20547results about "CAD numerical modelling" patented technology

A portable agent package apparatus for coupling to one or more environment, energy or water infrastructure or water body sensors produce timestamped or temporal process data, includes a physics surrogate world model trained to predict at least one hydraulic, chemical, or biological state variable of the sensed water system, a connection memory that stores metadata describing data source identifiers, units, and sampling cadence, pointers to available analytical tools or peer agent packages, or streams of operational experience or a hierarchical options library, an emotion tensor continuously encodes normalized metrics comprising at least one of model accuracy, computational load, data quality, latency, and uncertainty, or further including an exploration bonus channel, a value estimate error, an anomaly score, or an alignment divergence flag, and a bidirectional, authenticated communication interface that receives the temporal or timestamped process data from the one or more sensors, transmits Memo updates, and accepts goal directives.

The invention discloses an electromechanical system fault pre-diagnosis method and system based on digital twinning. The method comprises the following steps of obtaining multi-source data in an electromechanical system operation process; preprocessing the acquired multi-source data, wherein the preprocessing comprises data cleaning, normalization processing and feature extraction; and on the basis of the preprocessed multi-source data, an electromechanical system design drawing, a three-dimensional geometric model, material attributes and a kinetic equation are fused, and a digital twin model is constructed. According to the invention, through a digital twin model dynamic calibration and prediction algorithm, early abnormity of the equipment is identified in advance, the fault probability and the residual life are output, and non-planned shutdown is reduced; by constructing a cross-physical domain fault feature system and fusing model simulation and actual measurement data, the potential fault identification accuracy is improved, and the missed diagnosis rate is reduced; by calibrating parameters of the digital twin model in real time, the method adapts to nonlinear changes of equipment, ensures high-fidelity mapping of the model, and improves fault prediction precision.

This invention provides a comprehensive maritime shipbroking platform uniting digital twin modeling, AI-driven cargo allocation, blockchain-based contract management, predictive maintenance (AR / VR), route optimization, real-time tracking, and single-window compliance. The digital twin engine continuously simulates vessel performance, enabling data-driven decisions on stowage, scheduling, and maintenance. A cargo-freight matching module optimally allocates shipments, factoring in market rates, vessel metrics, and port congestion. Blockchain-secured smart contracts automate negotiations, ensuring transparency and tamper-proof enforcement. A predictive maintenance system applies advanced analytics to diagnose technical issues early, while the route optimization engine finds cost-effective, emission-compliant routes. Real-time tracking gives stakeholders constant visibility, and the single-window interface integrates Electronic Bill of Lading processes, satisfying IMO and IG P&I standards. Additionally, a dynamic vessel ranking system incorporates SIRE, RightShip, PSC, and user feedback for safer, more efficient chartering decisions. The invention addresses day-to-day operational challenges in maritime logistics, elevating efficiency and compliance.

The invention relates to the technical field of information data processing, in particular to a mountain area engineering slope real-time monitoring and early warning method and system based on digital twinning. The method comprises the following steps: acquiring original slope real-time monitoring data, and carrying out standardization processing to obtain standardized slope real-time monitoring data; the method comprises the following steps: constructing a digital twinborn model by using standardized slope real-time monitoring data to obtain a preliminary slope digital twinborn model; and dynamically updating the preliminary side slope digital twinborn model by using the standardized side slope real-time monitoring data and the side slope historical multi-source monitoring data to obtain a real-time updated side slope digital twinborn model. According to the method, the digital twinborn model is updated in real time, multi-physics coupling analysis and nonlinear stability prediction are combined, it is ensured that dynamic monitoring, stability evaluation, risk prediction and emergency response of the slope can reflect the actual condition in time, and therefore the scientificity and timeliness of disaster prevention and control are improved.

The invention discloses a load flow calculation and simulation control method and system for a digital twin power grid, and relates to the technical field of digital twin simulation control, and the method comprises the following steps: constructing a digital twin power grid model, and carrying out the dynamic topology optimization processing of the digital twin power grid model based on remote signaling credibility weighting; according to the optimized digital twin power grid model, identifying the power grid operation risk based on an integrated learning model; according to the risk identification result, generating a transfer path control strategy based on an analytic hierarchy process and a fuzzy comprehensive evaluation method; mapping the transfer path control strategy into a control action instruction set, and simulating execution and establishing a feedback correction mechanism on the digital twin power grid model; by generating the optimal path control strategy and performing control strategy analog simulation and self-adaptive feedback correction based on the digital twin power grid model, the problem of lack of intelligent path control strategy selection and simulation verification based on state dynamic identification in the prior art is solved.

The invention relates to the technical field of fan blade fatigue damage prediction. The invention provides a fan blade fatigue damage prediction method and system. The method comprises the following steps: constructing a coupling finite element model based on blade anisotropy parameters; blade surface three-dimensional strain field data, blade vibration acceleration signals, environment temperature and humidity and wind speed and direction data are obtained in real time, and a multi-dimensional monitoring data set is constructed; based on the multi-dimensional monitoring data set, nonlinear coupling features of all the load components are extracted, a multi-dimensional feature tensor is obtained, and a reference stress field matched with the current working condition is generated; inputting the multi-dimensional feature tensor and the reference stress field into a bidirectional long-short-term memory network, and establishing a data-physics combined driven damage evolution model; and positioning a damage area based on a damage probability distribution diagram output by the damage evolution model. The problems that in the prior art, prediction errors are obvious, sensitivity to early damage is insufficient, the false alarm rate is high, and accurate positioning of the damage position and quantitative prediction of the residual life are difficult to achieve are solved.

The invention discloses a distributed real-time monitoring and early warning system for a temperature field of a smelting furnace, and relates to the technical field of industrial process intelligent monitoring. The problems of accumulated measurement errors and non-stationary hotspot escape reconstruction hysteresis caused by static emissivity setting in an existing system are solved. Collecting multiband radiation intensity and voltage signals through time domain alignment of the multispectral sensor array and the thermocouple array; iterating emissivity parameters in real time by adopting a dynamic ash body spectrum ratio algorithm in combination with flue gas absorption characteristics; fusing non-contact and contact temperature measurement data based on weighted Kalman filtering and complementary filtering; constructing a space-time variable covariance function to carry out non-stationary Kriging interpolation; dynamically optimizing the local grid resolution by combining an adaptive grid module; the processing flow is accelerated through the parallel computing module; early warning is triggered based on abnormal probability judgment and is fed back to emissivity correction and grid optimization; according to the invention, the monitoring precision and real-time performance of the temperature field are obviously improved, and the risks of false alarm, missing alarm and equipment melting loss are effectively inhibited.

The invention relates to the technical field of special equipment intelligent monitoring and maintenance, in particular to a special equipment monitoring and maintenance method and system based on multi-dimensional data fusion, and the method comprises the steps: collecting and processing multi-source heterogeneous sensor data; constructing a dynamic digital twinborn model based on sensor data and a cross-domain term mapping rule; associating the sensor data with the digital twinborn model to establish a lightweight analysis model, performing health state simulation analysis on the edge equipment, and generating an analysis result; the data quality of the edge device is monitored through the analysis model, when the data quality is lower than a threshold value, a co-simulation process is started, a simulation analysis task is transferred to the cloud digital twin platform, and the edge device continues to conduct simulation analysis based on the analysis model; and constructing a simulation design decision rule base, and generating a maintenance suggestion. Through the multi-dimensional data fusion and edge cloud co-simulation architecture, the accuracy, real-time performance and reliability of monitoring and maintenance of the special equipment are improved.

The invention relates to the technical field of tunnel engineering, in particular to a tunnel surrounding rock grading method and system, comprising intelligent sensing and data acquisition, multi-source data fusion and modeling, hybrid model dynamic grading, real-time decision and support optimization, online learning and dynamic feedback, and risk early warning and emergency response. Compared with the prior art that a geological data acquisition mode combining manual drilling coring and low-resolution geophysical prospecting is adopted, efficiency is low, subjective errors are large, and a complex geological structure is difficult to cover, unmanned aerial vehicle LiDAR scanning, intelligent rock core image analysis and a high-density IoT sensor network work cooperatively, and the working efficiency is greatly improved. Real-time dynamic acquisition of full-section geological information is achieved, manual intervention errors are eliminated in combination with a multi-source data fusion algorithm, the automation level and three-dimensional space representation precision of data acquisition are remarkably improved, and a high-resolution holographic data base is provided for surrounding rock classification.

The invention discloses a plug-in inductor winding process control method based on production parameter dynamic compensation, and provides a closed-loop compensation control strategy taking a physical field evolution model as a core aiming at the problems of unstable sensing quantity and non-uniform structure in the traditional process. The method comprises the following steps: firstly, constructing an ideal physical field reference model according to target inductance parameters and a structure, and describing an evolution law of characteristics such as a magnetic field, an electric field or impedance in an ideal state; in the actual winding process, a physical field signal is collected through a sensor, production parameters such as wire tension and main shaft rotating speed are obtained, and a physical field distribution snapshot of the current winding state is generated. And the system compares the extracted actual features with the reference model, identifies a dominant disturbance source causing deviation in combination with real-time production parameters, and outputs a disturbance type label carrying causal information. And according to a label and a production parameter change trend, a compensation instruction is dynamically generated and executed, fine adjustment of tension, a winding displacement track or density is realized, and consistency and precision of an inductor finished product are ensured.

A portable agent package apparatus for coupling to one or more environment, energy or water infrastructure or water body sensors produce timestamped or temporal process data, includes a physics surrogate world model trained to predict at least one hydraulic, chemical, or biological state variable of the sensed water system, a connection memory that stores metadata describing data source identifiers, units, and sampling cadence, pointers to available analytical tools or peer agent packages, or streams of operational experience or a hierarchical options library, an emotion tensor continuously encodes normalized metrics comprising at least one of model accuracy, computational load, data quality, latency, and uncertainty, or further including an exploration bonus channel, a value estimate error, an anomaly score, or an alignment divergence flag, and a bidirectional, authenticated communication interface that receives the temporal or timestamped process data from the one or more sensors, transmits Memo updates, and accepts goal directives.

The invention relates to the technical field of crustal stress field data processing, in particular to a crustal stress field three-dimensional dynamic inversion method based on a multi-scale adaptive algorithm. The method comprises the following steps: acquiring a geological data set of a target area; constructing a crustal stress field three-dimensional initial model based on the geological data set, and performing geologic body space division and mesh generation to obtain crustal stress field three-dimensional mesh model data; performing multi-scale region division on the crustal stress field three-dimensional grid model data, and establishing a multi-scale weighting function to obtain multi-scale partition mapping information; and constructing a cross-scale boundary adaptive transmission mechanism, and establishing a stress tensor continuity constraint model at a multi-scale partition boundary to obtain cross-scale stress boundary coupling data. Through a multi-scale adaptive algorithm and dynamic closed-loop optimization, high-precision, dynamic and continuous inversion of a crustal stress field in a complex geologic structure is realized.

The invention discloses a slope rainfall infiltration stability dynamic evaluation and landslide prediction method. The method comprises the following steps: constructing a numerical model coupling rainfall infiltration and slope stress field response; integrating multi-source data, and updating model boundary conditions in real time; performing dynamic stability evaluation based on a local safety coefficient method; and training a landslide prediction model and realizing intelligent early warning identification. The seepage-stress-strength three-field coupling numerical model is provided, the physical response process of the unsaturated soil body is dynamically simulated, the precursor mechanism of rainfall-induced landslide can be truly restored, and prediction scientificity and accuracy are improved; remote sensing topographic data, soil property survey parameters and real-time meteorological monitoring information are integrated, model boundary conditions and initial states are dynamically updated through a system interface, real-time response to environmental changes is achieved, and simulation reliability is improved; a local safety coefficient evaluation mechanism is introduced, and a continuous weak region discrimination algorithm is combined, so that space identification and time sequence tracking of a potential slip region and a damage zone are realized, and the slope stability analysis refinement level is improved.

The invention specifically relates to the technical field of big data analysis, and discloses a water conservancy gate multi-parameter cooperative intelligent monitoring system, which comprises a multi-parameter acquisition module, a multi-parameter processing module, a comprehensive analysis module, an intelligent decision module, an operation and maintenance early warning module and a man-machine interaction module, the multi-parameter processing module is used for calculating flood control and discharge indexes, structure safety indexes and equipment health indexes; the comprehensive analysis module is used for judging gate risk levels; the intelligent decision-making module is used for generating an optimal gate scheduling scheme; the operation and maintenance early warning module is used for constructing a multi-level early warning mechanism; according to the method, parameter coverage is comprehensive, a gate digital twinborn model and a gate opening comprehensive evaluation model are constructed, the gate risk level is evaluated, an optimization strategy is dynamically adjusted through an intelligent decision module, the accuracy of gate risk judgment is improved, and the self-adaptive capacity of the system is improved.

The invention provides a multi-source heterogeneous data fusion side slope digital twin modeling method, which comprises the following steps of: acquiring side slope multi-dimensional monitoring data by arranging a GNSS (Global Navigation Satellite System) sensor, a multi-point displacement meter, a distributed optical fiber strain sensor, an accelerometer, an osmometer, a monocular camera and satellite remote sensing image equipment; the collected data is converted into a unified format through time alignment, space registration and standardization processing and serves as modeling input; the method comprises the following steps: constructing an initial digital twinborn model reflecting the real form and physical characteristics of a slope by utilizing a three-dimensional modeling and finite element simulation technology; in combination with real-time sensing data, model evolution is dynamically driven based on a space-time fusion algorithm, boundary conditions and material parameters are automatically corrected through actual measurement deviation feedback, and continuous twin iteration updating of the model is achieved; and finally, extracting a landslide risk index to realize real-time early warning of the side slope. According to the invention, multi-source sensing and digital twinborn fusion is realized, and the accuracy, real-time performance and intelligent level of slope monitoring are improved.

The invention discloses a water conservancy project construction monitoring data supervision system and method based on multi-source data fusion, and relates to the technical field of engineering construction. The multi-source data fused hydraulic engineering construction monitoring data supervision system and method comprises the following steps: S1, acquiring hydraulic engineering construction monitoring data, and preprocessing the hydraulic engineering construction monitoring data; s2, constructing a sliding window, extracting key structure and environment variables, comprehensively analyzing a structure disturbance degree, and dividing different disturbance state sections according to the structure disturbance degree; s3, an unstable state time period is extracted, a disturbance feature sequence is constructed, highly-suspected disturbance fragments are identified, response deviation between the structure and the environment is analyzed, highly-credible abnormal fragments are screened, and an abnormal data structure is generated; and S4, the coupling relation between the structural response and the environmental disturbance is analyzed in a combined mode, and a dynamic unbalance index used for measuring the unsteady state intensity is constructed. The problem that the abnormal recognition false alarm rate is high due to the fact that monitoring data are disturbed frequently in a complex construction scene is solved.

The invention discloses a modeling analysis method for optimizing an urban water supply pipe network, and belongs to the technical field of intelligent water affairs, and the method comprises the steps: building a comprehensive pipe network database, collecting hydraulic parameters through Internet of Things equipment, and fusing the hydraulic parameters with GIS topological data; constructing a dynamic hydraulic simulation model, coupling real-time data of a pressure sensor with an EPANET model, and correcting a friction resistance coefficient of a pipe section; a hybrid optimization algorithm architecture is designed, a double-target genetic algorithm is adopted for old pipe network transformation, and a constrained particle swarm algorithm is adopted for new pipe network planning; executing multi-stage optimization calculation, generating a pipe diameter feasible solution space in combination with geographical constraints, and iteratively outputting a scheme meeting a pressure threshold value; and generating a pipe network optimal configuration scheme, and outputting an engineering implementation list through spatial overlay analysis and valve regulation logic. According to the method, the problem of modeling distortion caused by a multi-source data island is solved, the defect that a static model is difficult to adapt to dynamic water demands is overcome, and the economical efficiency and the engineering implementability of a pipe network optimization scheme are remarkably improved.

The invention relates to the technical field of civil engineering informatization and automation design, in particular to a parameterized construction drawing generation method based on a constraint solution algorithm, and the method comprises the steps: defining a construction drawing foundation structure and variable parameters through an engineering design model embedded with a parameterized template; a parameter constraint relation set is generated in combination with geometric constraints, size constraints and logic constraints input by a user, constraint rules are converted into intermediate expressions through a formula analyzer, a syntax tree is constructed, and an executable parameter association rule set is generated through semantic verification. The auditing module verifies the drawing compliance based on the engineering specification database and generates a correction suggestion; and realizing cross-system data linkage between the multi-terminal collaborative interface and external BIM model parameter mapping. According to the method, the problems of detail loss and specification deviation in three-dimensional model projection are effectively avoided, and the drawing generation efficiency and the multi-specialty collaborative robustness are improved.

The present invention relates to the technical field of proton exchange membrane fuel cells. Disclosed are a fuel cell parameter identification method and apparatus, and a device and a storage medium. The method comprises the following steps: constructing a semi-empirical model for a proton exchange membrane fuel cell; calculating a theoretical value of an output voltage of the semi-empirical model, and constructing an objective function by means of a mean square error between the theoretical value and an actual value of the output voltage of the semi-empirical model; on the basis of the semi-empirical model, determining a plurality of parameters to be identified, and using said plurality of parameters as decision variables to construct a plurality of constraint conditions for the objective function; on the basis of the plurality of constraint conditions, constructing an optimization model for the proton exchange membrane fuel cell by using the minimization of the objective function as an optimization objective and using said plurality of parameters as variables to be solved; and solving the optimization model by means of a multi-policy sparrow search optimization algorithm, so as to obtain a plurality of optimal parameters to be identified. In the multi-policy sparrow search optimization algorithm in the present invention, Tent chaotic mapping is introduced to initialize a population, the number of populations is increased, and then the two populations are merged. An adaptive feedback mechanism is added in a follower position update stage and a vigilant position update stage, thereby reducing the convergence accuracy under a limited number of iterations. A DE / best / 1 mutation policy and a dynamic scaling factor sf are used to update the position of a sparrow. The global optimization capability and the local optimization capability of an algorithm are improved, thereby also improving the accuracy of fuel cell parameter identification.

The invention relates to the technical field of smart ports and low-carbon environmental protection, and discloses a green low-carbon port monitoring and evaluation system, which comprises an Internet of Things data acquisition module for acquiring multi-dimensional environmental data, carbon emission data and energy consumption data in real time through multi-type sensor equipment deployed at a port; the data processing and analysis module is used for cleaning and preprocessing the collected data, and mining the relationship between energy use and carbon emission by using a big data analysis technology and an artificial intelligence algorithm; the carbon footprint calculation module is used for calculating the carbon emission of the port activity through an accurate model according to the processed data; by deploying multiple types of sensors, multi-dimensional data in port operation can be acquired in real time, a comprehensive carbon emission evaluation system is formed, the limitation that a traditional method can only monitor a single parameter is overcome through the multi-dimensional data comprehensive acquisition mode, and the problem of information fragmentation is effectively solved; and a solid foundation is provided for subsequent data analysis and decision making.

The invention discloses an urban inland inundation water recession process numerical simulation method and system based on physical-data fusion, and belongs to the technical field of urban flood control and intelligent water affairs. A hydraulic model and a data driving model are fused, a prediction result is dynamically weighted through gating attention, and real-time correction is performed by using Kalman filtering; the method comprises the steps of multi-source data fusion and enhancement, wherein data collection and processing are carried out, space-time alignment is achieved, and feature construction is carried out; mechanism and mathematical double-engine collaborative prediction: collaborative prediction is carried out based on a physical model engine and a data driving engine, and adaptive fusion is realized; online reasoning and dynamic correction, including Kalman filtering dynamic correction and extreme scene emergency processing; and performing model evaluation and iteration. According to the method, the problem of high-precision prediction of the recession time and the ponding range in a complex urban environment is solved, the generalization ability of the model in non-seen scenes such as pipeline blockage and rainstorm extrapolation is improved, and a reliable decision basis is provided for urban inland inundation emergency scheduling and drainage facility optimization.

The invention relates to a nonlinear system reduced-order model generation method fusing physical constraint and digital twinning, and belongs to the technical field of digital twinning. The method comprises the steps of performing sparse sampling on high-dimensional dynamic response data, extracting a dominant mode of high-fidelity model data, and retaining a mode of a physical abrupt change region through physical field gradient constraint in the extraction process; the high-fidelity finite element model and the low-fidelity simplified model are combined, and a mixed order reduction framework based on a multi-fidelity agent model is constructed; fusing a data driving error and a physical mechanism residual error in data-physical joint driving model training and optimization based on a mixed order reduction framework, and updating a primary function of a dominant mode based on online stream data; and performing cross validation on the output of the high-fidelity finite element model and the reduced-order model, and improving the precision of the reduced-order model through an iterative optimization strategy. The intelligent order reduction problem of the model is realized, and the accuracy and rapidity of the order reduction model are ensured.

The invention relates to the technical field of press monitoring, in particular to a digital twinning-based three-dimensional monitoring system for a precision servo press, which comprises a physical layer sensing module for acquiring real-time operating parameters, environment variables and workpiece processing data of the press; the dynamic twin construction module constructs a total-factor digital twin, and simulates a force-heat-deformation coupling effect by using finite element analysis and a multi-body dynamics algorithm based on physical attributes and process parameters; the intelligent analysis center identifies a potential fault mode of the press machine and locates an abnormal source through multi-physics field simulation data in combination with an improved CNN-LSTM model; the three-dimensional visual interaction unit constructs an interactive immersive three-dimensional virtual scene, renders a running state and a processing process in real time, and generates a maintenance strategy; and the self-adaptive regulation and control unit predicts the residual life of the key component and dynamically adjusts parameters according to a maintenance strategy and real-time monitoring data. Therefore, the problems of single monitoring dimension, disjunction of maintenance strategies and the like in the prior art are solved.

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 Internet of Things, and provides a wire and cable online quality detection method and device, electronic equipment and a storage medium. The method comprises the following steps: performing multi-scale wavelet packet decomposition and signal entropy fusion on an original wire and cable signal matrix to obtain a feature tensor, and performing anomaly recognition extraction on the feature tensor through a graph attention network model to obtain a potential defect feature vector; and performing multi-physics coupling simulation inversion on the potential defect feature vector to obtain a defect quantization parameter set, performing dynamic reasoning according to the defect quantization parameter set through a Bayesian network model to obtain an online quality grade decision, and packaging the online quality grade decision according to a block chain intelligent protocol to obtain a quality traceability record. According to the invention, through organic coupling of multiple levels, multiple models and multiple technical means, real-time performance, accuracy, traceability and safety of online quality detection of wires and cables are realized.

The invention discloses a mining rock mass structure degradation monitoring method and system based on deep learning, particularly relates to the technical field of mine safety monitoring, and is used for solving the problems of monitoring lag and misjudgment caused by the fact that an existing static model cannot automatically adapt to rock mass damage dynamic evolution. The method comprises the following steps: constructing a dynamic damage incremental data set by collecting multi-source heterogeneous monitoring data under mining disturbance in real time, extracting newly-added damage features, and calculating a distribution offset degree between the newly-added damage features and historical features; when the deviation degree exceeds the limit, verifying and screening a damage feature subset according with a mechanical law based on a rock constitutive equation, and blocking non-physical noise interference; a geological structure evolution mode is matched through damage path dependence modeling, and a potential damage path thermodynamic diagram is generated in combination with a space gradient; performing cross-stage association on the thermodynamic diagram and a historical feature library by adopting a knowledge distillation mechanism to generate a dynamic weight matrix fused with a mining time sequence; and finally, updating the damage assessment model through a parameter reweighting mechanism to realize adaptive assessment of the rock mass degradation risk.

The invention relates to the technical field of data processing, and discloses an anti-oxidation barrier layer performance degradation prediction method and system based on multi-source data. The method comprises the following steps: collecting and preprocessing multivariate data of a barrier layer environment, and constructing three-dimensional tensor data; indexes such as oxygen blocking efficiency and anti-permeability performance are calculated, and performance time sequence data are obtained; extracting multi-dimensional features and fusing the multi-dimensional features through an automatic encoder; applying a hybrid deep learning model to predict performance degradation; analyzing degradation curve characteristics, and executing mode clustering to obtain a risk matrix; and optimizing a maintenance decision scheme based on risk assessment. According to the invention, the hybrid deep learning model is utilized to capture the dependency relationship of time and space dimensions at the same time, and the prediction precision is significantly improved; based on degradation mode identification and risk level evaluation, accurate multi-scene maintenance decision is realized, environmental risk is reduced, and maintenance cost is optimized.

The invention relates to the technical field of gas pipeline safety assessment, in particular to a gas pipeline safety assessment early warning system and method based on data analysis. The method comprises the following steps: collecting gas pressure data of a gas pipeline at multiple points at the same time, carrying out time-space synchronization processing to obtain aligned gas pressure data so as to construct a continuous gas pressure field of the pipeline, based on the gas pressure field, identifying the pressure distribution difference of the pipeline, estimating the transient flow velocity of the gas, continuously recording the transient flow velocity of the gas, and deducing the change of the flow velocity. Judging the deformation condition of the inner wall of the pipeline in combination with air pressure field data, positioning a deformation area, reconstructing a pipeline frame, analyzing deformation response, identifying a deformation type, identifying a corrosion evolution trend, performing safety evaluation, judging the gas circulation degree and performing physical safety evaluation. According to the invention, dynamic monitoring of the gas pipeline is realized, a data-driven safety management mode is promoted, the early warning capability of potential risks is improved, and a more intelligent safety assessment and early warning mechanism is formed.

The invention relates to a groundwater safety assessment method under an extreme climate event, which comprises the following steps: collecting multi-source heterogeneous data such as meteorological data, geological data, hydrological data and remote sensing data, and constructing a unified groundwater safety knowledge graph through standardized cleaning, semantic alignment and deletion completion; monitoring an extreme climate event in real time, and updating a node relation weight and sparsifying a transmission path based on knowledge graph dynamic evolution and a time sequence attention mechanism; performing risk propagation path reasoning on the dynamic knowledge graph in combination with an improved graph neural network, identifying key pollution nodes, and outputting a structured risk level and a coping suggestion; the system continuously optimizes atlas and model parameters based on evolution feedback, and high adaptability and reasoning precision of emergency response are achieved. According to the method, the intelligence, the real-time performance and the accuracy of underground water risk assessment are improved. The problems that the underground water pollution propagation path is difficult to dynamically identify and the decision adaptability is insufficient under extreme climate events are solved.

The invention discloses an enterprise operation global analog simulation method based on digital twinning. The method comprises the following steps: S1, collecting and preprocessing multi-source heterogeneous data; s2, constructing an enterprise operation digital twinning model, and defining an optimization objective function and constraint conditions; s3, optimizing the model by adopting a sea elephant optimization algorithm, and dynamically adjusting structural parameters, control variables and strategy configuration; s4, multi-scene simulation drilling is carried out based on the optimization model, and enterprise operation prediction results under different strategies are obtained; s5, evaluating and comparing key performance indexes corresponding to the strategies, and determining an optimal service configuration scheme; and S6, deploying an optimal service configuration scheme, collecting feedback data, and dynamically updating the model. According to the method, the enterprise operation digital twin model which can be optimized, simulated and fed back is constructed, so that operation simulation and optimal business scheme output under multi-strategy configuration are realized, and the enterprise decision-making intelligence level and the operation efficiency are improved.