690 results about "Stability assessment" patented technology

The invention provides an underground cavern surrounding rock parameter collaborative sensing method and system based on multi-source data fusion, and the method comprises the steps: firstly deploying a sensor group comprising an optical fiber strain sensor, a micro-seismic monitoring array, a three-dimensional laser scanner and an acoustic emission sensor, the optical fiber strain sensor is arranged along the axis of a cavern, the micro-seismic monitoring array is deployed according to a triangular network, and the three-dimensional laser scanner is arranged along the axis of the cavern; performing time synchronization on the sensor group through a synchronization pulse signal, establishing a local coordinate system at a central point of a cavern entrance, uniformly collecting data, generating a time-aligned data stream and a space-unified data set, and triggering associated sensors to enter a hierarchical response collection mode according to a strain rate and a vibration signal; and generating a multi-modal monitoring data set, finally carrying out dynamic weight distribution and conflict resolution processing on the multi-modal monitoring data set, extracting surrounding rock deformation characteristics and fracture event density, and outputting a surrounding rock stability evaluation result, thereby realizing underground cavern surrounding rock parameter collaborative perception and stability evaluation.

A stability assessment method of roadway surrounding rock includes: obtaining actual stratum rock parameters; establishing a two-dimensional geological model through numerical simulation based on a drill core columnar diagram and the actual stratum rock parameters; changing influencing factors, and recording amounts and acceleration values of deformation of roadway sidewalls, and whether failure occurs to obtain dynamic response characteristics of roadway surrounding rock, combining the changed influencing factors and the dynamic response characteristics as labels to obtain a dataset, and obtaining multiple datasets including the dataset; dividing the multiple datasets into a training set and a validation set, inputting the training set into a PSO-BP neural network and a GA-SVM deep learning model to obtain a preliminary stability assessment model, and adjusting and validating the preliminary stability assessment model by the validation set to obtain an optimized stability assessment model; and using the optimized stability assessment model to assess roadway stability.

The invention relates to the technical field of electrical equipment monitoring, in particular to a power distribution equipment state intelligent diagnosis system and method, and the system comprises a current fluctuation detection module, a voltage stability evaluation module, a heat effect diffusion analysis module, a fault point positioning module and an equipment state evaluation module. According to the invention, through extracting current change characteristics of key nodes and judging continuity of fluctuation differences, rapid identification of current anomalies can be realized, a voltage distortion region and a distribution range thereof can be positioned in combination with phase deviation and symmetry change of voltage waveforms, and the current anomalies can be identified based on a temperature gradient path and jump point screening. A hot spot diffusion trend and a thermal disturbance boundary can be described, morphological analysis and coordinate labeling are performed on the basis of multiple fault areas, multi-point concurrent positioning is realized, association between thermal disturbance and connection defects is identified in combination with temperature difference and current path change characteristics, fault diffusion trends and state disturbance blocks can be accurately presented, and the accuracy of fault diagnosis is improved. And the power distribution state identification and intervention efficiency is improved.

The invention discloses a stability analysis method of a network tracking / constructing hybrid station based on an impedance model, and relates to a modeling and stability evaluation technology of a wind power plant grid-connected system. The method comprises the following steps: firstly, respectively establishing sequence impedance models of a following network type direct-driven fan and a construction network type direct-driven fan based on a harmonic linearization method, and verifying the accuracy of the established models in different frequency bands by combining an impedance sweep frequency method; secondly, on the basis of considering the influence of line impedance, deducing to form an impedance network structure of a hybrid wind power plant grid-connected system, and constructing a stability judgment condition suitable for system analysis; and finally, based on the established impedance stability criterion, identifying possible oscillation risks of the system in subsynchronous and supersynchronous frequency bands. The method is suitable for wind power system stability analysis in various hybrid access scenes, and provides theoretical basis and technical support for subsequent system parameter setting, control strategy optimization and oscillation suppression measures.

The invention relates to the technical field of software time synchronization, and discloses a software time synchronization method and system for multi-sensor data fusion, and the method comprises the steps: extracting temperature, load and drift frequency characteristics through principal component analysis based on the working state and historical drift data of a sensor, and constructing a confidence evaluation model to calculate the credibility of a timestamp; identifying an abnormal node group by using k-means and an isolated forest algorithm, analyzing a phase deviation fluctuation and network delay interaction effect, extracting a nonlinear drift feature in combination with a Prophet algorithm, and calculating a phase correlation value by using Hilbert cross-correlation; and dynamically adjusting node clock parameters and generating a calibration timestamp according to the network influence weight and the stability evaluation result. According to the method, the problem of time desynchrony caused by clock drift and network delay factors in a distributed system is effectively solved, and the overall time consistency and reliability of the system are improved.

The invention belongs to the technical field of mine safety monitoring and geotechnical engineering stability evaluation, and relates to a mine filling body performance detection and stability evaluation method and system.The method comprises the steps that S1, a first type of monitoring signals are collected in real time through a distributed optical fiber sensor array, and meanwhile vibration waveform signals serve as second type of monitoring signals; s2, inputting the first type of monitoring signals and the second type of monitoring signals into a multi-source data fusion model, and generating fusion state signals representing the three-dimensional damage degree and the real-time deformation state of the filling body; s3, inputting the fusion state signal into a parameter dynamic identification module to generate an updated numerical model parameter signal; and S4, based on the updated numerical model parameter signal, executing stability quantitative calculation to generate a risk level signal. According to the mine filling body performance detection and stability evaluation method and system, the problem of inaccurate psychological state recognition such as large individual difference, strong environmental interference and weak dynamic adaptability can be solved.

The invention relates to the technical field of information processing, in particular to a hard mountain side slope digital twinborn monitoring method and system based on a cloud platform. The method comprises the following steps: obtaining real-time monitoring data of a slope in a hard mountain area, and carrying out data preprocessing to obtain standardized slope dynamic monitoring data; performing data compression and feature extraction on the standardized slope dynamic monitoring data, and transmitting a feature extraction result to a cloud platform to obtain cloud slope key information data; the method comprises the following steps: constructing a digital twinborn model by utilizing cloud side slope key information data based on a cloud platform to obtain a preliminary side slope digital twinborn model; through construction and optimization of the digital twinborn model, stability evaluation and risk early warning, the precision and real-time performance of slope monitoring in the hard mountainous area are remarkably improved, and a scientific basis and an executable long-term scheme are provided for slope treatment.

The invention relates to the technical field of aircraft manufacturing, and discloses an aircraft part production quality optimization method based on data analysis. The method comprises the following steps: collecting multi-process real-time processing parameters and quality inspection data of a production line, and generating a dynamic quality weight matrix according to a process parameter coupling degree; extracting process path difference characteristics of qualified products and unqualified products in historical batches, and encoding the process path difference characteristics as a quality evolution chain matched with adjacent process parameter mutation relevance; optimizing process parameters at a quality evaluation node, driving a parameter combination to iterate to minimize quality fluctuation, generating an adjustment amount, updating an evolution chain constraint coefficient, and synchronously constructing a stability evaluation function of a correlation weight matrix and a defect propagation path; triggering process compensation according to an adjustment amount gradient, and verifying the cohesion through a tolerance rule by taking a reference parameter matched with the target evolution chain as compensation data; and generating a feedback matrix by using the quality fluctuation index and the compensation result, and correcting the mapping relation between the weight matrix and the evolution chain.

The invention discloses a tunnel-landslide mass stability evaluation method based on space-time multi-scale coupling, and belongs to the field of tunnel-landslide masses, and the method comprises the following steps: S1, extracting a feature parameter set and an abnormal event mark set; s2, constructing a three-dimensional geological-mechanical model; s3, space-time multi-scale modeling of multi-physics field coupling is carried out; s4, outputting a stability prediction level by using the constructed ISSA-LSTM-GARCH combined prediction model; s5, generating an early warning in combination with an association rule engine; and S6, knowledge updating and systematic evolution of closed-loop optimization are carried out. According to the tunnel-landslide mass stability evaluation method based on space-time multi-scale coupling, multi-source monitoring data and a multi-physical field coupling model are fused, landslide mass stability under the influence of tunnel engineering is accurately and comprehensively analyzed through high-precision positioning and parameter inversion optimization, and powerful support is provided for engineering decision making.

The invention provides an intelligent safety and stability assessment method, system and device for an underground cavern and a medium, and the method comprises the following steps: S1, building a three-dimensional geological model of the underground cavern according to initial geological exploration data; s2, multiple monitoring instruments are pre-buried in key parts of the underground cavern, and monitoring data are collected in real time; s3, constructing an underground cavern safety and stability evaluation model based on a deep learning algorithm; s4, inputting real-time monitoring data into an evaluation model, combining historical monitoring data and geological model information, analyzing and comparing with a three-dimensional numerical calculation result, adjusting geological parameters, performing reconstruction evaluation, and outputting a safety and stability level; and S5, according to an evaluation result, automatically generating early warning information and processing suggestions. According to the method, the problems of poor real-time performance and low data utilization rate of traditional evaluation under complex geological conditions are solved, and the intelligent level of construction safety monitoring of underground cavern groups such as hydropower stations and pumped storage projects can be effectively improved.

The invention belongs to the technical field of plastic part quality detection, and discloses a plastic part gold stamping mark accurate positioning method and system based on visual identification, and the method comprises the steps: collecting environment illumination parameters, equipment operation parameters and image data, and processing the image data to obtain regional image data; performing edge detection and fracture repair operation on the regional image data to obtain identification edge feature data; calculating to obtain a position deviation quantized value according to the identification edge feature data; based on the position deviation quantized value, in combination with preset inclination direction information, adjusting an image visual angle and correcting an angle deviation to obtain corrected image data; executing edge path tracking on the corrected image data to obtain path complete data; obtaining an identifier positioning result based on the path complete data in combination with the environment illumination parameters and the equipment operation parameters; and carrying out stability evaluation on the identification positioning result, and obtaining a final system identification stability result in combination with preset detection fluctuation characteristics.

The invention discloses a power network parameter-based power flow system ill-conditioned evaluation method and system, and mainly relates to the technical field of data calculation of a power system. Comprising the following steps: generating a multi-dimensional evaluation index set comprising a network topology structure index, an admittance matrix characteristic index and branch impedance parameter statistics according to basic network parameters of a power system; through comprehensive analysis of the generated multi-dimensional evaluation index set, traditional Jacobian matrix singular value calculation is replaced, and the ill-conditioned condition number of power flow calculation of the power system is evaluated; according to the power grid type and the operation scene, dynamically adjusting the judgment threshold value of each index, and outputting the ill-conditioned risk level and the optimization suggestion. The method has the advantages that calculation independent of Jacobian matrix singular values is achieved, dynamic quantitative evaluation is carried out on bad operation conditions of the system, and meanwhile the load flow calculation stability evaluation precision of a large-scale power grid is remarkably improved.

The invention relates to the technical field of rainfall slope evaluation, in particular to a rainfall slope stability evaluation method based on a physical certainty coupling model. The method comprises the following steps: acquiring digital elevation model data, engineering geological survey data and historical rainfall data of a target area; extracting topographic features from the digital elevation model data to obtain basic topographic parameters; the method comprises the following steps: carrying out longitudinal layering division on a rock-soil body based on engineering geological survey data, and carrying out physical and mechanical parameter setting on each layered rock-soil body to obtain a rock-soil body layering parameter set; and performing frequency analysis on the historical rainfall data to obtain an extreme rainfall intensity sequence. According to the method, by fusing the multi-source data and the three-dimensional physical coupling model, dynamic and accurate rainfall induced slope stability evaluation and risk early warning are realized, and the prediction precision is effectively improved.

The invention discloses a book copyright protection and information traceability method based on a block chain technology, and particularly relates to the field of book copyright protection, comprising multi-source data acquisition, core index calculation, network stability evaluation, copyright credibility correction, infringement risk detection and comprehensive traceability rating. According to the method, block chain activeness, node distribution and copyright transaction data are fused, network stability and credibility correction coefficients are dynamically calculated, and traditional single-dimension limitation is broken through; a weight adjustment strategy is triggered by monitoring network fluctuation in real time, so that the fault-tolerant capability of the system is enhanced, and misjudgment caused by node abnormity is avoided; a hierarchical early warning system based on multi-dimensional quantitative indexes is constructed, and automatic three-level credibility rating is generated in combination with content similarity dynamic calibration and contract execution frequency analysis, so that the timeliness and judgment precision of copyright traceability are remarkably improved, full-process dynamic risk early warning is realized, and manual intervention dependence is reduced.

The invention discloses a deep underground engineering large deformation real-time monitoring and control method based on deep learning. The method comprises the steps of deployment of a monitoring system, preprocessing of monitoring data, construction of a neural network model, training of the neural network model, output of a prediction result, generation of a control instruction and quantitative evaluation of a control effect. According to synchronously acquired multi-source information such as surrounding rock strain, temperature, seepage pressure and micro-seismic disturbance energy, a deep learning model based on physical mechanism constraint is introduced, so that the reliability and generalization ability of a prediction result are remarkably improved; intelligent identification of surrounding rock stability assessment and failure modes is realized, a control instruction is automatically generated, and an implementation effect is quantitatively assessed.

The invention belongs to the technical field of ocean engineering and hydrogen energy equipment. According to the invention, multidisciplinary frontier technologies are fused. A high-precision distributed sensor network and a professional monitoring module are used for collecting data, and the data are ensured to be real and reliable through a block chain technology; by means of a multi-physics field coupling model and an advanced solver, a complex physical process in platform operation is accurately captured. And dynamic response prediction and multi-objective optimization are realized by combining bionic control and a deep reinforcement learning algorithm. And a comprehensive evaluation system is constructed, and an early warning decision closed loop is formed based on digital twinning. The method breaks through the limitation of traditional evaluation, effectively improves the operation safety and hydrogen production efficiency of the floating hydrogen production platform under variable sea conditions, and provides key technical support for the development of the offshore hydrogen energy industry.

The invention provides a coal seam surrounding rock stability monitoring method based on micro-seismic monitoring, which relates to the technical field of coal seam surrounding rock stability monitoring and comprises four steps of micro-seismic sensor network arrangement, real-time data acquisition and processing, surrounding rock stability evaluation model construction and real-time early warning and feedback of coal seam surrounding rock stability. According to the invention, a roof-side wall-support body cooperative monitoring architecture is adopted to cooperate with the micro-seismic sensor, and three-dimensional sensing network deployment is established, so that the micro-fracture activity in the surrounding rock of the whole stope can be continuously monitored in real time in a three-dimensional space full-coverage manner, the limitation of traditional point-type monitoring is overcome, and the dynamic evolution law of the micro-seismic activity is analyzed, so that the micro-fracture activity in the surrounding rock of the whole stope can be monitored in a real-time manner. According to the method, before macroscopic physical damage occurs, an area with abnormal stress concentration and accelerated damage accumulation can be identified, advanced early warning is realized, and precious time is gained for active prevention and control.

The invention relates to the technical field of foundation pit supporting, in particular to a point-shaped foundation pit supporting structure stability evaluation method and system, and the method comprises the steps: obtaining the engineering parameters, geological data and dynamic monitoring data of a target foundation pit; performing feature extraction on the dynamic monitoring data to obtain a stability evaluation index vector; calculating spatial effect characteristic parameters of the target foundation pit based on the engineering parameters; the influence of geological data on the spatial effect is considered, the spatial effect characteristic parameters are corrected, mapping is carried out in a preset spatial effect weight database, and an evaluation index weight vector of the target foundation pit is obtained; performing weighted calculation on the stability evaluation index vector and the evaluation index weight vector to obtain a stability evaluation index of the target foundation pit, and comparing the stability evaluation index with a preset stability threshold to obtain a stability evaluation result; according to the method, the change of the space effect of the foundation pit under different geological conditions can be reflected more accurately, so that the final stability evaluation index can reflect the actual stability of the foundation pit more accurately.

The invention provides a GNSS-based mountain area highway loess slope stability evaluation method and system, and relates to the technical field of loess slope stability evaluation.The method comprises the steps that firstly, a slope area is divided into grids, geologic feature data of each grid are obtained, super nodes are divided according to the geologic feature data, and the super nodes are divided into super nodes; monitoring points are determined, and the soil moisture content is monitored, so that the moisture content of the soil at different depths is analyzed; secondly, the original displacement is monitored through a GNSS, displacement correction is carried out based on the Fredld unsaturated soil shear strength theory, and the actual displacement is obtained; the influence of the accumulated rainfall on the soil body strength parameters is analyzed, and the soil body strength parameters are corrected in real time; coupling the corrected soil body strength parameter with the actual displacement, calculating a safety coefficient, and judging an early warning state according to the rainfall sensitivity coefficient; and finally, comprehensively judging the stability of the slope according to the early warning state. According to the method, through multi-parameter comprehensive analysis, the accuracy of slope stability evaluation is improved.

The invention relates to a geotechnical engineering geological investigation system and an investigation method thereof, which are used for solving the problems of drilling redundancy, repeated model correction and long period caused by traditional uniform hole distribution. The system comprises a geophysical exploration module, a rock-soil body structure feature extraction module, a geological abnormal area identification module, a survey detection point dynamic optimization module, a drilling sampling analysis module and a geotechnical engineering stability evaluation module. Earth material exploration is carried out on representative points to obtain a comprehensive geophysical prospecting data set, inversion is carried out according to the comprehensive geophysical prospecting data set to obtain a three-dimensional rock-soil structure, an abnormal area is recognized, accordingly, the drilling layout is dynamically optimized, and stability evaluation is completed at a time after a small amount of directional drilling sampling is carried out. By adopting the system, a high-precision geologic model and a stable evaluation result can be obtained with fewer drilled holes in a preliminary exploration stage, the cost is remarkably reduced, and the period is shortened.

The invention discloses a reclosing locking method and system based on winding hot-spot temperature fitting, and relates to the technical field of power transformer dynamic stability assessment, and the method comprises the steps: determining a target transformer, collecting parameters, building a winding current calculation model, calculating steady-state and short-circuit current waveforms, and carrying out the winding temperature rise test of the transformer, the method comprises the following steps: measuring a steady-state temperature rise value, constructing and correcting an electromagnetic heat flow coupling model, generating steady-state and transient-state temperature field data of a winding, constructing a target function of a winding hot-spot temperature, fitting simulation data by utilizing an optimization algorithm, obtaining a fitting function, calculating a winding stress value under a short-circuit working condition in real time, and comparing the winding stress value with a dynamically adjusted stress limit value, so as to obtain a winding stress value. Alarm and locking reclosing are triggered according to the result, and intelligent protection of the transformer is achieved. According to the invention, through accurate parameter acquisition, model establishment, temperature field simulation, hot-spot temperature fitting and stress calculation, real-time monitoring and intelligent protection of the transformer winding under the short-circuit working condition are realized.

The invention discloses a grain defective product detection method combining fingerprint spectrum and finite element analysis, and particularly relates to the technical field of grain quality detection.The grain defective product detection method comprises the following steps that grain chemical and physical characteristics are extracted through a fingerprint spectrum and a finite element model, and key characteristic vectors are obtained through dimension reduction screening; on the basis of defective grain grade labels, mutual information is calculated, cross-modal analysis is carried out, and a strong dependency feature group is identified; feature sparsity and stability evaluation are combined, features are optimized by adopting a self-adaptive screening method, a food quality knowledge graph is finally constructed, and intelligent detection and quality evaluation of defective food are realized; according to the method, the accuracy of a detection model, namely a grain quality knowledge graph, is improved through feature sparsity and feature stability evaluation, an automatic grain defective product detection and recognition mechanism is constructed by utilizing a multi-modal information fusion method of fingerprint spectrum and finite element analysis and combining a mutual information analysis and dynamic feature screening algorithm, and the detection accuracy is improved. And the dependence on manual intervention and experience judgment is reduced.

The invention relates to the technical field of tower crane safety monitoring, and discloses a tower crane construction safety guarantee method and system.The method comprises the steps that a multi-tower crane cross operation area is dynamically recognized based on tower crane parameter data and real-time operation state data, and a dynamic distribution diagram of the cross operation area is obtained; based on the tower crane load data and the environment parameter data, tower crane operation stability evaluation is conducted, and an operation stability evaluation value is obtained; performing dynamic collision risk analysis based on the dynamic distribution map and the operation stability evaluation value to obtain a dynamic collision risk index; setting an adaptive safety distance threshold based on the dynamic collision risk index; and grading early warning and intervention control are carried out based on a self-adaptive safety distance threshold. Through collection and fusion analysis of multi-dimensional data, comprehensive perception and dynamic monitoring of a multi-tower crane operation environment are realized, and dispersed tower crane safety monitoring can be integrated into a cooperative system, so that the safety management level of a construction site is remarkably enhanced.

The invention discloses a small sample modeling stability evaluation method and system based on Bootstrap resampling, and particularly relates to the technical field of computers and data intellectualization, the method comprises the following steps: completing data preprocessing and stable stage identification under a unified time base, and forming a segment set; an average block length is used as a decision quantity, an SBB is optimized to construct a sample space, and an optimal block length is determined in combination with variance consistency and nominal coverage rate consistency criteria; carrying out re-sampling training around short / medium / long scales, and collecting layered indexes such as prediction, parameters and features; stability calculation and empirical coverage rate calibration are completed based on intra-scale statistics and inter-scale weighted mixing, and a pseudo-stationary diagnosis score is output; and finally, engineering judgment and backspacing optimization are carried out according to the coverage rate, the correlation maintenance and the risk threshold. The system records random seeds, block metadata and model version generation evidence pointers, has the characteristics of traceability and reverifiability, and is suitable for scenes of production lines, network traffic, finance and the like.

In one implementation, a device divides a knowledge graph that represents a potential attack on a cloud computing environment in a plurality of graph slices. The device uses an artificial intelligence model to make predictions as to whether each of the plurality of graph slices are indicative of the potential attack. The device makes a stability assessment as to whether the predictions are stable in part by assessing a gradient between predictions for neighboring graph slices in the plurality of graph slices. The device provides, based on the predictions and the stability assessment, an indication that the potential attack is an actual attack on the cloud computing environment.

The invention discloses a highway tunnel supporting structure Newtonian mechanics deformation intelligent early warning platform, and relates to the technical field of supporting early warning, the highway tunnel supporting structure Newtonian mechanics deformation intelligent early warning platform comprises a deformation prediction module, the deformation prediction module is used for carrying out early warning and stability evaluation on a supporting structure; the deformation prediction module comprises a coupling modeling unit, a load response unit and an evaluation early warning unit. By designing the deformation prediction module, bidirectional coupling of rock mass pore water pressure and solid deformation is realized, the problem of misjudgment caused by neglecting underground water seepage softening surrounding rock and dynamic load cumulative effect in traditional early warning is solved, and prediction precision and active prevention and control capability of tunnel stability are improved.

The invention discloses a building structure stability intelligent evaluation method based on AI modeling, and the method comprises the following steps: S1, collecting building structure data, and constructing a structure diagram topology model; s2, performing spectral analysis, generating a frequency domain priori vector, and constructing a neural network input vector; s3, inputting the input vector into the improved physical information neural network; s4, constructing a neural network training objective function containing multi-source error information; s5, training the neural network by adopting a structural residual adaptive gating optimization algorithm to obtain a final model; s6, inputting the input vector into the final model to obtain a new state variable predicted value and a modal disturbance scoring result; and S7, generating a stability evaluation diagram and a risk scoring result based on an output result, and outputting a structure stability evaluation conclusion and early warning information. According to the method, high-precision modeling and multi-dimensional risk intelligent assessment of the dynamic state of the building structure are realized, and the method is suitable for health monitoring and early warning scenes of large civil structures and complex infrastructures.

The invention relates to the technical field of semiconductors, and provides a chip testing method and device based on multi-physics field simulation, and the method comprises the steps: collecting the multi-physics field response data of a chip, carrying out the feature splitting and classification processing, obtaining a thermal response feature vector, an electromagnetic response feature matrix and a stress distribution sequence, and generating a thermal response map; and carrying out correlation calculation on the electromagnetic response characteristic matrix based on the thermal response map to obtain a discriminative risk grade value and an error weighting factor, then carrying out risk trend fitting analysis on the stress distribution sequence, and generating a chip performance stability evaluation index and a test adaptability score under multi-physics field interference. The chip performance stability evaluation index and the test adaptability score under the multi-physics field interference are generated by using the collected multi-physics field response data, so that comprehensive evaluation of the performance of the chip under the multi-physics field effect is realized, and the problem that the accuracy of the test result is insufficient when the chip is in a complex environment in which multiple physics fields act at the same time is solved.

The invention relates to the technical field of rolling forming machine control, and discloses a self-adaptive tension control system of a dry-method electrode continuous rolling forming machine. The system is composed of a tension generation module, a state evaluation module, a dynamic compensation module, a strategy optimization module, a production management database and a rolling execution mechanism. The tension generation module collects original tension data in real time at a transmission node; the state evaluation module analyzes the operation state and generates a tension stability evaluation report; the dynamic compensation module calculates the material deformation compensation amount according to the report and generates a dynamic regulation and control instruction; the strategy optimization module optimizes a strategy and outputs a parameter packet in combination with the instruction and a historical record; the production management database stores material characteristics, equipment history and regulation and control cases; and the rolling execution mechanism is adjusted to operate according to the parameter packet. All the modules cooperate to achieve real-time monitoring, evaluation, compensation and optimization of tension, the traditional control problem is solved, and the product quality and efficiency are improved.

The invention relates to the technical field of road engineering, in particular to a new highway subgrade stability evaluation method and system based on data analysis, and the method comprises the steps: obtaining underground goaf data of a target engineering region, new highway subgrade design data and highway network data of an existing highway; carrying out underground goaf modeling, new highway subgrade modeling and highway network modeling by utilizing a three-dimensional modeling technology, and constructing a space coupling three-dimensional model; evaluating the stability of the underground goaf under the coupling influence of the existing highway; evaluating the stability of the newly-built highway subgrade according to an underground goaf stability evaluation result in combination with the space coupling three-dimensional model; newly-built highway subgrade stability early warning is conducted according to the newly-built highway subgrade stability evaluation result. According to the method, the stability influence of the existing highway on the underground goaf and the space-time coupling effect of the underground goaf on the newly-built highway subgrade are quantified, so that the accuracy and reliability of stability evaluation of the newly-built highway subgrade are effectively improved.