15 results about "Structural risk" patented technology

This invention belongs to the field of data processing technology, specifically relating to a method and system for generating maintenance strategies for bridge collision avoidance facilities based on knowledge graphs. The method includes: constructing a physical topology map of the collision avoidance facilities, obtaining the stiffness weights of connecting edges and initializing node parameters; collecting strain and stress data sequences of the collision avoidance units, and calculating the rheological damage index of the collision avoidance units using a dynamic time warping algorithm; constructing a force transmission probability matrix, and obtaining the comprehensive structural risk value of the collision avoidance units through iterative calculation; and calculating the maintenance urgency index by combining the position weight coefficients and service duration of the collision avoidance units to generate a dynamic maintenance strategy. This invention, by constructing a physical topology map reflecting the physical connection relationships between collision avoidance units, and combining the calculation of hysteresis energy consumption and stress transmission deduction of viscoelastic materials, can accurately identify latent transmitted damage and assess the degree of material rheological aging, thus achieving intelligent maintenance of bridge collision avoidance facilities.

The invention relates to the technical field of structural engineering and computational mechanics, in particular to a method and system for determining the axial pressure bearing capacity of a concrete filled steel tube at negative temperature, which responds to a bearing capacity calculation request, obtains working condition parameters of a target component and constructs a finite element simulation model for simulation analysis to obtain simulation bearing capacity data; the simulation bearing capacity data is compared with a standard theoretical value obtained through calculation according to a preset standard formula, a deviation coefficient is obtained, when the deviation coefficient is larger than a preset deviation threshold value, coefficient fitting is conducted through a preset size effect-negative temperature coupling correction model, a size effect correction coefficient and a negative temperature damage correction coefficient are obtained, and the size effect correction coefficient and the negative temperature damage correction coefficient are obtained. And based on the size effect correction coefficient and the negative temperature damage correction coefficient, the standard theoretical value is corrected, and the final axial compression bearing capacity is obtained. The problem of poor negative temperature adaptability caused by size effect and large-diameter deviation in the prior art is solved, and the structural risk caused by misjudgment of the bearing capacity is avoided.

The application provides a digital twin method and a predictive operation and maintenance system for a rectangular melting and separating furnace, equipment and a medium, which comprehensively considers the multidisciplinary complexity of the rectangular melting and separating furnace system by using the calculation results of the thermal coupling finite element model, is high in engineering precision, and is reliable in results; and can realize real-time forward and reverse calculation by using a statistical proxy model, can obtain the result state of the rectangular melting and separating furnace by forward process operation setting, and can perform fault detection and risk assessment, and can obtain the recommended value of the operation item setting by reverse presetting results, thereby effectively guiding the on-site production of the rectangular melting and separating furnace. Therefore, the application can efficiently and accurately predict the running state of the rectangular melting and separating furnace equipment under different process conditions, dynamically analyze the erosion and life trend of the furnace lining, identify abnormal deformation and structural risks of the furnace body in advance, guide the accurate operation and maintenance on site, significantly improve the safety, reliability and production efficiency of the furnace body, and thus has significant industrial application value and economic benefits.

The application discloses a crack-resistant reinforcement configuration optimization method for high concrete face slab dam panels, S1: acquiring first data; S2: generating second data based on a geometric model of dam panels; S3: generating an indication signal based on the first data and the second data through a Pareto frontier solver; S4: generating a reinforcement network optimization instruction based on a selected working point on a Pareto frontier curve; and S5: adjusting an initial reinforcement topology network in response to the reinforcement network optimization instruction, and outputting a final reinforcement configuration scheme; the application can realize significant synergistic gain of design safety and economic benefits by deep quantization of structural risk through probability inference, construction of a double-target optimization framework of efficiency and robustness, generation of a Pareto frontier performance boundary, and output of a reinforcement scheme with higher safety under the same cost or lower cost under the same safety standard through an interactive decision tool.

The present application relates to the technical field of image processing, in particular to a cable tower concrete crack identification method and system based on machine vision, comprising the following steps: based on the difference between the crack and non-crack areas of the cable tower surface image, identifying consistent fracture zones and marking texture levels, analyzing the gradient direction consistency and gray mutation response rate, screening mismatched areas, screening composite abnormal areas, evaluating the gray change trend, calibrating the crack response deviation, screening areas that need to adjust the coverage range, and outputting the cable tower crack identification linkage adjustment instruction set. In the present application, by extracting the local gradient direction and gray response characteristics of the image pixels, combining spatial matching and connectivity analysis, the abnormal areas with disorderly direction and response mismatch are accurately identified, and then the gray deviation of the crack area is evaluated, the crack level calibration and risk positioning are realized, the multi-scale information extraction and heterogeneous area adaptation capability are improved, and the structural risk evaluation effect is optimized.

This invention belongs to the field of data processing and risk management technology. Specifically, it discloses an environmental risk assessment method and system based on topological data analysis, comprising: acquiring multi-source real-time monitoring data; identifying dense data clusters and sparse bridge nodes through topological clustering; dynamically adjusting monitoring resources; performing dynamic feature calculations on the data to generate multi-level dynamic field data; performing continuous coherence analysis to extract stable topological patterns and encode them as structural fingerprint vectors; using these vectors to dynamically activate and adjust the environmental risk knowledge graph to generate a dynamic risk logic graph; separately deriving parametric risk scores and structural risk scores, and calculating their conflict degree; making decisions based on the conflict degree, outputting a comprehensive risk level or triggering structural risk warnings and explanatory information. This invention aims to overcome the limitations of traditional assessments in terms of lag and insufficient interpretability, achieving predictive management of environmental risks and self-optimization of the knowledge base.

This invention discloses a big data-based intelligent management method and system for water conservancy projects, belonging to the field of intelligent management technology for water conservancy projects. It collects multi-source operational data such as water flow rate, capillary permeability, freezing depth, and structural stress fluctuations, reconstructing them into spatiotemporally coupled data. Through tensor decomposition, it extracts leading factors of operational anomalies, constructs a freeze-thaw model, and infers cumulative structural risk factors. These risk factors are embedded in a graph structure, and a graph neural network is used to predict future local degradation sensitive points. Fuzzy logic reasoning is applied to these sensitive points to output reinforcement suggestions. A regional-level freezing environment vulnerability map is constructed across multiple projects, generating maintenance schedules and inspection paths. This invention achieves proactive identification and refined maintenance of freeze-thaw risks in hydraulic structures, improving project operational safety and resource allocation efficiency.

According to the digital twinning method for the rectangular melt separation furnace, the predictive operation and maintenance system, the predictive operation and maintenance equipment and the medium, the multidisciplinary complexity of the rectangular melt separation furnace system is comprehensively considered by utilizing the calculation result of the thermal-mechanical coupling finite element model, the engineering precision is high, and the result is reliable; and forward and reverse real-time calculation can be realized by utilizing a statistical agent model, the result state of the rectangular melt separation furnace can be obtained through process operation setting in the forward direction, fault detection and risk assessment can be carried out, and recommended values of operation item setting can be obtained through a preset result in the reverse direction, so that field production of the rectangular melt separation furnace is effectively guided. Therefore, the method can efficiently and accurately predict the operation state of the rectangular melt separation furnace equipment under different process conditions, dynamically analyze the erosion and life trend of the furnace lining, identify the abnormal deformation and structural risk of the furnace body in advance, guide the precise operation and maintenance operation on site, remarkably improve the safety, reliability and production efficiency of the furnace body, and reduce the production cost. Therefore, the method has remarkable industrial application value and economic benefit.

The invention relates to the technical field of financial science and technology and risk monitoring, in particular to an intelligent financial risk sensing method based on deep learning, and the method comprises the steps: regularly collecting the multi-dimensional time series data of an asset end, a liability end, economic operation indexes and the like of a target region through accessing a financial data system; constructing a cross feature representing a dynamic coupling relationship between indexes based on the cleaned data; processing by using a risk perception model comprising a time sequence feature extraction layer and a multi-head attention layer, and outputting a risk occurrence probability predicted based on a historical time sequence rule and a structure anomaly index calculated based on attention weight distribution; and finally, judging a risk level according to the double threshold values and outputting an early warning signal. According to the method, the structural discretization degree of the financial system can be quantified by introducing the structural anomaly index and sparsity regularization training, the deep structural risk under numerical value coverage is effectively identified, and automatic attribution of the risk source is realized.

This invention discloses an intelligent identification method for systemic financial risk based on causal networks and multi-model optimization. The method first reads and cleans financial market and corporate financial data to construct a quarterly logarithmic return series. Then, it calculates individual risk exposure based on the CoVaR mechanism and generates binary labels for risk states using a Gaussian mixture model (GMM). Further, it employs Granger causality testing to construct a risk transmission map between enterprises, uncovering structural risk relationships within the system. Next, it constructs a Long Short-Term Memory (LSTM) network and a Gradient Boosting Tree (XGBoost) model to predict risk states over multiple periods, and uses a Particle Swarm Optimization (PSO) algorithm to jointly fine-tune hyperparameters. Finally, it integrates the multi-model prediction results to output a systemic risk score and supports model interpretation and visualization based on SHAP values. This method is applicable to various scenarios such as financial supervision, bank risk control, and asset management, enabling efficient identification and accurate prediction of systemic risks.

This invention relates to the field of bridge monitoring technology, specifically disclosing a method, system, and device for early warning and discrimination of bridge structural risk status based on correlation network diagrams. The invention first constructs a correlation network diagram of measuring points in the bridge structure under a baseline state using preprocessed benchmark monitoring data. When a risk measuring point triggers an over-limit alarm during real-time monitoring, a risk correlation sub-network diagram is extracted centered on that risk measuring point and compared with the benchmark correlation network diagram. By analyzing the linkage anomaly characteristics of node data and changes in the correlation degree between nodes in the sub-network diagram, a true early warning (abnormal structural state) is distinguished from a false alarm (equipment failure, environmental interference, etc.). This invention significantly improves the accuracy and reliability of early warning of bridge structural risk status through multi-measuring point correlation linkage analysis.

The invention discloses a BIM (Building Information Modeling)-based existing building potential safety hazard detection and analysis method and system, relates to the technical field of building engineering, and is used for solving the problems of long response period, data lag, incomplete evaluation and strong subjective dependence caused by the fact that a manual inspection and periodic static detection mode is often adopted for potential safety hazard troubleshooting in the prior art. According to the method, structural stress monitoring, image recognition and analysis, historical use data and multi-dimensional data subjectively fed back by personnel are subjected to unified modeling, and a precisely quantified potential safety hazard assessment system is constructed in combination with a dynamic stress change trend and fluctuation degree, component apparent damage characteristics and structural risk index core parameters; according to the method, full-period and multi-dimensional evaluation and graded early warning of the existing building structure state are realized, the accuracy of structure risk identification and the real-time performance of response are remarkably improved, the structure leak detection and misjudgment risks are effectively reduced, and the structure safety in the long-term operation process of a building is guaranteed.

This invention discloses a method for monitoring and analyzing the health status of concrete pouring formwork support structures for viaducts, relating to the field of data acquisition and analysis. This invention simplifies on-site handling procedures by prioritizing easily rectifiable issues such as formwork tilt; it promptly adjusts the construction pace when the overall health level exceeds the standard, mitigating structural risks caused by increased load from the outset; it implements differentiated rectification strategies for different health status anomalies, using corresponding models to quantify and determine the scale of additional support poles, balancing the accuracy and rationality of rectification; after rectification, it increases the monitoring frequency of related indicators and conducts data trend prediction, dynamically switching monitoring frequencies based on prediction results to optimize the allocation of monitoring resources; thereby significantly improving the scientific, standardized, and intelligent level of formwork support structure hazard identification, rectification, and long-term monitoring, ensuring structural stability and safety throughout the entire construction process.

This invention discloses a method and system for evaluating the structural system of long-span bridges. The method includes: collecting and preprocessing data from manual inspections, non-destructive testing, and health monitoring of long-span bridges to form a standardized reference dataset; identifying typical defects and structural characteristics through field investigations, literature reviews, and expert surveys; establishing a multi-level evaluation framework; determining indicators for each level; and forming an evaluation system covering safety, durability, and applicability dimensions. The method employs a group hierarchical analysis approach, combining expert opinions with judgment information collected on a five-level scale, optimizing the consistency of the judgment matrix, calculating expert weights, and weighting the indicators accordingly. Quantitative indicator types are categorized, and a nonlinear dimensionless model is constructed for normalization. For sequential indicators, standardized scores are obtained through measurement point evaluation, uniform score calculation, and non-uniform coefficient analysis. Evaluation results are calculated hierarchically to identify structural risks and hazards, and maintenance plans are output. This achieves a scientific and reasonable assessment of the service status of long-span bridges.

The invention relates to the technical field of automobile safety, and particularly discloses an automobile structure safety design method based on multi-attitude simulation, which comprises the following steps: constructing an automobile composite attitude, analyzing and identifying multi-load direction nodes through load path overlapping, and screening failure risk nodes in combination with stress redundancy; calculating a simulation priority based on a failure risk node risk coefficient and a composite attitude historical incidence rate, carrying out simulation positioning of stress exceeding nodes, and dividing types; according to the method, the node optimization type is judged through the standard exceeding proportion, the nodes needing structure optimization are reinforced in combination with the maximum stress standard exceeding degree, differential path adjustment is conducted on the nodes needing path optimization according to the type, and the problems that in traditional automobile structure design, due to the fact that the composite attitude load coupling effect is neglected, the single working condition reaches the standard, and the composite working condition fails are solved; accurate recognition and targeted optimization of the automobile structure risk under the composite posture are achieved, and meanwhile the frequency of later simulation model adjustment and computer and auxiliary equipment repair is reduced.