1100results about "Seismology" patented technology

Systems and methods for monitoring and evaluating time-series real-time environment data to create a high-resolution, high-fidelity actual (e.g., nowcast) and predicted (e.g., forecast) representation of an environment of interest. In some aspects, the system comprises instructions to obtain a set of real-time environment measurements stored in a data repository corresponding to a time-series capture of environment data across an observational time period, identify one or more precursory signals within the set of real-time environment measurements, determine at least one anomalous precursory signal from the one or more precursory signals that exceeds the corresponding signal threshold, generate a time-series representation of an actual environment state across the observational time period based on the at least one anomalous precursory signal and the set of real-time environment measurements, and display, at a user interface, the generated time-series representation of the actual environment state.

The invention discloses an earthquake prediction method and system based on multi-physics coupling and quantum optimization, and relates to the technical field of earthquake prediction, and the method comprises the following steps: obtaining earth surface deformation observation data, geomagnetic observation data, gravity gradient data, and infrasonic wave monitoring data, and carrying out the preprocessing; performing multi-physics field coupling calculation on the preprocessed earth surface deformation observation data, geomagnetic observation data, gravity gradient data and infrasonic wave monitoring data; optimizing the fault network by adopting a D-Wave quantum annealing technology to find a lowest energy state to obtain a quantum optimization result; and generating an earthquake probability distribution diagram, an earthquake magnitude-time window table and a fault stress field three-dimensional model, thereby greatly expanding the breadth and depth of data, and remarkably improving the comprehensiveness of earthquake prediction.

The invention provides a high-negative-pressure gas extraction drill hole full-life-cycle efficiency evaluation method, and belongs to the technical field of mineral exploitation. A ground stress distribution model is established through microseism monitoring to determine drill hole arrangement parameters; a distributed optical fiber sensing system and a multi-parameter gas flow meter are used for monitoring the stress change around a drill hole and gas extraction data in real time, the coal fracture development degree is obtained in combination with sound wave testing, and all the data are input into a physical numerical value coupling model to calculate a permeability dynamic evolution curve. Establishing a permeability influence coefficient matrix and determining a key parameter weight, constructing an evaluation index system including the extraction amount, the permeability change rate, the drilling life and the coverage range, and performing multi-objective optimization configuration by applying a Pareto optimal solution theory. The technical problem that full-life-cycle dynamic monitoring and comprehensive efficiency evaluation of the high-negative-pressure gas extraction drill hole are lacked at present is solved.

The invention discloses a data center seismic isolation performance detection method based on AI intelligence, and particularly relates to the technical field of seismic isolation performance detection. Earthquake vibration characteristic data are collected in real time on a data center building and equipment, earthquake input characteristics are extracted in combination with a historical earthquake wave database, a seismic isolation performance prediction model is constructed and trained, a foundation shear wave velocity gradient abnormal value and a seismic isolation layer dynamic displacement response deviation ratio are further extracted, a deviation index is comprehensively calculated, and a seismic isolation performance prediction model is constructed and trained. And classification processing is carried out according to the deviation level, especially under the condition of a middle deviation level, foundation physical parameters are dynamically updated, and the model is optimized, so that the accuracy of the prediction model and the self-adaptive capability of the system are remarkably improved, and the shock isolation safety and the continuous operation capability of the data center in a strong shock environment are effectively guaranteed.

The invention discloses a railway beam bridge anti-seismic toughness evaluation method and system, relates to the technical field of bridge engineering structures, and solves the technical problem that the prior art lacks an anti-seismic toughness evaluation method suitable for a railway bridge. The method comprises the following steps: establishing a full-bridge nonlinear finite element model to carry out vulnerability analysis; according to a vulnerability result, weighting and quantifying a component damage state and a passage function damage state by adopting a damage probability; determining post-earthquake repair time and a function recovery value, obtaining a post-earthquake bridge passing function recovery curve, finally obtaining a toughness index of the bridge, and dividing the function state of the bridge into three grades; according to the method, a whole process solution of'bridge design parameter-nonlinear time history analysis-probability earthquake demand model-vulnerability analysis-passage function recovery-toughness index-earthquake resistance toughness three levels' of the railway beam bridge is provided, and reference is provided for pre-earthquake design and post-earthquake emergency repair decision.

The invention relates to the technical field of geological disaster monitoring, in particular to a geological disaster monitoring method and system based on a distributed optical fiber vibration sensing system, and aims to solve the problem of how to solve the interference of the change of an optical fiber and the change of an external environment on a geological disaster prediction result in optical fiber transformation. In order to solve the problem, the geological disaster monitoring method provided by the invention comprises the following steps: determining an area to be expanded according to a monitoring demand and a system distribution path of an original optical fiber; determining an actual distribution path of newly added optical fibers according to the landform characteristics and the geological risk level of the to-be-expanded area; after the newly-added optical fiber is installed, correcting the characteristic data according to the loss condition of the original optical fiber and the transmission characteristics of the newly-added optical fiber, and constructing a prediction model according to the corrected data; and combining the prediction model with the environment information, and determining the type of a disaster occurring in the target area.

The invention belongs to the technical field of data acquisition, and provides an underground pipeline detection data acquisition method and system, and the method comprises the following steps: collecting underground pipeline drawing data of a target region, and determining the information parameters of underground pipelines; performing field exploration, marking a ground outcrop point of the underground pipeline, and preliminarily judging the trend of the pipeline; detecting the underground pipeline through an electromagnetic method or a ground penetrating radar, and determining the position and the burial depth of the underground pipeline; reflected and scattered sound wave signals propagated through the internal fluid of the underground pipeline are collected through sound wave detection, and the state of the internal fluid is evaluated; fusing different detection data to generate an underground pipeline space model; by comprehensively using the electromagnetic method, the ground penetrating radar and the sound wave detection technology, the position, the burial depth and the internal fluid state information of the underground pipeline can be comprehensively collected, the physical position of the pipeline is determined, the state of the internal fluid of the pipeline is evaluated, and the accuracy and the reliability of the detection result are effectively improved.

The invention relates to a computer-implemented method for identifying at least one area of interest from a region of interest. The method comprises: obtaining a plurality of geo-information data over the region of interest for a geological time interval, the geo-information data comprising at least carbonate reference locations wherein marine carbonate sediments are observed, obtaining at least one paleo-digital elevation model of the region of interest, providing an Earth system model with a plurality of input sets of modeling parameters and obtaining output sets of paleoenvironmental parameters, determining a plurality of representative paleoenvironmental parameters for each carbonate reference location from the plurality of output sets of paleoenvironmental parameters, grouping at least a part of the carbonate reference locations into at least one group of carbonate reference locations defined as carbonate factory, and computing a susceptibility of occurrence of the carbonate factory.

The invention discloses a high gas impact ground pressure mine island working face blasting pre-pressure-relief scour prevention method which comprises the steps that a scour inducing key layer in a top plate above an island working face is determined, a top plate pre-splitting pressure relief roadway with specific construction parameters is arranged in the scour inducing key layer, and by means of the mode, elastic deformation accumulated in the scour inducing key layer can be effectively reduced, and one-time pressure relief can be achieved; then, a plurality of blast holes with specific parameters are constructed from the roof presplitting pressure relief roadway to the inducing key layer, finally, the blast holes are sectioned according to the hole depth and sequentially blasted, energy release of each time of blasting is accurately controlled through sectioned blasting, meanwhile, continuous pressure relief is achieved in time and space through sectioned blasting, and the blasting efficiency is improved. The stress of the inducing key layer is effectively released to realize secondary pressure relief; through mutual cooperation of primary pressure relief of the roof presplitting pressure relief roadway and secondary pressure relief of segmented blasting of the blast hole, the mine roof type impact risk facing high gas, an island working face and mining rock burst is effectively reduced, and subsequent safe mining of the island working face is guaranteed.

The invention discloses an underground space surrounding rock excavation damage area testing method, which belongs to the technical field of rock mass damage testing, and comprises the following steps: sampling rocks with different depths, carrying out ultrasonic testing, and determining rock sample attenuation characteristics and wave velocity; determining a comprehensive damage index range of the rock samples with different damage grades; then, sensors are arranged in the excavation process to collect ultrasonic and acoustic emission data; then, an initial calculation model is constructed, and a surrounding rock three-dimensional wave velocity model is obtained through inversion of collected data; and finally, determining a damage grade according to the comprehensive damage index, and marking in the model to realize damage area testing. According to the method, the mapping relation between the damage degree and the wave velocity change is established, the comprehensive damage index range threshold value of different subareas is provided, quantitative and gradable classification is achieved, the three-dimensional wave velocity model is inversed based on test and monitoring data, and three-dimensional presentation and grading labeling of the damage area are achieved.

The loadable AUV is applicable to an ice-piercing detector for detecting subglacial waters in Antarctica and extraterrestrial planets, including an ice-piercing detector and an AUV. The ice-piercing detector includes an ice anchor, a tail cabin, and an AUV storage cabin that are fixedly connected in sequence along the axis. A thermal melting drill bit is eccentrically rotatably connected to the end of the AUV storage cabin. An AUV winch system is installed in the AUV storage cabin. The end of the cable of the AUV winch system is connected to a lifting and docking system. The lifting and docking system includes a limit disk. A cable connector is fixed to the upper end face of the limit disk. A sensor group is installed on the lower end face of the limit disk. A connecting rod is also installed on the lower end face of the limit disk. A docking head for docking with the AUV is provided at the end of the connecting rod. An AUV locking mechanism for locking the AUV to the connecting rod is provided on the docking head. The AUV is loaded into the AUV storage cabin along the axis of the AUV storage cabin. A docking port for docking with the docking head is provided at the head of the AUV. A first USBL for guiding the AUV close to the ice-piercing detector is installed at the top inside the docking port.

The invention discloses a drilling construction intelligent decision support and risk early warning method, device and equipment, and the method comprises the steps: collecting drilling sound wave response data, carrying out harmonic analysis to recognize a reservoir resonance node, and constructing a reservoir energy conduction network; a fluctuation propagation path is tracked based on an energy conduction network, and a construction risk area is identified through stress wave spectrum features; porosity and permeability data are extracted for phase difference analysis, a seepage-construction mismatch factor is generated, and a construction optimization window is actively generated; constructing a geological potential energy gradient field to determine an overflow influence range, and performing tensor coupling on multi-dimensional risk factors to generate a three-dimensional construction decision matrix; an optimal construction scheme is generated through energy minimization and risk-efficiency dynamic balance processing; construction parameter response changes are monitored in real time, abnormal parameter combination symptoms are recognized, a grading risk early warning instruction is generated, and intelligent decision support and risk early warning of drilling construction are achieved.

The invention relates to the technical field of earthquake detection, and discloses a constant-pressure online detection device and method for the concentration and flow of gas escaped from a hot spring, and the device comprises a gas collecting hood, a buffer bottle, a gas flow detector, a constant-volume gas storage chamber, a constant-pressure detection valve, an online gas detector and a central controller; the gas collecting hood is arranged in a corresponding area of hot spring escape gas and collects the hot spring escape gas; the gas inlet end of the surge flask is communicated with the gas collecting hood through a pipeline, and the gas outlet end is communicated with the gas inlet end of the constant-volume gas storage chamber through a gas flow detector; the constant-volume gas storage chamber is communicated with an on-line gas detector pipeline through a constant-pressure detection valve, and when the air pressure value of hot spring escape gas stored in the constant-volume gas storage chamber is larger than a preset air pressure threshold value, the hot spring escape gas enters the on-line gas detector through the constant-pressure detection valve; the on-line gas detector is used for detecting gas components and concentration of gas escaped from the hot spring and sending detection data to the central controller.

Disclosed are a device and method for detecting and identifying shallow-stratum foreign objects based on distributed acoustic sensing. The device includes a vibrating sensitivity-enhanced fiber optic cable, a distributed acoustic sensing demodulator, a vibrator system, a vibration data processing unit, a velocity structure inversion unit and an AI-aided locating and identification unit. The distributed acoustic sensing demodulator transmits vibration signals to the vibration data processing unit for preprocessing to obtain waveform signals; the velocity structure inversion unit performs inversion to obtain the shallow-stratum underground velocity structure; positions of shallow-stratum foreign objects are identified through the underground velocity structure and abnormal waveforms; and the types of shallow-stratum foreign objects are identified. A distributed acoustic sensing method for fiber optic cables is used to obtain vibration signals, velocity imaging is performed on a shallow-stratum velocity structure, and AI is utilized to achieve locating and type identification of shallow-stratum underground foreign objects.

The invention relates to the field of seismology and geology, in particular to a G-R relation earthquake maximum magnitude estimation method based on fault feature weighting, which comprises the steps of extracting geometric features and structural features of a target fracture, fitting a region Gutenberg-Richter relation, calculating a comprehensive weight factor of the fracture, and adjusting a value a of the region based on the weight to obtain a specific value a of a single fracture; estimating the maximum magnitude of a single fracture by adopting an empirical relationship through combining the adjusted specific value a and the adjusted region value b; according to the method, the accuracy and reliability of earthquake maximum magnitude estimation are improved, and the method has superiority especially in areas with insufficient earthquake catalog data.

A fiber optic distributed sensing system and method. The fiber optic distributed sensing system includes: an optical fiber extending through an underwater gas field; an interrogator connected to the optical fiber so that light transmitted through the optical fiber is capable of being sensed by the interrogator; and a computer system configured to obtain sensor data captured by the interrogator and determine an amount or a characteristic of gas within the underwater gas field based on the sensor data. The method includes: capturing sensor data using a fiber optic distributed sensor extending through an acoustic field; performing a wavelet transform on the sensor data in order to obtain spatiotemporal data; and matching the spatiotemporal data to an event template of a predetermined set of event templates in order to identify a spatiotemporal event.

A computer-implemented method for automatically determining subsurface reservoir boundaries of a wellbore subsurface reservoir in a drilling system that include receiving an inversion image that forms part of an electromagnetic inversion result profile that indicates subsurface measurements captured by a downhole resistivity sensor. The method also includes generating an image mask for the inversion image using an ensemble image model that enables initial image masks outputted from multiple image-to-image machine-learning models into the image mask. The method additionally includes augmenting the inversion image with subsurface boundaries based on the image mask to generate an augmented inversion image. The method further includes generating at least one of: one-dimensional, two-dimensional, and three-dimensional representations of a subsurface area which indicate geological features and properties of the wellbore subsurface reservoir and adjusting a drilling parameter of a downhole drill within the wellbore subsurface reservoir based on the augmented inversion image.

The invention provides a shallow-buried tunnel blasting surface vibration waveform prediction method and system, and relates to the technical field of surface vibration waveform prediction, and the method comprises the steps: collecting geological, blasting and environmental parameters of historical and current blasting, and forming a standardized parameter set through three-stage index storage, abnormal value screening and standardization processing; establishing a mapping relation between blasting parameters and a vibration waveform time domain sequence based on an LSTM neural network, and predicting a current blasting vibration waveform; fusing the geological condition influence factors, the environmental sensitivity influence factors and the predicted waveform features to generate a comprehensive safety assessment index; and according to a matching result of the index and a multi-level safety threshold value, the risk is divided into four levels of safety, low risk, medium risk and high risk, and differential management and control are executed, so that accurate prediction and dynamic regulation and control of the blasting vibration risk are realized. According to the invention, the vibration wave propagation process in the shallow tunnel blasting construction can be accurately simulated, so that the influence of blasting can be better evaluated.

The invention relates to the technical field of optical signal processing and analysis, and particularly provides a submarine optical fiber vibration event detection method based on credibility weighting, and the method comprises the steps: obtaining an optical pulse echo matrix according to a received signal, and carrying out the decomposition of the signal through a low-rank and sparse decomposition model. A credibility coefficient is introduced to measure the reliability of the data, an optimization model is established, and the credibility coefficient is set to be a tide credibility coefficient or a natural disaster credibility coefficient according to the marine environment. Solving the optimization model to obtain a sparse item estimation matrix corresponding to the sparse item; and detecting a natural disaster event and a vibration event in the received signal according to the sparse term estimation matrix. According to the method, reliability evaluation of the signals can be dynamically adjusted according to different marine environments, noise and real vibration events are effectively distinguished, high robustness is kept in the complex and multi-interference marine environment, the method is not prone to being affected by interference sources, and event detection precision and accuracy are good.

The invention belongs to the technical field of mine safety and engineering earthquake monitoring, and particularly relates to a mine earthquake earth surface vibration intensity prediction method and system based on an improved sine and cosine algorithm, and the method comprises the steps: firstly constructing a mine earthquake intensity prediction model containing geometric attenuation, inelastic attenuation and an earthquake source energy item, and then constructing a target function based on measured data, and optimizing model parameters by adopting an improved sine and cosine algorithm. The algorithm improvement comprises the following steps: adaptively adjusting a search step size through a dynamic amplitude exponential decay strategy, and balancing global exploration and local development capabilities; and a periodic random restart mechanism is introduced, and the position of the individual with the worst fitness is reset to avoid premature convergence. Through the combination of the physical model and the intelligent optimization algorithm, the problems of fixed function form and low parameter optimization efficiency of the traditional empirical model are solved, and high-precision prediction of the mining induced earthquake surface vibration intensity is realized.

The invention discloses an acoustic emission signal noise reduction and feature extraction method and system suitable for a deep roadway, and relates to the technical field of safety monitoring of deep mineral resource mining, and the method comprises the specific steps: arranging an acoustic emission sensor array along the deep roadway, collecting multi-source data, converting the multi-source data into digital signals, and storing the digital signals; identifying an interference type through a wavelet packet decomposition and environment correction algorithm; self-adaptive noise reduction is carried out by using a complexity sensitive penalty algorithm; time domain and frequency domain features are extracted, coupling parameters are calculated, and time domain and frequency domain features are obtained through Hilbert-Huang transform; and finally, through principal component analysis dimensionality reduction and mutual information entropy screening, constructing a feature vector and transmitting the feature vector to a safety early warning system. According to the invention, the processing precision and reliability of the acoustic emission signal are improved through the multi-source signal acquisition module, the interference identification module and the adaptive noise reduction module; signal characteristics are comprehensively described through algorithm optimization, key characteristics are output through characteristic optimization, real-time monitoring and early warning are achieved through a dynamic damage vector algorithm, and deep roadway construction safety is guaranteed.

The invention provides a TLF-GV signal correlation earthquake magnitude prediction method and system, and a medium, belongs to the technical field of earthquake prediction, and aims at solving the problems that a traditional magnitude prediction model lacks physical constraints, TLF-GV features are not fully utilized, the interpretability is poor, and cooperation with a preorder technology is lacked. Comprising the following steps: extracting a multi-source feature vector of a TLF-GV signal containing a gravity component absolute amplitude, a vibration component relative amplitude and a signal phase; based on the G-R law, constructing the positive correlation physical characteristics log10 (GVCIpeak) and BF * Tanomal of the abnormal intensity and the magnitude of the TLF-GV signal; weighting the features according to a Bayesian factor BF value; fitting according to historical samples to obtain a physical empirical formula of positive correlation of GVCI peak logarithm and magnitude as a physical constraint, and constructing a loss function by taking the physical constraint as a regularization item and combining with a mean square error of a magnitude prediction task; and constructing an XGBoost model, and training according to the training set to obtain a prediction model for earthquake magnitude prediction. According to the method, quantitative prediction of earthquake magnitude is realized based on multi-source features of TLF-GV signals in combination with a physically constrained XGBoost regression model.

The disclosure focuses on using a boundary identification system to actively determine borders and boundaries in subsurface geological features, such as reservoirs. In various implementations, the boundary identification system uses an ensemble image model leveraging multiple image-to-image machine-learning models to efficiently and accurately generate reservoir boundaries from inversion result profiles and images. In many instances, the boundary identification system generates reservoir boundaries from inversion results in real-time. Additionally, in some instances, the boundary identification system further improves the accuracy of the ensemble image model by diversifying the inputs and using ensembling on the individual model outputs during inference.

A method can include receiving input for a field scenario that comprises fluid dynamics and chemical phenomena, where the input defines substances amenable to speciation; executing a fluid dynamics simulator to perform timesteps for transport of the substances while calling a chemical phenomena framework that determines speciation of the substances; and generating results for the field scenario based on the executing.

The invention discloses a space differential disturbance extraction method based on satellite in-situ electron density data, which comprises the following steps: collecting seismic data and a space weather index, obtaining observation data of the in-situ electron density data, and preprocessing the observation data; obtaining spatial differential data according to the observation data and the background data, and obtaining magnetic calm differential data according to the spatial weather index and the spatial differential data; clustering the magnetic calm differential data to obtain abnormal disturbance data, extracting seismic disturbance data in the magnetic calm differential data based on the seismic data, and extracting ionized layer abnormal data based on the abnormal disturbance data and the seismic disturbance data; and constructing a spatial difference disturbance extraction model according to the spatial difference data and the ionized layer abnormal data, inputting the to-be-analyzed data into the spatial difference disturbance extraction model, and outputting an extraction result. The method not only can improve the precision of spatial differential disturbance extraction, but also has good interpretability, and can be directly applied to a spatial differential disturbance extraction system.

The invention relates to the technical field of intelligent operation and maintenance of a power system, in particular to an intelligent early warning method and system for preventing external damage of a power transmission line. The method comprises the following steps: acquiring acoustic data of a power transmission line through an acoustic sensor; performing voiceprint feature sparse reconstruction according to the transmission line acoustic data to obtain voiceprint feature data; performing sparse causal graph matching on the voiceprint feature data to obtain sound source behavior causal graph data; performing double-domain attention cross recognition according to the sound source behavior causal atlas data to obtain sound source separation recognition data; and performing scene map generation on the sound source separation identification data to obtain sound source scene map data. According to the invention, by constructing the nested causal chain graph and fusing the propagation weight mechanism, the development evolution and multi-stage trigger relationship of the external damage event of the power transmission line can be accurately captured. Compared with a traditional single-point detection method, the method has higher chain reasoning ability and early risk identification ability, and the response foresight and causal interpretability of an early warning system are effectively improved.

The invention discloses a rock mass fracture risk early warning method, system and device based on acousto-optic data fusion and a storage medium, and the method comprises the steps: collecting acoustic emission energy, acoustic emission counting, DIC data and other acousto-optic monitoring data of a rock mass test piece, and calculating to obtain an acoustic emission b value, an acoustic emission energy variance, strain field differentiation data and other multi-dimensional precursor indexes; cUSUM-Pettitt is used for cooperatively checking and recognizing mutation points, confidence and physical mutation amplitude are combined to divide multiple early warning levels, a classification model is trained through a LightGBM algorithm, and accurate early warning of the rock mass fracture risk is achieved. Multi-source physical field information is fused, the limitation of single data monitoring is overcome, the early warning reliability and timeliness are improved, and the method is suitable for disaster prevention and control in the fields of mine safety, rock mass engineering and the like.

A method, comprising receiving input data representing a subterranean formation, defining a grid representing the input data, with the grid including cells. The method also includes identifying at least one of the cells in which kerogen is present based on the input data, simulating hydrocarbon movement within the kerogen using the grid, and generating a model of hydrocarbon expulsion to pore space based on the simulating.

The invention discloses an optical fiber acoustic wave comprehensive monitoring and detecting method and system, a plurality of optical cables and DAS optical fiber acoustic wave sensor equipment are arranged in a tunnel, ultrasonic waves and infrasonic waves are emitted in a tunnel environment through the DAS optical fiber acoustic wave sensor equipment, the ultrasonic waves are used for monitoring the outline of a tunnel wall, and the infrasonic waves are used for monitoring the infrasonic waves. The infrasonic waves are used for monitoring the geological variation condition outside the tunnel; various sound wave signals in a tunnel environment are sensed through the optical cable, the optical cable is caused to vibrate, the vibration signals are transmitted to the DAS optical fiber sound wave sensor equipment through the optical cable, and the vibration signals are transmitted to the cloud platform through the DAS optical fiber sound wave sensor equipment; received optical cable vibration signals are processed and analyzed through the cloud platform, and monitoring and abnormity alarming of the train running state, the tunnel wall contour and the geological change condition outside the tunnel are achieved; the optical cable is cheap, normal monitoring is not affected by foot kicking of people in the tunnel, and long-term and large-scale monitoring in the tunnel can be realized.

The invention relates to an ocean detection positioning technology, in particular to a submarine pipeline offset positioning method and a self-adaptive sound source frequency adjusting module. Comprising the following steps that S1, a combined intelligent pipe cleaner set is installed at an inlet of a submarine pipeline, and a sound source emitting device on the combined intelligent pipe cleaner set conducts self-adaptive adjustment on the frequency of sound wave signals generated by the sound source emitting device according to different media in the submarine pipeline through a self-adaptive sound wave frequency selection method; s2, the AUV moves along with the combined intelligent pipe cleaner set, signals sent by the combined intelligent pipe cleaner set are received in real time, and the signal propagation distance # imgabs0 # between the AUV and the combined intelligent pipe cleaner set is determined through a weighted fusion algorithm; s3, the position estimation # imgabs1 # of the AUV is updated through a Kalman filtering algorithm, and therefore the position of the submarine pipeline where the combined intelligent pipe cleaner is located is determined. The real-time positioning of the seabed offset orbit is realized, so that the real-time positioning requirements of high precision and low error in a complex seabed environment are met.