1079 results about "Underground pipeline" patented technology

The invention belongs to the technical field of earthquake disaster scene construction, and discloses an earthquake disaster scene construction method based on big data, and the method comprises the following specific steps: 1, multi-source heterogeneous data collection and preprocessing ground structure data collection: employing a multi-platform remote sensing technology to obtain urban building group space distribution characteristics; a three-dimensional coupling model of an underground pipe network and a ground building is constructed by integrating multi-source data, so that the defect of isolated analysis of a ground system and an underground system in a traditional method is overcome, and synchronous simulation of a dynamic association process of pipe network fracture and building damage is realized; the additional influence of pipe network failure on the building foundation stability and the linkage effect of soil erosion caused by leakage fluid are accurately analyzed, meanwhile, a time-space association rule of pipe network fracture events and multiple types of secondary disasters such as building fire disasters and fire fighting failure is established, and the building group function paralysis risk and the disaster diffusion range under different damage degrees are dynamically predicted; and the disaster evolution path is displayed in real time through a visual platform.

The invention provides an underground pipe network defect intelligent identification and early warning method and system based on deep learning, and relates to the technical field of pipe network detection, and the method comprises the steps: employing a pipeline detection robot to obtain multi-source data, and carrying out the preprocessing; mapping the features to a manifold space, constructing a similarity matrix, and carrying out geometric difference weighted fusion; constructing an incidence matrix to calculate a spatial distance and mechanical strength, and carrying out iterative propagation to generate a defect characteristic spectrum; and acquiring an evolution sequence by adopting a self-adaptive sliding window, determining influence factors based on anti-fact intervention, and calculating a state transition probability to determine an optimal maintenance scheme. According to the method, the pipe network defect identification accuracy is improved, and pipeline state prediction and maintenance decision intelligence are realized.

The invention provides a pipe gallery disease monitoring and diagnosis method based on multi-source heterogeneous data, and relates to the technical field of disease monitoring and diagnosis, and the method comprises the steps: carrying out the data collection through a multi-mode sensor network disposed in an underground pipe gallery; fusing the multi-source heterogeneous data set based on a graph neural network to generate a dynamic sensing characteristic spectrum of the whole domain of the pipe gallery; carrying out disease evolution mode identification, and determining a pipe gallery disease risk level; and triggering the self-adaptive early warning strategy, executing the self-adaptive early warning strategy to generate a decision instruction set, and pushing the decision instruction set to the visual monitoring platform. The technical problem that potential problems are difficult to find in time and the operation efficiency of the pipe gallery is affected due to the fact that detection and risk assessment of the pipe gallery diseases depend on periodicity is solved, real-time monitoring and early recognition of the underground pipe gallery diseases are achieved through effective integration and processing of the multi-source heterogeneous data, and the method and the device have the advantages of being high in practicability and the like. And the efficiency and the safety of pipe gallery management are improved.

The invention belongs to the technical field of underground pipeline construction, and particularly relates to a multi-point synchronous jacking force distribution and posture monitoring system for ultra-long-distance pipe jacking construction and a control method. Comprising a sensor module, a data acquisition module, a control module and a hydraulic jacking device, the control module carries out numerical differentiation on displacement signals to obtain the real-time propelling speed of each jacking point, and the real-time propelling speed is compared with a preset target propelling speed to obtain speed deviation; the axial friction resistance of the corresponding pipe section is calculated according to the jacking force signal and the displacement signal; comparing the real-time attitude angle signal with a designed target attitude to obtain an attitude deviation; inputting the speed deviation, the axial friction resistance of the corresponding pipe section and the attitude deviation into a coupling control algorithm, and outputting jacking force increment or speed correction. According to the invention, the propulsion and postures of the jacking points of the main jacking station and the plurality of relay jacking stations can be kept synchronous and coordinated.

The invention discloses a multi-dimensional risk supervision method for bridge and underground pipe gallery engineering, and particularly relates to the field of underground pipe gallery engineering supervision, and the method comprises the steps: an underground structure slippage risk identification and prediction process based on pressure data driving: firstly, collecting an original pressure signal and screening effective data; establishing a standardized two-dimensional pressure field matrix by using the variation function; local gradient mutation features are recognized through Sobe l convolution, evaluation units are divided to calculate entropy distribution, entropy change abnormity is extracted, and a multi-dimensional phase space trajectory is constructed; the maximum Lyapunov index is further applied to estimate and recognize the stability state, gradient and entropy features are combined to construct a parameter inversion observation set, and a constrained optimization algorithm is adopted to iteratively solve an equivalent friction angle; constructing the time sequence into a time sequence, inputting the time sequence into an ARI MA model, executing slip rolling prediction, finally generating a risk thermodynamic diagram with confidence, and achieving the dynamic evaluation and early warning of the slip trend of the underground pipe gallery.

The invention relates to the technical field of underground pipe gallery distributed intelligent control, and discloses an underground pipe gallery intelligent illumination and energy consumption optimization system, which comprises a dynamic credibility evaluation module for analyzing multi-modal environment sensing data in real time and generating a dynamic credibility score; the edge-cloud collaborative optimization module is used for realizing distributed collaborative learning through local model fine tuning and global knowledge distillation; and the event-driven resource allocation module dynamically optimizes a lighting strategy and a communication priority based on credibility scores and emergency information, and through multi-modal data cross validation and adaptive weight adjustment, the robustness of anomaly detection is significantly improved, and misjudgment caused by sensor failure or environmental interference is effectively suppressed; the dynamic planning algorithm is used for synchronously optimizing energy consumption and equipment service life, efficient balance of safety and energy efficiency is achieved, the system is particularly suitable for complex underground environments and can automatically adapt to sudden anomalies, and the intelligent level and the energy utilization efficiency of pipe gallery illumination are remarkably improved.

The invention relates to the technical field of pipeline GIS, and discloses a pipeline GIS system and method. The method comprises the following steps: acquiring GIS underground pipe network topological data of a target area in a GIS system, and acquiring an earth surface abnormal data set of the target area through an unmanned aerial vehicle; performing multi-channel classification and weighted spatial interpolation on the surface anomaly data set to obtain surface anomaly risk distribution data; performing spatial correlation analysis through a heterogeneous attention module to obtain a spatial attention weight and earth surface anomaly propagation characteristics; performing time-space synchronization analysis on the surface abnormal data set and the GIS underground pipe network topology data to obtain risk cascade propagation data; and performing risk judgment on the risk cascade propagation data to obtain a safety state early warning result of the underground pipeline, and generating an unmanned aerial vehicle route adjustment instruction and a GIS system response instruction. According to the method, the complex space association relationship between the surface anomaly and the underground pipeline can be automatically identified and quantified, and the anomaly detection precision and credibility are improved.

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 belongs to the technical field of electric power and communication engineering, and particularly relates to an intelligent line patrol instrument based on multi-band electromagnetic induction and TDR fusion, and the intelligent line patrol instrument comprises a signal transmitting end which comprises a main control unit, a multi-band electromagnetic signal generator, a TDR pulse module and a self-adaptive coupling circuit. And the signal receiving end comprises a three-axis magnetic field sensor, an ADC module, an FPGA processing unit, a DSP processing unit and a touch display screen. The invention relates to an intelligent line patrol instrument combining multi-band electromagnetic induction and time domain reflection (TDR) technologies, which is used for path tracking, depth measurement and high-precision fault positioning of underground cables and optical cables, and is particularly suitable for underground pipeline maintenance and construction scenes under complex electromagnetic environments (such as transformer substations and dense pipeline areas).

The invention provides an underground pipe network engineering drawing drawing method and system based on a standard legend library, and relates to the technical field of engineering drawing, and the method comprises the steps: obtaining pipeline space positions, connection relations and initial data of legend information; retrieving the standard legend with the highest matching degree by using a deep neural network to generate a two-dimensional engineering drawing; the two-dimensional drawing is converted into a three-dimensional model, and the pipeline layout with the collision risk is adjusted through an optimization algorithm based on field potential flow; and automatically updating and outputting an engineering drawing according to the optimized three-dimensional model. According to the method, intelligent optimization of pipeline layout is realized, and the drawing efficiency and accuracy are improved.

The invention provides a subway construction management method and device in a complex sensitive environment, and relates to the technical field of subway construction management, and the method comprises the steps: obtaining first information which comprises construction region shield propulsion parameter time sequence data, building settlement monitoring data, underground pipeline distribution data and micro-seismic sensor array data; preprocessing the first information to obtain a three-dimensional geological influence map fusing underground structure-stress response-dynamic disturbance; performing semi-variation function modeling and spectrogram co-training clustering algorithm joint optimization on the three-dimensional geological influence map to generate a multi-layer heterogeneous map structure; performing dynamic graph convolutional network and graph integral propagation model co-evolution on the multilayer heterogeneous graph structure to generate a risk cascade prediction model; and solving the prediction model to obtain a global management scheme of subway construction. According to the invention, subway construction full-life-cycle management with controllable risk, timely response and dynamic strategy adjustment is realized.

The invention belongs to the technical field of underground pipeline detection and defect identification, and particularly discloses an underground pipeline defect detection method based on image and point cloud data fusion, and the method comprises the following steps: collecting a video image and laser radar point cloud data of the inner wall of an underground pipeline, and carrying out the synchronous pairing of an image frame and a point cloud frame based on a timestamp; performing defect identification on the synchronized image frames to obtain a defect region ROI; based on a pre-calibrated external parameter matrix of a camera and a laser radar, converting the synchronously acquired point cloud data coordinates into an image coordinate system, calculating through internal parameters of the camera to obtain pixel coordinates, and screening out a point cloud point set falling into a defect region of interest (ROI) as a defect point cloud subset; and sequentially carrying out denoising, registration and clustering segmentation on the defect point cloud subset to obtain a three-dimensional point cloud structure of the defect, and calculating a quantization parameter of the defect based on the three-dimensional point cloud structure. According to the invention, automatic real-time detection and accurate quantification of pipeline defects can be effectively realized.

The invention provides a buried pipeline stress monitoring system and method, and relates to the technical field of pipeline monitoring, and the system comprises a TMR sensor; the full-bridge strain gauge group is configured to acquire stress data of the to-be-measured pipeline; the monitoring module is configured to obtain historical data; training the linear model by using the historical environment magnetic field data and the historical stress data to obtain an initial magnetic field stress model; updating model parameters of the initial magnetic field stress model by using a least square method to obtain a target magnetic field stress model; and inputting the environment magnetic field data and the stress data into the target magnetic field stress model to obtain target stress data so as to solve the problems that a single sensor is mainly adopted for measurement in the current pipeline stress monitoring process, and the measurement process is easily interfered by factors such as temperature drift and magnetic field fluctuation, so that the monitoring precision cannot meet the actual demand, and the measurement accuracy is poor. And the real stress state of the pipeline is difficult to reflect.

The invention relates to the technical field of information management, in particular to an underground pipeline gallery risk monitoring method and device. According to the method, a heterogeneous sensing terminal network is used for sensing a target underground pipeline gallery according to a fast and slow time sequence, and a pipeline gallery monitoring anchor point set is determined; performing cross-modal interaction on a heterogeneous sensing terminal network based on the pipeline and pipe gallery monitoring anchor point set to obtain a pipeline and pipe gallery monitoring anchor point multi-modal fusion feature set; performing anchor point risk assessment according to the pipeline and pipe gallery monitoring anchor point multi-modal fusion feature set, and obtaining a pipeline and pipe gallery monitoring anchor point risk factor set according to an assessment result; and when the pipeline and pipe gallery monitoring anchor point risk factor set is greater than or equal to a preset anchor point risk factor threshold value, triggering a risk early warning instruction. According to the method, accurate identification and dynamic early warning of the key risk points of the underground pipeline gallery are achieved through fast and slow time sequence sensing and cross-modal feature fusion processing based on the monitoring anchor points.

The invention provides an underground pipeline leakage detection method and system based on physical enhancement thermal inertia imaging, and belongs to the technical field of pipeline detection, and the method comprises the steps: obtaining an earth surface thermal field and environment meteorological parameters of a current target area; the surface thermal field and the environmental meteorological parameters are input into a double-flow physical information neural network model, a predicted background thermal field is obtained, the double-flow physical information neural network model is obtained through training based on leakage-free historical data and a total loss function, and the total loss function comprises physical loss items determined based on a one-dimensional heat conduction partial differential equation; calculating a space-time residual image of the earth surface thermal field and the predicted background thermal field, and obtaining an apparent thermal inertia distribution map of the earth surface based on the space-time residual image and environmental meteorological parameter inversion; and detecting whether the underground pipeline in the target area leaks based on the space-time residual map, the apparent thermal inertia distribution map and a preset condition. According to the invention, the accuracy of underground pipeline leakage detection can be improved.

The invention provides a line migration and modification scheme optimization evaluation method based on a multi-dimensional constraint condition, and the method comprises the steps: obtaining the position information of an urban underground pipeline, extracting the buried depth of an unlabeled pipeline and an intersection point according to the position information of the underground pipeline, carrying out the labeling, and obtaining the pipeline distribution data containing a three-dimensional coordinate and the buried depth deviation; pipeline horizontal distance information in the preliminarily optimized layering scheme is extracted, whether the distance meets the safety requirement or not is judged through a space geometry algorithm, and if not, horizontal distance parameters are adjusted, and adjusted horizontal distance parameters are obtained; acquiring damage degree data of the protection layer, and if the damage degree exceeds a preset threshold value, establishing a pipeline layout model with real-time response by dynamically updating a vertical layering standard and a horizontal spacing parameter; and according to the optimized pipeline collaborative distribution scheme, calculating a pipeline space utilization efficiency index, and carrying out iterative updating on the pipeline layout model to obtain a pipeline layout optimization result adapted to the dynamic environment.

The invention discloses a sewage treatment device with a dosing function, and relates to the field of sewage treatment.The sewage treatment device comprises pretreatment equipment, the water outlet end of the pretreatment equipment is connected with a plurality of sedimentation tanks through underground pipelines, rotating columns are installed on the inner sides of the sedimentation tanks, and each rotating column comprises a dosing tank and a water outlet tank; a water outlet groove is formed in the sedimentation tank, chemicals are added into the sedimentation tank through the water outlet groove, the inner wall of the sedimentation tank is fixedly connected with a slag discharging channel and a slag discharging opening, the slag discharging channel is fixedly connected to one side of the slag discharging opening, a discharging opening of the slag discharging opening is externally connected with slag discharging equipment through a pipeline, and a material collecting unit, a material guiding mechanism and an auxiliary assembly are arranged on the outer wall of the rotating column. Through cooperation of parts such as the collection hopper, when the collection hopper moves to the highest point of the first abutting plate, the opening is aligned with the scum discharging opening to pour out scum as much as possible, and therefore the scum cleaning efficiency is improved.

The invention discloses an urban underground pipeline life cycle safety monitoring method and system based on big data. The method comprises the following steps: acquiring full life cycle multi-source data of an urban underground pipeline; performing fusion preprocessing on the multi-source data, and constructing a pipeline digital twinborn model; performing real-time monitoring on the pipeline operation state by using a space-time correlation analysis algorithm, and extracting abnormal features; based on a pre-constructed deep learning risk assessment model, performing grading assessment on the pipeline safety state, and generating a risk grade; predicting the remaining service life of the pipeline through a service life prediction algorithm in combination with historical fault data and real-time monitoring data; and generating a maintenance decision suggestion according to the risk level and the remaining service life, and realizing full-life-cycle safety monitoring of the underground pipeline. A digital twinborn model is constructed through multi-source data fusion, and dynamic visual management of the whole life cycle of the underground pipeline is achieved. Through combination of time-space correlation analysis and a deep learning algorithm, pipeline abnormal features are accurately identified, and the risk assessment accuracy is improved.

The embodiment of the invention discloses an underground pipeline simulation operation method and device based on spatial distribution. A specific embodiment of the method comprises the steps of collecting a multi-source underground pipeline data set in a target city area; performing data standardization on each piece of multi-source underground pipeline data in the multi-source underground pipeline data set to generate a standardized multi-source underground pipeline data set; establishing an initial underground pipeline three-dimensional structure model; performing pipeline space alignment on the initial underground pipeline three-dimensional structure model to generate a target pipeline three-dimensional structure model; and adding the target pipeline three-dimensional structure model to a three-dimensional pipe network visual distribution diagram layer in a preset target urban area underground distribution network global twin space, and performing underground pipeline simulation operation on the three-dimensional pipe network visual distribution diagram layer according to preset pipeline operation parameters. According to the embodiment, the accuracy of the generated target pipeline three-dimensional structure model can be improved.

The invention belongs to the technical field of data processing, and provides a multi-source data fusion underground pipe network data intelligent detection method, which comprises the following steps of: firstly, acquiring multi-source detection data of an underground pipe network; then, according to the multi-source detection data and an isolated forest algorithm based on Bayesian optimization, abnormal point information is determined; and finally inputting the abnormal point information into a pre-established pipeline state model, and determining a fault prediction value of the abnormal point of the underground pipe network. Wherein parameters of the pipeline state model are updated in real time according to an online variational inference algorithm; the online variational inference algorithm is provided with a first learning rate of a global parameter and a second learning rate of a local parameter; the first learning rate is used for reflecting common characteristics of the whole monitoring system or the pipeline network; the second learning rate is used for reflecting the change of the sensor along with the change of the underground environment. The differential learning rate is adopted for the global parameters and the local parameters, so that the pipeline state model is continuously updated in real time, and the safety risk of the underground pipe network in the future is accurately predicted.

The invention discloses an underground pipeline detection method and system based on multi-source data fusion and intelligent analysis, and belongs to the technical field of pipeline detection. The method comprises the steps of collecting multi-source data of a target area; wherein the multi-source data comprises electromagnetic signal data, electromagnetic wave reflection data and sound wave propagation data; performing filtering and normalization preprocessing on the multi-source data to obtain target data; constructing a multi-source data fusion model to perform feature level fusion on the target data to generate a fusion feature vector; an underground pipeline recognition model based on deep learning is constructed to intelligently analyze and recognize the fusion feature vector, and a recognition result of the underground pipeline is obtained; and marking and displaying the identification result on an electronic map in a visual mode. According to the invention, by fusing data collected by various detection technologies and applying an intelligent analysis algorithm, the accuracy, reliability and efficiency of underground pipeline detection are improved, and the limitation problem of the existing detection technology is solved.

The invention discloses a method for lifting and repairing a pipeline and a pipe gallery slab staggering under soft soil, and aims to meet the requirement of grouting pressure design in a slab staggering differential settlement repairing project of an underground large-diameter pipeline and a small-section pipe gallery and improve the grouting repairing effect. According to the method, a mechanical model is established based on a mechanical mechanism existing in a staggered slab pipe joint and a surrounding soil body, and the grouting pressure range of grout is determined; based on the grouting pressure range, a theoretical model of settlement deformation of the soft soil layer below the pipe joint after grouting and compression deformation after solidification of the high polymer grout is established; calculating the slurry height required for lifting the error table pipe joint to a certain height per linear meter; and finally, lifting and repairing of the slab staggering pipe gallery are carried out by adopting a method that a bag is arranged below the pipe joint, high polymer polyurethane is injected inwards for lifting and repairing, and lightweight concrete is filled outwards for reinforcement, so that grouting lifting and repairing of the slab staggering pipe joint in the soft soil area under the condition that the underground pipeline and the underlying foundation soil of the pipe gallery bear safely are achieved. The construction is fast, the economic benefit is high, and the like.

The invention discloses an underground pipeline intelligent leakage detection method and device, and relates to the field of intelligent leakage detection.The method comprises the steps that a high-frequency pressure sequence of a target pipe section is collected, and based on the Boltzmann superposition principle, the high-frequency pressure sequence is used for constructing a neural lag operator network by taking the environment temperature as a heat rheological regulation factor; taking the full historical stress-strain memory field tensor as input, carrying out non-local memory characteristic and hysteresis loop analysis, carrying out point-by-point differential processing on a high-frequency pressure sequence and the intrinsic nonlinear hysteresis response signal of the pipe, stripping non-stationary background fluctuation, avoiding modulation interference of material nonlinearity on a fluid signal, and obtaining a non-linear hysteresis response signal of the pipe. The method comprises the following steps: extracting the abnormal residual error of the fidelity fluid dynamics, constructing a spatio-temporal evolution Poincare section, inputting the spatio-temporal evolution Poincare section into a convolutional neural network, judging the real leakage negative pressure wave and the random drift of the sensor through the morphological difference of attractors on the spatio-temporal evolution Poincare section, and eliminating the periodic false alarm caused by the rheological property of the material.

The invention discloses an underground pipeline internal integrated positioning method based on multi-sensor cooperation, and belongs to the technical field of navigation positioning. The method comprises the following steps: correcting a yaw angle based on a magnetometer, performing attitude compensation on a Z axis through the magnetometer, and effectively eliminating an integral error caused by performing attitude calculation only depending on a gyroscope; on the basis of displacement estimation of multi-redundancy odometers, four wheels at the bottom of the robot are each provided with a motor encoder, the four odometers can complement one another, and position estimation of the IMU is effectively corrected through more reliable data; based on data fusion of bidirectional unscented Kalman filtering, non-linear errors are avoided through unscented Kalman filtering, and a smooth trajectory with higher precision can be obtained in cooperation with bidirectional filtering. According to the invention, the underground pipeline track positioning precision in a satellite signal rejection environment can be effectively improved.

The invention discloses an underground pipe network dynamic risk detection and early warning method, which comprises the following steps that firstly, a multi-source heterogeneous database is constructed, various data are integrated to form a core database, and the construction step comprises the step of collecting various data; and then data standardization processing is carried out, and various coding and normalization operations are carried out. Then constructing a spatial-temporal characteristic matrix; then, a hybrid model containing a multilayer perceptron and a convolutional neural network is constructed for deep learning training, and a model input layer contains multiple feature tensors and has related strategies; and then the model score is mapped to a geographic information system to generate a risk thermodynamic diagram, a threshold value is set to trigger an early warning signal, linkage with an emergency platform is carried out, and three-dimensional visual display is carried out. Cross-regional cooperative training and risk trend analysis can be carried out in the model, and a digital twin platform can be embedded to realize dynamic simulation detection. According to the method, a comprehensive data basis is constructed, a complex relationship is mined, model performance is improved, and decision support is provided for pipe network risk management and control and the like.

The invention discloses an underground pipeline settlement monitoring device and monitoring method.The underground pipeline settlement monitoring device comprises a static force level gauge mechanism which is connected in series and monitors pipeline settlement through changes of the internal liquid level, and further comprises a vibration air compression mechanism and an opening and closing mechanism which are communicated and both arranged on the static force level gauge mechanism; the protection mechanism is arranged on the opening and closing mechanism, a connecting hose is arranged in the protection mechanism and used for communicating the adjacent static force level gauge mechanisms, and an extrusion mechanism is arranged outside the protection mechanism and used for applying pressure to the interior of the protection mechanism. The static leveling instrument has the beneficial effects that when vibration exists at a measuring point, the vibration air compression piece reciprocates in the air compression cylinder under vibration and can suck air along the one-way valve and press the air into the opening and closing mechanism, the first sliding piece is pushed to move downwards, and a liquid inlet and a liquid outlet of the static leveling instrument mechanism are blocked; the liquid is prevented from flowing between the liquid storage tank and the connecting hose due to vibration, so that the liquid level is prevented from changing due to vibration to influence the detection precision.

The invention discloses an urban underground pipeline safety active prevention and control management system and method, and mainly relates to the technical field of urban underground pipeline prevention and control management. The data acquisition module is used for acquiring multi-source sensing data of urban underground pipelines in real time; the data preprocessing module is used for performing noise filtering and normalization processing on the multi-source sensing data and extracting time domain and frequency domain features; the deep learning model module is composed of a time sequence feature extraction network and an anomaly detection network; the risk assessment module is used for calculating a pipeline health score based on the output of the deep learning model module; and the active prevention and control module triggers an early warning signal according to the risk level and is linked with a pressure regulating valve and a flow pump station to execute dynamic regulation and control. The method has the beneficial effects that fusion of multi-source data and accurate prevention and control and dynamic optimization of underground pipelines are realized, and the safety management efficiency of the pipelines is improved.

The invention discloses an underground pipeline robot autonomous navigation system with an obstacle avoidance function, and relates to the technical field of autonomous navigation.Multi-dimensional information of obstacles encountered by an underground pipeline robot in historical operation is collected and transmitted to a space-time diagram neural network ST-GNN for training, and the obstacle risk level is obtained; the method comprises the following steps: classifying paths with identical starting points and ending points into one class, calculating distances and timestamps among all obstacles on the paths to obtain position classes, fitting the position classes by a rectangular region optimization method based on Bezier curve fitting to obtain different risk regions, and the underground pipeline robot analyzes the alternative paths passing through the different risk areas to select the most suitable navigation path. The problems of low passing efficiency and long time consumption caused by the fact that navigation of the underground pipeline robot is influenced by obstacles are solved.

The invention discloses an underground pipeline robot autonomous control method based on visual servo. Firstly, a binocular camera is used for obtaining three-dimensional information of a target object, then hand-eye calibration is carried out to unify a mechanical arm coordinate system and a camera coordinate system, then PBVS is used for controlling the mechanical arm to move according to the three-dimensional information of the target and the three-dimensional information of the mechanical arm, and finally an online supervised visual servo system is used for correcting input in real time. Autonomous control of the robot is achieved, and the grabbing accuracy is guaranteed. According to the autonomous control method for the underground pipeline robot based on the visual servo, autonomous adjustment of the pipeline robot in the object grabbing process can be achieved, and therefore the target object can be accurately grabbed.

The invention discloses a method and a device for detecting and positioning an underground pipeline containing a ferromagnetic material. According to the invention, the fluxgate sensors and the position and attitude sensors are installed on the support frame, and the support frame is driven to rotate, so that rapid magnetic scanning measurement of the underground pipeline is realized, and the trend, the horizontal position and the depth of the pipeline are estimated. According to the invention, the efficiency is greatly improved by adopting a rotary scanning strategy, the problem of uneven or muddy ground is avoided, and the method is suitable for detecting a ferromagnetic target with a slender structure; the direction and depth of the pipeline are estimated by using the coordinates of the extreme points of the magnetic field, and the positions of ferromagnetic targets which are non-uniformly distributed along any straight line can be estimated; the method does not need an accurate magnetic field value, is suitable for a complex environment with strong interference, is short in estimation time, and supports real-time positioning of the pipeline; and the corresponding pipeline depth below each measurement node is estimated by using the measurement data of the plurality of measurement nodes at different heights, so that the method can be applied even if the pipeline is not horizontally buried.