912 results about "Pile" patented technology

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 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 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 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 discloses a scheduling management method and system for an unmanned finished product warehouse area, and the method comprises the following steps: analyzing material attributes and order information when materials are warehoused, selecting a raw material area FIFO order scheduling strategy or a finished product area customer order clustering strategy according to the type of the warehouse area, candidate stack positions are generated in combination with a warehouse-area-row-column-layer-stack position five-level coding system, the optimal stack position is determined after stack safety verification, and a crown block operation instruction is generated; when the crown block works, acquiring starting and target coordinates, loading the digital twin model of the reservoir area to generate an initial path, and re-planning a high-risk path by adopting an improved path planning algorithm introducing a load coefficient and a congestion index through conflict risk assessment; and during multi-vehicle collaborative operation, task collaboration and path conflict avoidance are realized based on a distributed multi-agent architecture and a preset communication protocol.

The invention discloses a physical-data dual-drive bench blasting effect prediction method in the technical field of blasting construction and intelligent mining, which comprises the following steps of: dynamically calibrating a fractal dimension field, and constructing a rock blasting classification model by combining blasting indexes such as fractal gradient, rock density, uniaxial compressive strength and drilling speed; performing three-dimensional reconstruction on the geometric morphology of the step before blasting by using an unmanned aerial vehicle high-precision modeling technology, and importing a finite element simulation model to predict a key blasting effect; collecting an actual blasting effect after blasting, and correcting parameters of the numerical simulation prediction model; and constructing a data set of actual lumpiness distribution of the muck pile, establishing a mapping relation between the rock detonability grade and the charge density, and optimizing the differential time sequence and the single-hole charge amount. According to the invention, through fractal dimension field dynamic mapping and real-time feedback control, accurate adaptation of blasting energy and a rock mass structure is realized.

The invention discloses a method for analyzing the uplift bearing capacity of a PHC spiral pipe pile based on numerical simulation. The method comprises the following steps: vertically screwing a pile body into a designed elevation by adopting intelligent rotary excavating equipment provided with a special drill bit, and monitoring the press-in speed, the rotating speed, the torque and the perpendicularity in real time; recording a press-in resistance-depth curve, a torque-depth curve and pile body perpendicularity data; establishing a three-dimensional refined numerical model containing a real spiral blade, and calibrating soil key parameters through a parameter inversion algorithm to obtain a calibrated numerical model; an anti-pulling load is simulated on the calibrated numerical model, and the ultimate anti-pulling bearing capacity and the failure mode are analyzed; based on a multi-working-condition simulation result, a practical calculation formula of the ultimate uplift bearing capacity is obtained through regression analysis; and model inversion is driven through construction data, refined numerical simulation is combined, the uplift bearing capacity prediction precision is improved, and a reliable basis is provided for the uplift design of the PHC spiral pipe pile.

The invention relates to the technical field of geotechnical engineering structures, in particular to a multi-factor coupling CFG pile full-life failure probability prediction and optimization method. Comprising the following steps; the method comprises the following steps: S1, integrating resistance degradation, pile-soil interaction, geological evolution and load time-varying characteristics to construct a limit state model; s2, on the basis of the limit state model constructed in the step S1, Monte Carlo simulation circulation is used, the failure probability is calculated, and a CFG pile system failure probability change graph is obtained, S3, parameters in the limit state model are optimized, after each parameter is optimized, the steps S1 to S2 are carried out again, and the optimized CFG pile system failure probability change graph is obtained; according to the method, the accuracy and the adaptability of full-life prediction and optimization of the CFG pile are improved.

The invention relates to the technical field of quality regulation and control, in particular to a self-adaptive pile forming quality regulation and control system and method for soft foundation treatment pile construction. The method comprises the steps that a construction parameter acquisition unit acquires key construction parameters in real time through a multi-type sensor network; the data preprocessing and feature extraction unit is used for preprocessing the collected key construction parameters and extracting key feature parameters reflecting the construction stability and the pile forming uniformity; and the pile-forming quality judgment and deviation recognition unit is used for comparing the key characteristic parameters with a preset pile-forming quality threshold model in real time, judging the quality state of the pile-forming process and outputting a deviation category and a deviation strength index. According to the method, the limitation that judgment only depends on a single parameter threshold in the prior art is broken through, and a multi-mode coupling resonance intensity evaluation algorithm is provided for solving the problem that nozzle abrasion is high in concealment and great in influence.

The invention relates to a double-pile foundation seabed dynamic response simulation method under the wave action based on a multi-physics field coupling model. According to the method, a Wave-Stratum-Seabed Interference (WSSI) model integrating wave-seabed-structure interaction is constructed, the interaction between waves and a double-pile foundation under different pile spacing ratios and incoming flow deflection angles is simulated on an OpenFOAM platform through a finite volume method (FVM), and then the dynamic response of a seabed is analyzed. The model comprises a wave sub-model and a seabed sub-model based on the Biot three-phase consolidation theory, and key parameters such as free surface rise, dynamic wave pressure, vertical pore water pressure, vertical effective normal stress and liquefaction depth can be accurately calculated. An effective simulation tool and method are provided for pile foundation design and safety evaluation in ocean engineering, optimization of the pile foundation layout is facilitated, and the stability of an ocean structure is improved.

The invention provides a method for predicting a cabin material pile in a ship loading process of a ship loader based on a diffusion network, and relates to the technical field of bulk cargo loading and unloading automation. Three-dimensional point cloud data in a cabin in the ship loading process is acquired, the aligned three-dimensional point cloud data is projected to a two-dimensional plane parallel to the bottom of the cabin, and a material pile depth map is generated; constructing a training sample pair based on the historical ship loading operation data, and training a conditional diffusion network model to obtain a target conditional diffusion network model; inputting the material pile depth map of the first time period and planned blanking point information corresponding to the second time period into a target condition diffusion network model to obtain a material pile depth map corresponding to the second time period; and taking the material pile depth map corresponding to the second time period as an initial depth map for prediction in the next time period to carry out iterative prediction, and outputting a material pile depth prediction set after the material pile depth map prediction in all time periods in the ship loading process is completed. According to the invention, the fidelity and authenticity of the predicted material form can be improved.

The invention relates to the technical field of electronics, and discloses a method, a device and equipment for predicting and calculating pile sinking dynamic resistance of an offshore wind power steel pipe pile. The method comprises the following steps: inputting a target pile body position, a target pile-soil relative speed, target pile sinking time and target soil layer parameters of the offshore wind power steel pipe pile in a target pile sinking state into a pre-constructed reduction coefficient prediction model, the reduction coefficient prediction model outputs a target moving side friction reduction coefficient and a target moving end resistance reduction coefficient, and the reduction coefficient prediction model is constructed based on a physical information neural network model; and solving a pre-constructed pile sinking dynamic resistance calculation equation by utilizing the target movable side friction reduction coefficient, the target movable end resistance reduction coefficient, the target pile-soil relative speed and the target soil layer parameters to obtain a target pile sinking dynamic resistance predicted value of the offshore wind power steel pipe pile in the target pile sinking state. And the accuracy of the pile sinking dynamic resistance prediction result is effectively improved.

The invention relates to the technical field of grouting systems, and discloses an intelligent grouting control system for a high-pressure jet grouting pile, which is characterized in that a closed-loop control system integrating real-time data acquisition, intelligent parameter regulation and control, accurate process execution and quality evaluation feedback is constructed; the construction links which originally depend on manual experience and are scattered and independent are integrated into a highly-collaborative intelligent whole. The construction state and the stratum condition can be sensed in real time, automatic decision making is carried out, the optimal process parameters are executed, the pile forming effect is evaluated in real time, feedback optimization is carried out, and therefore passivity and uncertainty of a traditional construction mode are changed fundamentally. And finally, the effect of improving the extensive and empirical manual operation of the jet grouting pile construction into a fine and intelligent modernized technological process is achieved, the uniformity, stability and reliability of the single pile construction quality are effectively ensured, and the pile forming quality level and the construction efficiency of the whole project group are continuously improved through learning iteration of historical data.

The invention discloses a cast-in-place pile integrity detection method based on a heat conduction model, and relates to the technical field of cast-in-place pile detection. The method comprises the following steps: firstly, constructing a cast-in-place pile heat conduction characteristic template library comprising standard heat conduction curves corresponding to various integrity states, wherein each standard curve comprises a plurality of key temperature nodes; generating a comprehensive heat conduction curve by fusing the standard curves of the same type of integrity states; acquiring actual temperature monitoring data of the to-be-detected cast-in-place pile, selecting a corresponding comprehensive heat conduction curve, and extracting a reference heat conduction curve; marking abnormal conduction nodes in the reference conduction curve to obtain a corrected conduction curve; and establishing a heat conduction anomaly analysis model to verify and correct key temperature nodes of the conduction curve, determining defect nodes, generating an integrity report containing position information of the defect nodes, and completing detection. Various quality conditions of the cast-in-place pile can be covered, advantages and characteristics of curves are integrated, and detection errors and unnecessary links are reduced.

The invention relates to the technical field of slope reinforcement, and discloses a slope anti-slide pile reinforcement method based on intelligent monitoring, which comprises the following steps: firstly, acquiring slope data of various monitoring points such as displacement, stress, pore water pressure and the like, extracting characteristic parameters, matching to obtain parameter pairs, screening the parameter pairs through bidirectional verification and consistency verification, and then, carrying out slope anti-slide pile reinforcement. And constructing a reinforcement model by taking the parameter pairs as reference points, and generating a standard reinforcement scheme by adopting layered parameter packaging. And meanwhile, a reinforcement path is generated according to side slope geological data, grid areas are divided by constructing a two-dimensional geological distribution diagram, core reinforcement areas are classified, priorities are marked, an initial reinforcement path is iteratively optimized by utilizing a fitness evaluation system in combination with a genetic algorithm, and an optimal reinforcement path is obtained. And finally, on-site verification is conducted on the standard reinforcement scheme, and parameters such as the pile body distance and length are adjusted according to the mean square error of verification data and original data. According to the method, the reinforcement precision and efficiency are improved, and the reliability and adaptability of the scheme are ensured.

The invention provides a large-diameter shield tunnel anti-floating method and system based on an anti-floating coefficient, and belongs to the technical field of tunnel anti-floating. The method comprises the steps that static and dynamic multi-dimensional anti-floating parameters are obtained, anti-floating coefficients are calculated after preprocessing, an early warning value is set according to the anti-floating coefficients, an anti-floating pile arrangement scheme is selected, and pile arrangement design is optimized through a genetic algorithm; a stratum is reinforced through partition differential grouting, parameters of a steel frame and a pre-stressed anchor cable are optimized by combining a genetic algorithm aiming at a gate-type structure, and a collaborative anti-floating reinforcing system is constructed; and real-time anti-floating monitoring is carried out, a tunnel anti-floating digital twin mirror image is established based on data, state prediction and system dynamic optimization are carried out, and a final reinforcement result is output. According to the method, the problem that in a traditional method, the stratum resistance static value is not matched with actual construction disturbance is effectively solved, and the accuracy and engineering applicability of anti-floating design are remarkably improved.

The invention discloses a charging pile module dynamic power distribution control method and a related device, which are used for improving the stability and prolonging the service life of a charging pile during use. The method comprises the following steps: collecting real-time operation data of all charging pile modules in use in a charging pile layout matrix; performing power demand prediction on the real-time operation data according to historical operation records through a prediction model to obtain a prediction result; generating a virtual load mapping space according to the prediction result and an energy coupling relationship, wherein the virtual load mapping space is a mapping model of a charging pile node and an energy flow, wherein the charging pile node takes a charging pile module in use as a node; inputting the virtual load mapping space into a multi-agent collaborative optimization algorithm to obtain a target power value of a charging pile node so as to control a corresponding charging pile module according to the target power value; and recording the control feedback of the target power value as the historical operation record.

The invention relates to the technical field of geotechnical engineering monitoring, and discloses a deformation coordination analysis system for a row pile-inclined throwing support system. The system comprises a multi-source data acquisition module, a data fusion processing module, a dynamic association model construction module, a risk coupling evaluation module, a deformation trend prediction module, a coordination blind area early warning module and an operation strategy optimization module. The multi-source data acquisition module acquires real-time deformation monitoring data and stress response data of the row piles and the inclined throwing supports; the data fusion processing module preprocesses the data and extracts features; the dynamic association model construction module constructs a deformation coordination association model based on the processed data and features; the risk coupling evaluation module analyzes risk coupling characteristics; the deformation trend prediction module predicts an aging trend residual error; the coordination blind area early warning module generates dynamic early warning; and the operation strategy optimization module optimizes a support operation strategy. The system can comprehensively master the system deformation coordination state, identify the risk in advance, and guarantee the engineering safety.

The invention belongs to the technical field of geotechnical engineering precision monitoring and calculation intelligent crossing, and discloses a deep foundation pit support pile micro-deformation capturing and predicting method and system fusing physical mechanism constraints, and the method comprises the following steps: obtaining and fusing a static physical field and a dynamic displacement field of a deep foundation pit support pile; constructing an enhanced space-time field atlas containing physical boundary conditions; constructing a micro-deformation prediction model, constructing a physical information loss function based on a support pile beam unit fourth-order differential equation, and training the micro-deformation prediction model based on the physical information loss function to obtain a trained model; and inputting the enhanced time-space field atlas into the trained model, extracting spatial morphological characteristics of pile body micro-strain in the enhanced time-space field atlas, and obtaining a support pile deformation prediction result. According to the method, the technical bottleneck of'micro-feature annihilation 'of a general model is broken through, and high-fidelity capture of micron-sized deformation is realized.

The invention discloses a metering performance analysis method, system and device for a single charging pile, and a storage medium, and relates to the technical field of charging pile metering performance analysis, and the method comprises the steps: collecting the operation data of a charging pile, calculating the error between an actual metering value and a theoretical estimation value of the charging pile, and calculating the calculation result; the total deviation degree of the error is quantized through a mean square error method; based on the Bayesian theory, carrying out uncertainty evaluation on the calculated errors, and integrating the distribution characteristics of the errors; constructing a multi-index comprehensive evaluation system, performing anomaly detection on the charging pile, generating an identification result of an abnormal charging pile and root positioning information of metering deviation, formulating an operation and maintenance strategy and a calibration period, and optimizing the multi-index comprehensive evaluation system; according to the method, the metering performance of the charging pile can be comprehensively evaluated, a deviation source is accurately positioned, an error overrun pile is identified, error fluctuation is reduced, the evaluation accuracy and consistency are improved, and powerful support is provided for operation, maintenance and calibration.

The invention relates to the technical field of pile foundation testing, in particular to a test pile load testing method and a loading structure, and the method comprises the following steps: testing preparation: establishing a real-time data acquisition link; graded loading: graded loading: collecting pile top settlement-time sequence data in real time at the load holding stage of the current load stage; self-adaptive load holding judgment: calculating a settlement convergence trend based on settlement data acquired in real time under the current load level; if the prediction model judges that the settlement is fully converged under the current load level, directly entering the next load level without waiting for the settlement rate to be reduced to a fixed threshold value; if the prediction shows that the settlement cannot be converged or tends to be damaged, triggering damage early warning and turning to a damage treatment flow; continuously detecting, namely repeating the step of graded loading and the step of self-adaptive load holding judgment until a preset maximum test load is reached or damage is triggered; and evaluation: evaluating the bearing capacity of the pile foundation. The test efficiency is improved, and the intelligent degree is improved.

The invention relates to the technical field of Internet of Things control, in particular to an intelligent control method and system for pile machine monitoring and grout making and feeding linkage, and the method comprises the steps that grout making design parameters are configured in advance, and initial grout making is executed according to the grout making design parameters; the construction pile machine is positioned according to the pile position number, initial-stage slurry feeding is conducted on the positioned construction pile machine, a monitoring host is arranged to conduct real-time construction monitoring on the construction pile machine, and slurry feeding state information is collected; and an Internet of Things control platform is established, unit pile foundation construction is divided into a plurality of grout making and feeding stages according to the designed ash amount, each grout making and feeding stage is used as a monitoring unit, stage grout making of the next grout making and feeding stage is executed according to the grout feeding state information, and the grout feeding speed is synchronously controlled. By means of the system, the problem that in the existing pile foundation construction process, the slurry making and feeding quality is difficult to control accurately can be effectively solved, and automatic data collection, intelligent analysis and dynamic adjustment in the whole slurry making and feeding process are achieved.

The invention discloses a hoop diameter adjustment and operation method for an underwater robot to detect apparent defects of a circular pile, and the method comprises the steps: obtaining image data and sonar data, and obtaining an initial surface information data set; performing image enhancement and sonar filtering to obtain a clear surface information set; according to the clear surface information set, correcting an imaging error to obtain a corrected feature information base; according to the feature information base, identifying the defect area to obtain a defect distribution mapping graph; according to the defect distribution mapping graph, combining a preset sonar positioning result to obtain defect distribution information; according to the defect distribution information, the preset circular pile curvature and the preset defect density are combined, the adjustment amplitude of the hoop diameter is calculated, and an adjustment interval is obtained; according to the adjustment interval, the diameter configuration is iteratively optimized in combination with the preset geometric parameters and the preset fitness simulation result, and the optimal hoop diameter value is obtained; and according to the optimal hoop diameter value, an underwater installation control instruction is generated, and a final installation parameter scheme is output.

The invention relates to an intelligent monitoring system and method for the whole pile forming process of a cloth bag pile, and belongs to the technical field of intelligent monitoring systems.The intelligent monitoring method comprises the following steps that firstly, geological data are obtained and input into a database of the monitoring system; step 2, acquiring pressure, flow and temperature parameters of the concrete slurry by using a sensor, acquiring an unfolding form and a displacement parameter of the film bag, and transmitting the acquired parameters to a database of a monitoring system; 3, the monitoring system analyzes and processes the collected parameters, and carries out real-time early warning according to an analysis result; fourthly, the pile type and the pile forming quality of the cloth bag pile are detected through the nondestructive testing technology, and the pile forming quality is evaluated; 5, the pile forming quality is displayed through a terminal display module; the method has the beneficial effects that the whole process of pile forming of the cloth bag pile is monitored by constructing the concrete flow-film bag deformation coupling model.

The invention relates to the field of civil engineering automatic monitoring, and discloses a method for measuring cast-in-situ bored pile center deviation based on a three-point circle method.The method comprises the steps that the initial pose of an integrated frame installed on the pile top is accurately calculated through external equipment such as a total station, an internal tilt angle sensor is calibrated, and during real-time monitoring, the initial pose of the integrated frame is accurately calculated; the three-point coordinates on the contour of the circular target object are obtained through the three non-collinear laser distance measuring sensors in the frame, the relative position vector of the center of the circular target object relative to the frame is solved in real time through the three-point circle method, attitude compensation operation is conducted on the relative position vector, the attitude compensation is converted into a global coordinate system, and finally the attitude compensation is conducted through the initial translation vector. The real-time absolute coordinates of the target object are synthesized, and high-precision deviation monitoring is achieved. According to the method, initial calibration and real-time compensation are combined, errors caused by inclination of a monitoring reference are effectively eliminated, and the real-time performance, the automation degree and the data reliability of pile foundation construction deviation monitoring are improved.

The invention provides a vertical MJS grouting reinforcement system based on an intelligent cloud platform and a construction method, and the system comprises an intelligent front end device which comprises an underground pressure sensor, a tilt angle sensor and a slurry flow meter and is used for collecting grouting pressure, drill rod perpendicularity and slurry flow data in real time; the intelligent cloud platform comprises an Internet of Things module, an intelligent control module and a precise parameter construction module, and the Internet of Things module is connected with the intelligent front-end device and used for receiving collected data; the intelligent control module is used for collecting and recording grouting pressure, grout flow and underground pressure; the accurate parameter construction module is provided with a formation pressure sensor and used for collecting formation response signals and forming dynamic comparison of in-ground pressure and formation pressure, and grouting pressure, grout flow and jetting speed parameters are adjusted through the numerical difference of the in-ground pressure and the formation pressure to control the pile forming diameter. Intelligent monitoring and accurate control of grouting construction are achieved, efficiency and pile forming quality are improved, and manual intervention and stratum disturbance are reduced.

The invention discloses an anchor bar pile pulling resistance detection system and detection method, and relates to the technical field of data processing, first to fourth sensing modules are connected with a data processing module, and the first to fourth sensing modules are arranged on anchor bar pile pulling resistance detection equipment; the first sensing module measures the pulling resistance of the anchor bar pile; the second sensing module measures displacement data; the third sensing module measures the tension of the anchor bar pile; a fourth sensing module measures temperature data of the anchor bar pile in the drawing process; and the data processing module is used for drawing an anti-pulling force-displacement curve according to the anti-pulling force and displacement data, drawing a pulling force-displacement curve according to the pulling force and displacement data, and determining a detection result according to the anti-pulling force-displacement curve, the pulling force-displacement curve and the temperature data. According to the technical scheme, the detection efficiency can be improved, and the detection result can be obtained in time; and meanwhile, relevant data in the anti-pulling test process can be intelligently and automatically obtained, so that the detection accuracy can be improved.