprocessing results into the linear potential well wall piecewise tristable stochastic resonance system; optimizing the parameters of the linear potential well wall piecewise tristable stochastic resonance system using an improved moss growth optimization algorithm; numerically solving the system's motion equations using the fourth-order Runge-Kutta method to obtain the enhanced output signal; and performing frequency recovery on the enhanced output signal to generate an output signal with a complete linear frequency modulation structure. This invention fundamentally solves the problems of output saturation and difficulty in improving the signal-to-noise ratio in traditional stochastic resonance systems under strong noise environments through the linear potential well wall piecewise tristable structure, significantly improving the system's adaptability to complex underwater noise.

The invention discloses an offshore wind plant underwater noise real-time estimation method and device, a medium and a product, and relates to the field of underwater noise estimation.The method comprises the steps that an underwater noise sound pressure signal, a three-axis vibration acceleration time domain signal, an air sound pressure time domain signal, the wind speed, the cabin rotating speed and active power output are collected; segmenting each time domain signal, performing fast Fourier transform on each time domain signal in each time window, estimating power spectral density, dividing the power spectral density into a plurality of frequency bands, and calculating an underwater sound pressure level, a structural vibration acceleration level and an air sound pressure level of the frequency bands according to the power spectral density; training in each operation condition interval to obtain a final inversion coefficient set corresponding to each frequency band in each operation condition interval; and according to the operation condition interval to which the current environment condition belongs and the inversion coefficient set of the frequency band, calculating the underwater sound pressure level estimation value of each frequency band, and generating underwater noise spectrum distribution in a set frequency range, so that the underwater noise of the offshore wind plant can be efficiently and accurately estimated.

The invention discloses a calculation method and application of an underwater random bubble curtain pressure value simulation model. The method comprises the following steps: firstly, establishing a bubble generation model based on random characteristics, and realizing random distribution of bubble positions and sizes through an MATLAB program and a randperm function; then, a fluid and bubble dynamics bidirectional coupling model is constructed, and pressure field association is established by keeping grid sizes consistent; generating a grid by adopting an SALE method, and combining the bubble model, the target plate model and the fluid model through open and import commands; and finally, carrying out numerical calculation by adopting an LS-DYNA solver, iteratively adjusting parameters based on an optimization algorithm, and outputting a pressure value simulation result. According to the method, full-process automatic modeling is realized, the efficiency is improved by more than 50%, million unit-level large-scale calculation is supported, the model precision is high, the universality is high, and the method can be widely applied to simulation analysis and device optimization of underwater noise reduction, antifouling and oxygenation scenes.

The application discloses a kind of ship underwater noise tracking monitoring device and method, the method includes the following steps: 1) the monitoring device is installed to ship;2) keep ship in the channel in the state of stop, each hydrophone is monitored in the channel ambient noise, and data acquisition equipment records ambient noise signal;3) according to test condition, ship equipment is normally opened or runs;4) ship is normally navigated in the channel, and data acquisition equipment records the sound wave signal measured by hydrophone;5) based on the ambient noise monitored in step 2), the sound wave signal measured by each hydrophone is removed ambient noise, and all the noise-removed underwater radiated noise signals of the ship are analyzed and processed.The application is arranged with tracking measurement hydrophone array around the ship, and the ship is monitored under various conditions in line with the actual situation, with the real ship flow field around the ship during navigation, and the monitoring result has high accuracy.

The invention discloses an underwater target track measuring and calculating method, a storage medium and a terminal, belongs to the technical field of underwater target detection and positioning, and solves the problems that underwater target track points are sparse and positioning errors exist due to underwater noise, low sampling frequency and the like, and further tasks such as underwater target position estimation, tracking and track prediction are affected. According to the method, a training set and a test set are constructed through continuous three-dimensional trajectory data collected at high frequency, coordinates are converted through a principal component analysis method, samples are enhanced, trajectory rules are learned through a multi-layer perceptron model, a mean value is obtained through multiple times of prediction of a sliding window, and a complete underwater target trajectory is obtained. The track law is learned by using the neural network, so that the prediction is more accurate; pCA is used for converting samples, so that the model precision and generalization ability are improved; enriching samples through data enhancement; and a sliding window is used for multiple predictions to obtain an average value to improve prediction precision.

The invention provides a large and medium-sized water surface ship radiation noise generation and control method, system and device covering a scale effect, and a storage medium, and belongs to the field of large and medium-sized water surface ship radiation noise prediction and analysis. The difference of contribution degrees of machinery, hydrodynamic force and propeller noise to underwater radiation noise in different navigation states is considered, a multi-modal noise separation method is adopted to separate coupling components, and a ship underwater radiation noise source is simplified into a noise source model along a ship body; a ship scale effect analysis and multi-resolution time-frequency analysis technology is introduced, and a generation mechanism of ship radiation noise is simplified into underwater radiation noise caused by shell vibration caused by the fact that a plurality of distributed low-frequency loads act on a ship structure; large and medium water surface ship radiation noise passing characteristic rules are disclosed, and a ship navigation state underwater radiation noise simulation theoretical model is established. According to the method, an effective means is provided for generation and control of underwater noise of the ship, and optimization of ship design and reduction of underwater radiation noise are facilitated.

The invention discloses an offshore wind plant underwater noise monitoring method, which comprises the following steps: equipment preparation and assembly: preparing an underwater noise monitoring unit for monitoring, the underwater noise monitoring unit being composed of a hydrophone, a rope, a buoy and a balancing weight; measuring an underwater noise background value: adopting one underwater noise monitoring unit to carry out underwater noise monitoring at a position which is in the upwind direction of the offshore wind power plant and is a certain distance away from the boundary of the site; influence range and attenuation characteristic measurement: respectively aiming at a single unit, two units and a plurality of units in the offshore wind power plant, adopting the underwater noise monitoring unit to carry out underwater noise monitoring; data collection and processing: recovering all the arranged underwater noise monitoring units, exporting monitoring data, analyzing and processing the monitoring data, and further quantitatively evaluating the influence range and attenuation law of underwater noise; according to the invention, systematicness, reliability and analyzability of underwater noise monitoring data in the operation period of the offshore wind plant are improved.

The invention discloses a ship full-band underwater noise forecasting method based on multi-forecasting method fusion, and relates to the technical field of ship noise forecasting. According to the method, underwater noise forecasting is carried out on a target ship through multiple different underwater noise forecasting methods. And then inputting the plurality of obtained underwater noise forecasting results into a fusion forecasting network constructed based on a neural network model, and performing attention enhancement on spectrum signals of different characteristic frequency bands in the plurality of underwater noise forecasting results by the fusion forecasting network based on a flexible attention mechanism to realize data fusion. And obtaining a full-band underwater noise fusion forecast result. The method can have the advantages of various underwater noise forecasting methods, thereby being beneficial to improving the reliability and accuracy of ship full-band underwater noise forecasting.

This invention proposes a method for underwater noise recognition based on a continuous wavelet transform and an improved residual neural network. First, a variational modal decomposition algorithm is used to decompose the collected vehicle self-noise signal. The decomposed modal components are reconstructed based on the Pearson correlation coefficient, thereby removing redundant information from the self-noise signal. A continuous wavelet transform is then performed on the reconstructed self-noise signal to obtain its corresponding time-frequency image, thereby extracting the features of the self-noise signal. Finally, an attention mechanism is used to improve the residual network, constructing a residual neural network based on the attention mechanism. The time-frequency image of the self-noise signal is input into the network for training, thereby achieving recognition and classification of the self-noise signal. This invention addresses the problem of one-dimensional signals containing less information in feature extraction of underwater vehicle self-noise signals and avoids the precision loss of deep neural networks, effectively improving the accuracy of identifying different types of noise.

According to the sparse denoising and feature extraction method and system for the active sonar received signal of the underwater high-speed small target and the related device provided by the invention, a three-subnet model joint learning model is constructed and is used for sparse denoising and sparse feature extraction of the active sonar received signal of the underwater high-speed small target; the model comprises a target subnet model, a noise subnet model and a reverberation subnet model which are used for extracting underwater high-speed small target echo signal features, underwater noise signal features and underwater reverberation signal features respectively, feature transmission and fusion are carried out on the three features through an information interaction module, noise suppression is achieved, echo signals are reserved, and the target subnet model, the noise subnet model and the reverberation subnet model are integrated. And finally reducing the signal into a time domain signal. The method can provide sparse denoising and sparse feature extraction capabilities of active sonar received signals under the condition of low signal-to-noise ratio, is suitable for active sonar received signal processing of underwater high-speed small targets in a complex marine environment, and provides echo signals with higher signal-to-noise ratio for subsequent detection, positioning, identification, tracking and the like of the underwater high-speed small targets.

The invention discloses a pool vibration isolation and noise reduction design system based on dynamic characteristic analysis. The system comprises a sensor array, a data acquisition and preprocessing module, a dynamic characteristic analysis module, an active actuator array, an underwater acoustic control module and a central controller. Vibration and noise signals of a pool structure are collected in real time through a sensor, and dynamic characteristic parameters such as the inherent frequency and the damping ratio of the structure are analyzed online through algorithms such as random subspace recognition; based on the parameters, the central controller respectively drives the actuator array and the underwater secondary sound source by adopting a self-adaptive control algorithm and an acoustic active control algorithm to generate a reverse acting force and an anti-noise signal so as to cooperatively suppress structural vibration and underwater noise; according to the invention, the defect that the traditional static design is difficult to adapt to the dynamic change of the fluid-structure interaction system is overcome, intelligent and self-adaptive integrated cooperative control of vibration reduction and noise reduction is realized, and the control precision, stability and efficiency are remarkably improved.

The invention relates to an underwater noise real-time detection and sonar biological recognition integrated system, which is characterized in that an audio acquisition array is arranged underwater to acquire audio data of a plurality of characters, then frequency division is carried out to roughly divide objects of different sound sources, and in order to reject the audio data mixed with similar frequencies, the audio data of different sound sources can be obtained; according to the invention, a time period in which the audio energy is stable is reserved as an effective time period by using the distribution characteristics of the energy, and a time period in which the energy is sharply increased is removed. And then finding the audio receiving time difference of a plurality of audio acquisition positions by using numerical coding, and positioning by using a time difference of arrival method. Positioning a synthetic track and extracting motion features, and performing rough classification by using the motion features; and finally, performing more accurate biological category identification by using the effective audio data and the coarse classification labels. Compared with a traditional identification method, the method can effectively remove interference data, and is more accurate in identification.

The present application relates to the technical field of aquatic animal breeding and protection, and particularly relates to a water structure component for protecting sound-sensitive aquatic animals and a use method thereof, which comprises a cusp array composed of a plurality of cusp units and used for absorbing underwater noise, and a base fixedly connected with the cusp array and used for mounting the water structure component on an existing structure. In the component, the cusp units are designed according to specific length and height-width ratio and are made of foamed polymer materials, which can not only efficiently guide and dissipate sound waves, but also have excellent structural strength and durability and can effectively resist water flow scouring. The cusp array and the base are integrally connected through grouting forming, and the mechanical interlocking structure formed by the bottom occlusion hemispherical holes greatly enhances the integrity. The steel reinforcement reserved holes arranged on the base can enable the water structure component to be connected with the existing structure in a permanent and reliable manner through mature steel bar planting and secondary grouting processes, so that long-term engineering safety and noise reduction stability are ensured.

The present invention discloses a SLAM method for underwater motion drift and noise estimation based on maximum a posteriori estimation, comprising: 1) estimating the motion drift of an underwater robot based on EKF-SLAM; 1.1) initializing the robot SLAM system state; 1.2) constructing sampling points based on EKF-SLAM state estimation; 1.3) updating the covariance matrix based on the state estimation error; 2) updating the motion drift of the underwater robot based on maximum a posteriori probability; 3) estimating underwater noise parameters based on EKF-SLAM; 3.1) initializing the underwater noise parameters; 3.2) estimating the underwater noise parameter covariance matrix; 3.3) updating the covariance matrix based on the state estimation; and 4) updating the underwater noise parameters based on maximum a posteriori probability. This method improves the state prediction portion of the existing EKF-SLAM method, combines estimation of motion noise and observation noise in the system model, adaptively filters the Gaussian distribution noise variance of the system model, and then performs SLAM estimation, thereby improving underwater self-positioning accuracy by approximately 20%.

The present invention relates to the field of manipulator control technology, and in particular to a method for controlling an underwater mobile manipulator based on load estimation, comprising: establishing a kinematic model and a dynamic model of the manipulator based on the motion data of the manipulator; determining whether the degree of environmental interference meets requirements based on underwater noise, and determining an adjustment method if it does not meet the requirements; obtaining the current position and posture; calculating the output parameters of an integral sliding mode controller and the compensation output parameters of an extended state observer; calculating the torque of the manipulator; calculating the thrust values ​​of several propellers of the manipulator; calculating the load mass of the manipulator; updating the position and posture of the manipulator based on the thrust values ​​and the load mass of the manipulator, and obtaining the updated position and posture. The present invention improves the operational stability and control accuracy of the manipulator.