1362 results about "Pile" patented technology

The invention discloses a safety monitoring system of a liquid cooling over-charging pile, and relates to the technical field of over-charging pile monitoring, the system comprises a data acquisition module, a data processing and analysis module, a dynamic model construction module, a temperature prediction module and a safety early warning module; according to the method, the dynamic model is constructed through the long short-term memory network LSTM, the complex nonlinear relation and the time sequence dependence of the multi-dimensional data are mined by using the gating mechanism of the dynamic model, the accurate characterization of the operation state of the liquid cooling over-charging pile is realized, the actual operation state of the equipment can be accurately described, the temperature data time sequence modeling is performed through the LSTM, and the accuracy of the temperature data time sequence modeling is improved. Parameters such as multi-source temperature and cooling liquid flow are fused, real-time prediction of the temperature change trend is achieved, the defect that a traditional algorithm is insufficient in temperature time sequence dependence capture is overcome, temperature abnormity can be recognized in advance, a safety threshold value is dynamically adjusted through a fuzzy logic algorithm, and self-adaptive threshold value adjustment is achieved in combination with parameters such as charging power. The problem that a traditional fixed threshold value is poor in adaptability is solved, and the early warning accuracy is improved.

The present invention belongs to the technical field of digital mine safety, and particularly relates to a blasting area surface morphology inversion method based on an unmanned aerial vehicle. The method comprises: S01, on-site data collection; S02, blasting area morphology inversion; S03, a muck pile throw distance; and S04, a muck pile surface fragmentation distribution. In the present invention, oblique photography by an unmanned aerial vehicle is used, and three-dimensional model reconstruction is performed by capturing a blasting area image, so that a feasible aerial survey scheme is formulated, and the integrity of collected data is good; and reverse modeling of the blasting area is performed by means of the steps of feature point extraction, spatial information conversion, point cloud generation, grid generation, etc.; voxel grid downsampling, point cloud matching and error detection are used to perform registration on point clouds of a target area before and after blasting, so that a good effect is achieved; and a color-based region growing algorithm is used to perform coarse segmentation of point cloud features on an ore-rock block on the surface of a muck pile, and a PointNet++ algorithm is used to perform fine segmentation of point cloud features on the ore-rock block on the surface of the muck pile, so that the muck pile throw distance and the muck pile surface fragmentation distribution are calculated.

The invention discloses an intelligent operation and maintenance management system and method based on a charging pile, and belongs to the technical field of fault early warning, and the method comprises the steps: building a unified time sequence operation data matrix through collecting multi-source state data generated in the operation process of the charging pile; key features are extracted to construct feature vectors, and a multi-classification neural network model is utilized to evaluate a health state; a micro-degradation evolution path model is constructed in combination with the health trend in the continuous observation period, and a fault prediction curve is generated; performing similarity matching on a prediction result and a fault prior curve library, calculating a risk weight coefficient, identifying potential fault nodes and outputting an early warning list; constructing a regional task scheduling graph based on the high-risk pile position, fusing geographic position, power level and residual life information, and optimizing to generate an operation and maintenance path and a resource configuration scheme; according to the method, the fault prediction accuracy and operation and maintenance efficiency of the charging pile can be remarkably improved, and intelligent operation and maintenance and response optimization are realized.

The invention relates to the technical field of pile foundation monitoring, and discloses a pile foundation state real-time monitoring and diagnosis system based on a digital twinborn technology. The system comprises a multi-source data acquisition module, a data confidence evaluation module and an acoustic emission monitoring decision module. The multi-source data acquisition module comprises a plurality of sensor groups deployed at different depths of a pile foundation, each group comprises a strain sensor, an acceleration sensor, an acoustic emission sensor and a temperature sensor, and pile foundation data can be acquired in multiple dimensions; the data confidence evaluation module receives original data, generates a correction data sequence through time sequence noise separation and reconstruction, and calculates data confidence according to correction data distribution dispersion; the acoustic emission monitoring decision module judges whether acoustic emission monitoring is started or not according to the data confidence coefficient, and controls the acoustic emission sensor array at the top of the pile foundation to collect acoustic emission signals during starting. The system can comprehensively obtain pile foundation data, improve data accuracy, achieve early damage recognition and guarantee pile foundation safety.

The invention discloses a pile foundation integrity detection method which comprises the following steps: applying a variable amplitude sound wave excitation sequence to a pile foundation structure, collecting acoustic response signals under different excitation amplitudes, and obtaining nonlinear dynamic response data reflecting material rheological characteristics; performing frequency dispersion analysis and hysteretic characteristic analysis on the nonlinear dynamic response data, extracting a stress memory dissipation coefficient and a nonlinear memory capacity index, and establishing a time-varying characteristic curve; according to an abnormal change interval and a memory capacity fading rate of the time-varying characteristic curve, determining a defect position and a defect type, and obtaining an evolution state parameter of the defect; and based on the historical change trend of the evolution state parameters, the material degradation rate and the structure damage development rate are calculated, the health state index of the pile foundation structure is predicted, and a full life cycle performance evolution curve is formed. Through stress memory activation and nonlinear dynamic response analysis, the detection method considering the rheological property of the material is established, and the performance evolution rule of the pile foundation in the whole life cycle can be accurately evaluated.

The invention provides a defect positioning accurate identification method considering the change of the section of a pile body, and the method comprises the steps: carrying out the simulation comparison of a constructed sound wave signal feature vector and a pre-constructed sound wave feature vector of a defect-free pile foundation, calculating the feature difference degree of the two, setting a difference degree threshold value, and if the feature difference degree exceeds the threshold value, carrying out the recognition of the defect-free pile foundation. If yes, preliminarily determining that the pile body has defects; on the basis of the adjusted sound wave detection, sound wave signals are collected again, signal processing and feature extraction are conducted through a self-adaptive path tracking algorithm, and the three-dimensional coordinates of the defect in the pile body are calculated according to the time delay and the propagation speed of reflected waves by combining the established multi-reflection model and an inversion sound wave propagation path; and according to the calculated three-dimensional coordinates of the defect reflection points, the influence degree of the defects on the bearing capacity of the pile foundation is calculated by combining the defect size estimated through the amplitude of reflected waves, the defect type recognized through waveform features and the strength parameter of a pile body material through a finite element method.

The invention relates to the technical field of pile foundation construction, in particular to a pile foundation pore-forming quality intelligent management and control method and system based on drilling machine working parameters, and the method comprises the following steps: constructing a data acquisition array, and obtaining key working parameters of a drilling machine and pile foundation pore-forming quality index data based on the data acquisition array; establishing a standard time sequence database; building a dynamic correlation model based on the standard time sequence database, and building a quality judgment standard of the pile foundation pore-forming based on the dynamic correlation model; dynamically predicting the pile foundation formed hole according to the dynamic correlation model to obtain the quality state of the pile foundation formed hole, and obtaining a quality deviation result of the pile foundation formed hole in combination with the quality judgment standard; and a regulation and control strategy of the drilling machine is constructed based on the quality deviation result, the key working parameters are adjusted according to the regulation and control strategy, and intelligent control over the quality of pile foundation hole forming is achieved. Intelligent control over the pile foundation hole forming quality is achieved through driving of the working parameters of the drilling machine.

The invention provides a method and system for predicting the uplift bearing capacity of a mud mixed pile. According to the method, modeling is carried out by integrating geomechanics and monitoring data. The method comprises the following steps: firstly, acquiring geometric parameters of a multi-layer heterogeneous soil layer interface, constructing a database through a mechanical test, and improving the reliability by combining gradient recognition and cooperative verification of pile deformation and load data; dividing longitudinal segmentation units, adjusting anti-slip parameters to generate a space mapping table, establishing a debonding dynamic model containing an interlayer retardation effect, simulating a debonding expansion process, and outputting load evolution data. And judging the maximum load of the critical state based on data convergence analysis, and taking the maximum load as a predicted value of the uplift bearing capacity. According to the technical scheme, through dynamic parameter mapping and debonding process simulation, accurate prediction of the uplift bearing capacity of the pile foundation in a complex soil layer is achieved, and the defect that a traditional model is insufficient in characterization of interlayer heterogeneity and dynamic debonding behaviors is overcome.

The invention relates to the technical field of civil engineering, and discloses a foundation pit slope protection pile concrete strength evaluation and intelligent reinforcement integrated method which comprises the following steps: S1, data acquisition and transmission: acquiring a slope protection pile structure state and environmental parameters in real time through a multi-source sensor network, the sensor network comprises a mechanical sensor, an environment sensor and a hydrological sensor which are deployed in a spatial distribution manner; s2, data preprocessing and feature extraction: performing space-time fusion processing, including noise suppression, space-time reference unification and multi-dimensional feature extraction, on the acquired multi-source sensing data, and generating a structured feature matrix; according to the technical scheme, the multi-source sensor network and the deep learning model are combined, the strength of the foundation pit slope protection pile concrete is monitored and evaluated in real time, the accurate and lossless strength prediction effect is achieved, the problems of destructive testing and precision limitation are avoided, and the test efficiency is improved. The defects of low precision and poor real-time performance under complex geological conditions in the prior art are overcome.

The invention provides a prefabricated pipe pile construction total factor intelligent management method and system based on the digital twinborn technology, and relates to the technical field of construction management, a three-dimensional model is established through the BIM technology, pipe pile attributes are bound, construction data are collected in real time through the Internet of Things, and a dynamic digital twinborn body is formed. Through integration of an API interface and a project management platform, an AI algorithm is used for analyzing data, resource allocation and construction plans are dynamically adjusted, instructions are optimized and pushed to the site, construction efficiency and installation precision are improved, and the problems that traditional management is low in efficiency and large in error are solved.

The present invention provides an intelligent construction control method, apparatus, and system for splitting jet grouting mixing piles, relating to geotechnical engineering. The method includes receiving real-time perception information from a construction information self-perception system, which includes drilling depth, drilling rig output power, drill bit torque, drill rod axial force, and pore water pressure of the drill bit. This information is input into a trained machine-learning model for stratum information, establishing a nonlinear implicit correspondence relationship between the perception information and construction stratum conditions, and outputting real-time stratum state information. The real-time stratum state information is then input into a trained self-matching machine-learning model for optimal construction parameters, establishing a nonlinear implicit correspondence relationship between the stratum state information and optimal construction parameters, and outputting current optimal construction parameters. This invention enables real-time intelligent inversion of stratum information and dynamic self-optimization control of construction parameters.

The invention provides a desert photovoltaic support pile foundation uplift bearing capacity detection and construction parameter optimization method, and relates to the technical field of construction optimization, and the method comprises the following steps: dividing construction subareas according to soil parameter information of a desert target area, carrying out test pile detection in each construction subarea, and establishing a distribution rule of uplift bearing capacity of a pile-soil interface; the pile foundation diameter, the pile foundation burial depth and the pile foundation surface treatment mode serve as variables, a multi-objective optimization function including an uplift bearing capacity stability objective, an uplift bearing capacity redundancy objective and a construction efficiency objective is established, and the weight coefficient of the multi-objective optimization function is dynamically adjusted along with the environment temperature and sand bed migration; solving the multi-objective optimization function by adopting a particle swarm algorithm to obtain an optimal construction parameter combination of each construction subarea; and the optimal construction parameter combination is adopted for pile foundation construction, a pile foundation is selected for uplift bearing capacity rechecking, a rechecking result is fed back to the multi-objective optimization function, and dynamic fine adjustment is conducted on the construction parameters.

The invention relates to a machine learning-based scouring damage bridge buckling load prediction method and system, and belongs to the technical field of machine learning and structural engineering. The prediction method comprises the steps of obtaining original data, preprocessing the original data, constructing a LightGBM machine learning model, and evaluating and verifying the constructed model by adopting multiple performance evaluation indexes. According to the method, key variables such as bridge structure parameters and foundation soil physical property indexes are comprehensively considered, a large sample database covering a large number of scouring combinations is constructed, and the critical buckling load of the structure is accurately calculated as an output target by using a pier-bearing platform-pile structure system buckling analysis method after scouring damage based on an energy method. A multivariable nonlinear mapping relation is established in combination with an ensemble learning algorithm, traditional complex calculation is replaced, and the method has good engineering adaptability and universality and can be suitable for rapid prediction of the buckling bearing capacity under different parameter bridges, different foundation forms and complex scouring conditions.

The invention relates to the field of ocean engineering construction monitoring, and discloses a pile body penetration identification method based on multi-modal perception and dynamic coupling modeling, which comprises the following steps: collecting pile body motion data by deploying vision, inertia and laser ranging sensor networks, constructing a pile body-foundation dynamic model in combination with finite element discretization, and constructing a pile body penetration identification model; separating vibration interference and real displacement by using extended Kalman filtering; the YOLOv8 model is improved to realize pile body contour tracking in a low-illumination environment, and multi-source data is fused to predict a pile body motion state; the high-precision penetration is calculated through vibration compensation correction, weighted fusion and dynamic threshold judgment, and grading alarm and construction control instructions are triggered. According to the invention, the problems of low multi-sensor data fusion precision and weak anti-interference capability in a complex marine environment are solved, real-time monitoring and closed-loop feedback of the penetration of the pile body are realized, and reliable technical support is provided for piling construction.

The invention belongs to the technical field of supporting structure performance evaluation, and particularly discloses and provides an NT-CEP pile wall anchor foundation pit supporting structure anti-overturning evaluation method which comprises the steps that in the whole construction period, the construction process is divided into a plurality of continuous load steps based on excavation and supporting procedures; under each load step, pile body strain, arc-shaped wall stress, anchor rod axial force and deep soil displacement are synchronously collected, and a multi-source response data set is constructed; constructing an NT-CEP pile-arc wall-anchor rod coupling model based on the design drawing; carrying out inversion analysis through the model, and solving the load sharing proportion and the overall anti-overturning safety coefficient of each component under each load step; and continuously identifying the abnormal component based on the load sharing proportion, and dynamically analyzing and outputting an anti-overturning capability evaluation index based on the change trend of the safety coefficient. According to the method, dynamic refined evaluation of the construction process is realized, and the authenticity and perspectiveness of an evaluation result are remarkably improved.

The invention provides a charging pile operation and maintenance management method and system. The method comprises the following steps: acquiring an operation state parameter and an environment parameter of the charging pile; inputting the operation state parameters and the environment parameters into a multi-task prediction model to obtain the residual life and confidence interval of each key component of the charging pile; wherein the multi-task prediction model comprises a shared feature extraction module and a plurality of life prediction branches, and each life prediction branch corresponds to one key component; and determining a fault risk level of the charging pile based on the residual life and the confidence interval of each key component, and performing operation and maintenance management on the charging pile based on the fault risk level. Accurate and reliable data basis can be provided for operation and maintenance management of the charging pile, conversion of operation and maintenance of the charging pile from passive response to active prevention can be achieved, and remarkable breakthrough in the aspects of prediction precision, cost efficiency and system reliability is achieved.

According to the offshore wind turbine single pile rigidity numerical simulation method considering long-term circulation and scouring, the accuracy and the calculation convenience are both considered in model building through the linear elastic steel pipe pile model and the Mohr-Coulomb soil body model. By calculating the horizontal ultimate bearing capacity of the single pile, combining the ultimate analysis method and the p-y curve calculation method, the influence of different scouring depths on the fan is considered, the horizontal load bearing capacity and inherent frequency evolution of the single pile are comprehensively measured, and a basis is provided for design and safety evaluation. A sandy soil rigidity attenuation model is established for secondary development of Abaqus and verified, and the rigidity change of sandy soil under long-term cyclic loading is considered, so that the simulation precision is improved. And the numerical model is tested and verified to ensure the reliability. And the accumulated lateral displacement of the steel pipe pile can be accurately predicted, the influence rule of cyclic load times, soil elasticity modulus and embedding depth factors on displacement development is disclosed, wind power engineering design optimization is assisted, and economical efficiency and safety are improved.

The invention provides a single pile bearing capacity prediction method based on an XGBoost machine learning algorithm, comprising the following steps: (1) acquiring and preprocessing test data including soil layer input parameters, pile parameters and construction parameters, and dividing the preprocessed test data into a test set and a training set; (2) constructing a machine learning model in a pile foundation design stage according to the soil layer input parameters and the pile parameters, constructing a machine learning model in a pile foundation construction stage according to the soil layer input parameters, the pile parameters and the construction parameters, and respectively optimizing the two machine learning models by adopting a Bayesian optimization algorithm; and (3) the two optimized machine learning models are used for calculating the single-pile bearing capacity in the pile foundation design stage and the single-pile bearing capacity in the construction stage according to needs. According to the method, influence factors of all stages are comprehensively considered, algorithm learning is carried out on the influence factors, the bearing capacity of the reinforced concrete prefabricated pipe pile can be rapidly and effectively predicted, and the method can be used for optimizing the pile length design, reducing the pile material cost and improving the construction quality.

The invention discloses a method and a system for estimating the pile length of an engineering pile. The method comprises the following steps: acquiring a multi-source detection signal reflecting the striking response of a pile hammer and stratum prior information; on the basis of stratum prior information, carrying out physically-driven adaptive analysis on sound wave components in the multi-source detection signals, and extracting preliminary sound wave reflection features; performing correlation verification on the preliminary sound wave reflection characteristics by using vibration components in the multi-source detection signals to generate corrected reflection characteristics; and the signal quality of the corrected reflection characteristics is evaluated, an intelligent hammering control instruction is generated according to the signal quality so as to execute closed-loop enhanced signal excitation, finally, multi-modal information before and after enhancement is fused, and a final pile length estimated value is obtained through calculation. Through sound and vibration coupling verification and intelligent active excitation, the accuracy, reliability and detection depth of pile length estimation under the conditions of strong noise, complex stratum and large depth are improved.

The invention discloses an offshore pile foundation evaluation system construction method based on evolutionary algorithm optimization and PIML linkage. A standardized engineering data set is obtained; parameterizing the pile-soil coupling dynamics boundary condition, the pile-soil interaction specification criterion and the limit state and use state criterion, and storing the parameters as a physical constraint set; obtaining an initial performance evaluation model; generating an alpha evolutionary optimization performance evaluation model; outputting a bearing capacity-settlement relation index, a dynamic and static stiffness degradation curve index and a residual bearing capacity probability distribution index to form a structural performance index set; generating a credible structure performance index set containing confidence boundaries and extracting an influence degree sorting result; and automatically generating a structured evaluation report according to a verification result. The model output has physical consistency and engineering interpretation all the time, and the problem that a traditional pure data driving model is unreliable in result under data sparsity and environment sudden change is remarkably solved.

The invention discloses a bridge engineering-oriented foundation pile quality detection system, which relates to the technical field of engineering quality detection, and comprises a data acquisition module configured to acquire detection data including foundation pile construction parameters, geological environment data, stress wave signals and geometric dimension parameters, the wave velocity correction module is configured to correct a standard wave velocity through a dynamic coupling algorithm, the defect depth calculation module is configured to calculate the defect depth of a foundation pile defect, and the credibility evaluation module is configured to output a credibility correction coefficient and feed back the credibility correction coefficient to the wave velocity correction module by quantifying errors of detection data; according to the method, through multi-physical field wave velocity correction, pile diameter-defect depth double correction and credibility quantitative iteration, a high-precision, objective and traceable integrated solution is systematically provided for bridge engineering foundation pile quality detection.

The invention belongs to the technical field of charging pile thermoelectric control, and particularly relates to a high-power liquid cooling charging pile charging power optimization scheduling method and system. The method comprises the following steps: collecting operation state parameters of a plurality of liquid cooling charging piles; identifying a target pile body in a charging state or a standby state; calculating a theoretical energy supply upper limit based on an electrothermal model; combining the vehicle charging demand, the priority, the battery state and the power grid load to construct a power scheduling model and execute distribution; power adjustment and redistribution are carried out when cooling abnormity or connection faults are detected; and finally, a scheduling control instruction is generated and executed in real time. The system comprises an information acquisition module, a state identification module, a model calculation module, a power distribution module, a dynamic adjustment module and an instruction issuing module. According to the invention, efficient, safe and dynamic power scheduling control of the high-power liquid cooling charging pile in a multi-device parallel connection scene is realized.

The invention relates to the technical field of constructional engineering detection, and discloses a cast-in-place pile cross-hole CT detection method, system and equipment based on multi-wave integration, and the method comprises the steps: laying inspection holes, deploying a time-sharing excitation sensor, collecting multi-wave data, fusing the multi-wave data, correcting and compensating, and constructing a joint objective function to invert material parameters. A three-dimensional model is generated, defect space information is extracted, and hidden defect accurate detection is achieved; the system comprises a probe control module, a data acquisition module, a data processing module, a three-dimensional modeling module and a user interaction module. The device comprises an integrated probe device and a three-layer sensing structure from inside to outside. According to the method, anti-interference detection is achieved through multi-wave-type collaborative excitation and layered shielding probe design, and pile body defects are accurately positioned in combination with cross-wave-type data fusion processing and a self-adaptive inversion algorithm; defect space distribution is visually quantified based on dynamic threshold three-dimensional modeling, an integrated system adapts to a complex engineering environment, and the detection precision and efficiency of the cast-in-place pile are remarkably improved.

The invention discloses a cast-in-situ bored pile digital construction control method which comprises the following steps: S1, collecting historical data, and establishing a cast-in-situ bored pile construction information database; s2, geological modeling is established, and a pile hole area three-dimensional stratum and soil layer distribution corresponding to all pile holes are generated in a fitting mode; s3, according to the cast-in-situ bored pile construction information database, finding out average construction efficiency and cost data under various hole forming processes of similar stratums in various stratums of a pile hole area, and calculating the construction period and cost of different hole forming processes of a single pile for construction management personnel to select the hole forming processes and equipment specifications; s4, monitoring early warning and auxiliary decision making in construction; and S5, post evaluation analysis. According to the system and the method, key construction parameters of the bored pile are automatically monitored by relying on core construction equipment and devices, the production dynamic state of the bored pile is visually displayed through online monitoring and early warning of the key parameters by the system and the platform, a friendly interactive interface is provided for operators, and construction parameter selection is assisted.

The invention relates to a method and device for determining the fatigue loss of an offshore photovoltaic pile foundation stand column and a medium, and relates to the technical field of fatigue analysis. The method comprises the steps that pile foundation load dynamic data of the pile foundation stand column are determined according to historical stand column state data; determining a target residual life of the three-dimensional digital twin model under an extreme service condition based on the pile foundation load dynamic data; based on the target residual life, the column material parameters of the pile foundation column, the pile foundation load dynamic data and historical environment variable data corresponding to the historical column state data, training a to-be-trained neural network model to obtain a fatigue life prediction model; and inputting real-time model state data of the three-dimensional digital twinborn model into the fatigue life prediction model to obtain real-time residual life probability distribution of the three-dimensional digital twinborn model, and determining real-time fatigue loss of the pile foundation stand column according to the real-time residual life probability distribution. The accuracy of the fatigue loss analysis result is improved.

The invention discloses a monitoring control system for static pressure pile machine construction. The monitoring control system comprises a central control system, a man-machine interaction screen display module, a static pressure pile machine operation module, a construction data acquisition module, a welding quality monitoring module, a pile foundation bearing capacity prediction module and a cloud data storage and presentation module. The invention further discloses an implementation method of the monitoring control system for static pressure pile machine construction. Through linkage of the central control system and the static pressure pile machine, efficient automation and accurate control of the construction process are achieved; in the construction process, the leveling and pile pressing operation of the pile clamping platform and the pile pressing platform can be regulated and controlled through construction data collected in real time, it is ensured that the prestressed concrete pile vertically enters the soil and reaches the designed pile pressing depth, and deviation is avoided; the welding quality monitoring module can monitor the welding quality in real time, and it is ensured that the construction quality meets the standard.

The invention provides a large-diameter long-pipe-section pipe pile hoisting path planning method and device, and relates to the technical field of cranes. According to the method, motion state parameters of pipe sections and pipe piles are obtained in real time, and in combination with three-dimensional environment sensing data of a hoisting area, a space operation diagram containing obstacles and a safety buffer area is constructed; generating a plurality of groups of candidate paths combined by speeds and steering angles in the fan-shaped region according to a set angle interval by taking the current motion direction as a reference; removing paths conflicting with obstacles through collision detection, and reserving an effective path set; based on multi-dimensional feature analysis of a security item index, a smoothness index and a progress index, a scoring function is constructed by presetting a weight, and an optimal path is dynamically selected; path planning is carried out through a fixed time interval until the pipe pile stably arrives at a target point; through real-time environment modeling and dynamic path planning, the problems of obstacle avoidance and efficiency of large-diameter pipe pile hoisting in a complex scene are solved, and hoisting safety and operation accuracy are remarkably improved.

The invention discloses a bridge pile foundation construction perpendicularity real-time detection system and method based on an unmanned aerial vehicle group, and relates to the technical field of bridge pile foundation construction, and the system comprises a main detection unmanned aerial vehicle module which is used for vertically hovering above a pile hole and scanning along a hole wall to generate point cloud data; the slave positioning unmanned aerial vehicle group module is arranged on the periphery of the pile hole and provides positioning compensation for the master detection unmanned aerial vehicle module; the ground reflective target is arranged at the opening part of the pile hole and is used for aligning the initial positioning of the main detection unmanned aerial vehicle module with the point cloud coordinate system; the edge calculation terminal module is arranged on a construction site, receives and processes the point cloud data and calculates perpendicularity deviation; the result generation module visualizes the analysis result and outputs and feeds back the result to constructors, and the problems that a traditional method is low in efficiency and poor in precision and depends on manual work are solved.

The invention provides a grain pile form dynamic monitoring method based on an intelligent visual system, and the method comprises the steps: extracting a speed field and a direction field of grain flow motion from video data in a grain flow falling process through employing an optical flow field analysis method, and obtaining the initial information of a grain flow track; according to the track change information, the initial diffusion range of the grain flow at the bin bottom is analyzed, whether the diffusion mode is uniform or not is judged, and if diffusion is not uniform, the corresponding area is marked as an irregular diffusion area; a dynamic diffusion model is established based on the track change information, the dynamic diffusion model is adopted to process an irregular diffusion area, and the accumulation speed and the form change trend of the grain pile are predicted in combination with the speed field and the direction field of grain flow movement; and according to the predicted grain pile stacking speed and form change trend, operation parameters of the conveying equipment are adjusted, and the operation parameters comprise the speed and angle of a conveying belt.

The invention discloses a secant pile nondestructive testing system and method based on multi-source sensing data fusion, and belongs to the technical field of civil engineering detection, the full pile long strain is axially monitored through an optical fiber, three-dimensional imaging and stress wave global scanning of a secant interface are realized through a sound wave transmitting-receiving probe pair, line-plane-body multi-level detection is formed, and the secant pile nondestructive testing system and the secant pile nondestructive testing method based on multi-source sensing data fusion are obtained. Through cooperative complementation of strain / temperature data, stress wave data and wave velocity three-mode data, defect type probabilistic recognition is achieved, the weight of each sensor is dynamically adjusted according to the real-time environment, and the defect comprehensive probability is calculated and output. And dynamic weight distribution is realized. Through multi-source sensing data fusion analysis, pile body integrity, occlusion interface compactness and temperature strain distribution characteristics can be accurately identified, and the method has the characteristics of multiple detection dimensions, high precision and strong interference resistance.