Panel operation period deformation dynamic monitoring data analysis method

Abstract

The invention relates to the technical field of rock-fill dam concrete face operation monitoring, in particular to a face operation period deformation dynamic monitoring data analysis method. The method comprises the following steps: acquiring panel design data and constructing a panel three-dimensional simulation model; on the basis of the three-dimensional simulation model, the stress condition of the structure is obtained, and the composite stress instability state of the panel is judged in combination with heterogeneous settlement data of the foundation; the thermal fatigue growth condition of the panel is evaluated based on the simulation model, and the chain type degradation trend of the structure is accurately estimated by integrating the stress instability state; analyzing the actual deformation condition of the panel by monitoring the chain type degradation trend and the instability state and combining the operation deformation data, identifying design defects and generating design defect data; panel design is optimized and adjusted according to the design defect data; through deformation monitoring of the panel structure, the panel structure is more stable.

Description

Technical Field

[0001] This invention relates to the field of operation monitoring technology for concrete panels of rockfill dams, and in particular to a method for analyzing dynamic monitoring data of panel deformation during operation. Background Technology

[0002] The widespread application of concrete panels in large-scale hydraulic engineering projects has gradually made them a key load-bearing and control structure in the dam's anti-seepage system. With the ongoing construction of ultra-high rockfill dams (300-meter class), the panel structures face increasingly complex operating conditions and higher structural performance requirements. During long-term service, these concrete panels are subject to multiple combined effects, including the dam's self-weight stress, temperature deformation, differential foundation settlement, and rockfill deformation, leading to increasingly significant issues with operational stability and structural safety. Rockfill dam concrete panels typically feature a large width-to-thickness ratio, pronounced curved surfaces, complex joint structures, and variable stress paths. Furthermore, the service environment exhibits significant geological heterogeneity and climatic differences, resulting in a highly spatiotemporally coupled structural response. In actual operation, factors such as uneven foundation settlement, dam shear deformation, and temperature gradients can cause degradation issues such as crack propagation, joint opening, and weakened structural load-bearing capacity. However, traditional dynamic monitoring of panel deformation during operation suffers from inaccuracies in detecting the composite stress instability state of the panel and inaccurate estimation of the chain-like degradation trend of the panel structure. Summary of the Invention

[0003] Therefore, it is necessary to provide a method for analyzing dynamic monitoring data of panel deformation during operation to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a method for analyzing dynamic monitoring data of panel deformation during operation includes the following steps: Step S1: Obtain the design data of the concrete panel of the rockfill dam; collect the construction material data of the panel based on the design data of the concrete panel of the rockfill dam; construct a three-dimensional simulation model of the panel based on the design data of the concrete panel of the rockfill dam and the construction material data of the panel; Step S2: Determine the stress state of the panel structure based on the three-dimensional simulation model of the panel; detect the heterogeneous settlement of the panel foundation based on the stress state of the panel structure; determine the composite stress instability state of the panel based on the heterogeneous settlement of the panel foundation and the stress state of the panel structure. Step S3: Obtain data on changes in the surrounding environment of the panel; estimate the thermal fatigue growth of the panel based on the data on changes in the surrounding environment of the panel using the 3D simulation model of the panel, and obtain the thermal fatigue growth status of the panel; estimate the chain degradation trend of the panel structure based on the composite stress instability state of the panel and the thermal fatigue growth status of the panel. Step S4: Monitor the deformation of the panel structure based on the chain degradation trend of the panel structure according to the unstable state of the panel composite stress; evaluate the panel design defects based on the panel structure deformation to obtain panel design defect data; optimize the panel design based on the panel design defect data to obtain panel design optimization data.

[0005] This invention acquires and accurately reconstructs the geometric structure of the concrete panels of a rockfill dam, enabling the construction of a three-dimensional simulation model of the panels. This lays the foundation for subsequent structural stress analysis and settlement detection, improving the accuracy of structural assessment. By combining heterogeneous foundation settlement data with structural stress conditions, the composite stress instability state of the panels can be comprehensively identified, enhancing the dynamic monitoring capability of structural safety risks. By combining environmental change data with the simulation model, the thermal fatigue growth of the panels is scientifically estimated, effectively revealing the fatigue evolution process of the structure under the influence of complex environments and promoting the dynamic assessment of structural health. Based on the composite stress instability state and thermal fatigue information, the chain-like degradation trend of the structure is accurately judged, achieving a deep understanding of the overall degradation path of the structure. Combined with operational deformation monitoring data, the system identifies structural design defects, conducts targeted design optimization, improves the rationality of the design scheme and the structural adaptability, and significantly enhances the stability and safety performance of the structure. The overall technical process achieves comprehensive closed-loop management from design data acquisition and structural stress and settlement analysis to thermal fatigue assessment, degradation trend judgment, and design optimization, ensuring the stability and reliability of the panels during operation. Therefore, this invention is an optimization of traditional panel operation monitoring, which solves the problems of inaccurate detection of composite stress instability state of the panel and inaccurate estimation of the chain degradation trend of the panel structure. It improves the accuracy of detecting composite stress instability state of the panel and the accuracy of estimating the chain degradation trend of the panel structure. Attached Figure Description

[0006] Figure 1 A flowchart illustrating the steps of a method for dynamic monitoring and data analysis of panel deformation during operation; Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S2. Figure 3 for Figure 1 A detailed flowchart illustrating the implementation steps of step S3. The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0007] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0008] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0009] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0010] To achieve the above objectives, please refer to Figures 1 to 3 A method for analyzing dynamic monitoring data of panel deformation during operation includes the following steps: Step S1: Obtain the design data of the concrete panel of the rockfill dam; collect the construction material data of the panel based on the design data of the concrete panel of the rockfill dam; construct a three-dimensional simulation model of the panel based on the design data of the concrete panel of the rockfill dam and the construction material data of the panel; In this embodiment of the invention, design data for the concrete panel of a rockfill dam is obtained. Detailed design documents for the panel, including structural design drawings, construction drawings, material lists, and design parameter specifications, are retrieved through a project management system or design drawing database. A structural analysis tool is used to extract the geometric parameters of the panel from the design documents, extracting key design information such as panel dimensions, segmentation method, thickness, reinforcement layout, joint locations, and material performance parameters. Based on the above design data, combined with structural design specifications and engineering standards, a panel geometric structure data table is constructed, specifically including the structural dimensions, connection methods, stress nodes, and material properties of each part of the panel. Subsequently, using automatic structural data generation software, the design data and structural parameters are input, and the program reads the panel's segmentation layout, dimension information, and panel construction material data to automatically generate a three-dimensional simulation model of the panel. This model includes the panel's three-dimensional solid shape, key structural features, and material mechanical properties. During the generation process, a three-dimensional outline is first drawn based on the panel's planar dimensions and segmentation layout, then refined to the joint and reinforcement locations, and finally parameters such as the material's elastic modulus, density, and Poisson's ratio are assigned. The 3D model is saved in the standard format of the structural simulation database for subsequent analysis.

[0011] Step S2: Determine the stress state of the panel structure based on the three-dimensional simulation model of the panel; detect the heterogeneous settlement of the panel foundation based on the stress state of the panel structure; determine the composite stress instability state of the panel based on the heterogeneous settlement of the panel foundation and the stress state of the panel structure. In this embodiment of the invention, the three-dimensional simulation model of the panel obtained in step S1 is imported into a structural analysis platform, and boundary conditions and loading conditions are applied, including water pressure, structural self-weight, and foundation support conditions. Using finite element analysis software, static calculations are performed to solve for the stress distribution, deformation state, and key node displacements within the panel. The results output the principal stress, shear stress, and deformation contour maps of each element of the panel. The stress data of the bottom nodes of the panel are exported and compared with on-site foundation settlement monitoring data. Combined with geological survey data, areas of abnormal settlement are identified. Based on the settlement distribution and its impact on the stress at the bottom of the panel, heterogeneous settlement indices of the foundation are calculated, forming data on the heterogeneous settlement status of the foundation. Using this data, combined with the stress analysis results of the panel structure, the composite stress instability state of the panel is comprehensively determined. The composite stress instability state specifically includes stress concentration areas, deformation critical points, and settlement influence ranges. All relevant data are stored in the form of a structural instability state table. This step realizes the process from stress calculation of the simulation model to settlement impact assessment, obtaining composite stress instability state data.

[0012] Step S3: Obtain data on changes in the surrounding environment of the panel; estimate the thermal fatigue growth of the panel based on the data on changes in the surrounding environment of the panel using the 3D simulation model of the panel, and obtain the thermal fatigue growth status of the panel; estimate the chain degradation trend of the panel structure based on the composite stress instability state of the panel and the thermal fatigue growth status of the panel. In this embodiment of the invention, environmental monitoring systems collect data on changes in the surrounding environment of the panel, including parameters such as temperature, humidity, wind speed, and solar radiation intensity. The collected time-series environmental data is preprocessed, and an interpolation algorithm is used to generate a continuous environmental change curve. Based on the three-dimensional simulation model obtained in step S1, the environmental change curve is input to the thermal stress analysis module to calculate the internal temperature field of the panel and the resulting thermal stress distribution. Subsequently, a thermal fatigue accumulation algorithm is applied to estimate the lifespan of the thermal stress cycle, obtaining data on the panel's thermal fatigue growth. This data reflects the gradual impact of thermal fatigue on structural performance. Next, the composite stress instability data from step S2 is fused with the thermal fatigue growth data, and a degradation path analysis method is used to determine the chain degradation trend of the panel structure under the influence of stress instability and thermal fatigue. The degradation trend unfolds in both time and space dimensions, forming a chain degradation trend curve file, providing a data foundation for subsequent monitoring. This step completes the entire process from environmental data acquisition and thermal stress calculation to structural degradation trend analysis.

[0013] Step S4: Monitor the deformation of the panel structure based on the chain degradation trend of the panel structure according to the unstable state of the panel composite stress; evaluate the panel design defects based on the panel structure deformation to obtain panel design defect data; optimize the panel design based on the panel design defect data to obtain panel design optimization data.

[0014] In this embodiment of the invention, based on the chain degradation trend curve obtained in step S3 and the composite stress instability state data in step S2, a panel operation deformation monitoring system is connected. This system collects multi-dimensional deformation data of the panel in real time by deploying distributed displacement sensors, crack width sensors, and settlement gauges at key structural parts of the panel. The original displacement, crack opening, and settlement data collected by the sensors are spatially registered using a coordinate transformation algorithm and mapped to corresponding nodes of a pre-established three-dimensional simulation model of the panel, achieving accurate reconstruction and dynamic tracking of the structural deformation field. Combined with the chain degradation trend curve, the reconstructed deformation data undergoes temporal and spatial analysis to identify abnormal deformation areas and their changing patterns, revealing the correlation between deformation development and structural degradation, and dynamically updating the panel operation deformation database. This database summarizes deformation characteristic information, providing the deformation evolution process of the panel during operation. Subsequently, the deformation database is compared and analyzed with the original panel design data to locate and classify defects in areas where panel deformation exceeds limits and crack expansion areas, assessing weak points and structural safety risks in the design, and generating a detailed panel design defect data report. The report includes the extent of deformation exceeding limits, the specific locations of insufficient local structural bearing capacity, and potential safety hazards. Based on design defect data, design optimization schemes are developed. By adjusting panel segment dimensions, optimizing joint construction, and adjusting material distribution parameters, structural performance is improved. The generated design scheme data undergoes structural mechanics verification and stability validation to ensure that the adjusted parameters meet design specifications and usage requirements. The optimized data is then stored in the design optimization database, completing the design optimization closed loop. This process achieves continuous closed-loop control from dynamic monitoring of panel deformation and defect identification and assessment to design optimization adjustments, ensuring structural safety and operational stability.

[0015] Preferably, step S1 includes the following steps: Step S11: Obtain the design data for the concrete panel of the rockfill dam; In this embodiment of the invention, during the acquisition of design data for the concrete panel of a rockfill dam, the original design documents of the panel are extracted through an engineering design management system. These documents include structural design drawings, construction drawings, and detailed design specifications. The design documents clearly indicate the panel's dimensional parameters, segmentation, connection methods, load standards, and material specifications. Using a structural data extraction tool, the drawing information in the design documents is automatically parsed to obtain the panel's geometric dimensions, node arrangement, zoning, and key structural parameters. Information is extracted from the technical requirements and construction processes in the design specifications to clarify the design's functional indicators and construction control points. The extracted design data covers detailed parameters of each layer of the panel structure, such as thickness, reinforcement arrangement, and concrete mix proportions. The acquired data is standardized through database format processing to form a unified panel design dataset, providing basic input for subsequent steps. The output of this step is a complete panel design data file, including geometric parameters, structural layout, and design specification requirements.

[0016] Step S12: Obtain the panel geometry based on the design data of the concrete panel of the rockfill dam, thereby obtaining the panel geometry data; In this embodiment of the invention, structural analysis software is used to perform structural analysis on the panel design files based on the obtained design data of the rockfill dam concrete panel. The software constructs a structural frame model of the panel based on the dimensional information and connection relationships in the design data, clarifying the load-bearing units and key nodes of the panel. By analyzing the construction details in the design drawings, the structural composition of beams, columns, and slabs inside the panel is obtained, refined to specific node connection forms and reinforcement measures. The load conditions and boundary conditions in the design data are input into the structural analysis module to calculate the expected stress state and deformation trend of each part of the structure. After analysis, the panel geometric structure data file is exported, containing structural unit coordinates, node connection relationships, material distribution information, and structural stiffness matrix. This structural data includes not only the static structural layout but also the load conditions considered during design, for use in subsequent material acquisition and 3D simulation model construction. This ensures that the design data is successfully converted into structural parameter data, and the panel geometric structure data is output.

[0017] Step S13: Collect the material data for the concrete panel construction based on the design data of the rockfill dam concrete panel to obtain the material data for the panel construction. In this embodiment of the invention, based on the design data of the concrete panel of the rockfill dam, the material collection for panel construction is carried out. According to the material specifications in the design documents, the material quality inspection reports of the construction project are retrieved to obtain detailed specifications and performance parameters of the concrete, steel bars, prestressed tendons, and other materials used. Sampling and testing are performed on the materials used during on-site construction, including physical performance testing and chemical composition analysis, to ensure that the material performance matches the design specifications. Testing equipment includes a concrete strength tester, a steel bar tensile testing machine, and a material composition analyzer. The collected physical and mechanical performance data covers key parameters such as compressive strength, elastic modulus, and yield strength. The collected material performance data is compared and confirmed with the design data, and any discrepancies are archived. During the collection process, attention is paid to the correlation between material batch numbers, construction dates, and supplier information to form a complete construction material data archive. The result is a set of detailed panel construction material data, including material composition, performance indicators, and actual material information during on-site construction.

[0018] Step S14: Construct a three-dimensional simulation model of the panel based on the panel construction material data and panel geometric structure data to obtain the panel three-dimensional simulation model.

[0019] In this embodiment of the invention, a three-dimensional simulation model of the panel is constructed using the obtained panel geometric structure data and construction material data. The structural data is imported into a three-dimensional modeling platform to establish the basic geometric framework of the panel, including the size, position, and connection nodes of each block. Subsequently, the material mechanical property parameters from the construction material data are applied to the three-dimensional model, assigning corresponding elastic modulus, density, and Poisson's ratio to different regions. During the modeling process, the heterogeneity of materials in different parts of the panel is simulated in detail to reflect the spatial distribution of actual construction materials. Mesh generation technology is used to perform detailed mesh division of the panel to ensure that the model can accurately reflect the structural stress and deformation characteristics. After the model construction is completed, boundary conditions and external load conditions are imported in preparation for subsequent stress analysis and deformation monitoring. The panel three-dimensional simulation model output in this step contains complete information on structural shape and material parameters, providing basic data support for dynamic deformation monitoring and analysis during operation. The entire process ensures a high degree of integration between structural design and actual construction material information, achieving the accuracy and reliability of the simulation model.

[0020] Preferably, step S14 includes the following steps: Step S141: Determine the concrete section of the panel based on the panel geometry data to obtain the panel concrete section data; In this embodiment of the invention, information about the concrete component areas in the panel structure is extracted based on the panel's geometric data. The design structural data clearly indicates the distribution areas of different materials in the panel, including the location, shape, and extent of concrete sections. By analyzing the material distribution hierarchy in the structural design drawings and combining it with node connection relationships, the boundaries and spatial layout of the concrete sections are determined. Each section is segmented and identified, clarifying the geometric dimensions and interconnections of each concrete section. After extraction, panel concrete section data is generated, specifically including the spatial location, volume, thickness, and corresponding structural node information of each section. The data format adopts a structured three-dimensional coordinate system, facilitating subsequent strength testing and simulation model applications. This step realizes the operation of extracting the concrete usage area from the design structure and outputs detailed concrete section data.

[0021] Step S142: Monitor the foundation strength of the panel based on the data of the panel concrete section; In this embodiment of the invention, data from the concrete section of the panel is used to monitor the strength of the foundation. The physical and mechanical properties of the foundation soil are analyzed by combining geological survey reports and foundation bearing capacity test data. Real-time data on foundation bearing pressure and deformation are collected using monitoring equipment such as strain gauges and pressure sensors installed on the panel base. Based on the monitoring data, the foundation bearing capacity is calculated, focusing on the uniformity of stress and local bearing capacity changes in the foundation beneath the concrete section. Heterogeneous settlement areas are identified by combining foundation settlement measurement data. A foundation pressure distribution and settlement information are integrated using data processing algorithms to output a foundation strength status report. This report includes bearing capacity parameters and their changing trends at different locations of the foundation, ensuring a comprehensive assessment of the bearing environment of the concrete section. The monitoring results serve as the basis for subsequent structural bearing capacity calculations.

[0022] Step S143: Evaluate the compressive strength of the panel construction material based on the panel construction material data; In this embodiment of the invention, the compressive strength of concrete is evaluated based on the material data of the panel construction. Specific mix proportion data used during concrete construction are collected, including cement type, aggregate specifications, water-cement ratio, and additives. Using laboratory test results, compressive strength tests are conducted on construction concrete samples, collecting crushing loads and corresponding failure modes. The concrete strength growth pattern is analyzed by combining on-site curing environmental parameters, such as temperature and humidity. Standard specimen curing and compressive strength testing procedures are used to obtain the actual compressive strength value of the concrete. The material compressive strength data is compiled and combined with historical quality inspection records to form complete building material compressive strength data. This data reflects the load-bearing capacity of concrete in actual use, providing accurate material performance parameters for structural load-bearing capacity calculations.

[0023] Step S144: Determine the load-bearing capacity information of the panel structure based on the compressive strength of the panel construction material, the strength of the panel foundation, and the data of the panel concrete section; In this embodiment of the invention, the structural bearing capacity of the panel is determined by integrating the compressive strength data of the panel construction material, the strength data of the panel foundation, and the data of the panel concrete sections. The spatial distribution information of the concrete sections is matched with the bearing capacity data of the foundation beneath each section, establishing a correlation between the sections and the foundation bearing capacity. Next, the compressive strength parameters of each concrete section are input into the bearing capacity calculation model. Combined with the spatial variation of the foundation bearing capacity, the bearing limit of each concrete section is evaluated. A multi-level data fusion method is used to classify and determine the bearing capacity of each section, analyze potential weak points, and summarize the bearing capacity information of each section to form the overall structural bearing capacity data of the panel, clarifying the stress limit and safety boundary of the panel in different areas. This structural bearing capacity information serves as a key parameter for subsequent loading into the three-dimensional simulation model.

[0024] Step S145: Determine the outer contour boundary line data of the panel based on the panel geometry data; In this embodiment of the invention, the outer contour boundary line of the panel is determined based on the panel's geometric structure data. Specific operations include extracting the coordinates of the outermost boundary nodes of the panel from the structural design drawings to clarify the overall dimensions and shape characteristics of the panel. Using digital boundary extraction technology, the boundary points of each component in the design data are connected to form a closed two-dimensional boundary line contour. The boundary line is geometrically verified to ensure that its continuity and smoothness meet design requirements. Key corners, curve segments, and connection point coordinates are marked on the boundary line. This boundary line data is saved in a two-dimensional planar coordinate format and includes the panel's contour shape and boundary structure characteristics. This data provides the geometric basis for determining joint locations and three-dimensional spatial positioning.

[0025] Step S146: Determine the panel seam location information based on the panel's outer contour boundary line data and panel geometric structure data; In this embodiment of the invention, panel joint location information is determined using panel outer contour boundary line data and design structural data. Based on the splicing method and connection node distribution of each segment in the design structure, the spatial coordinates and extension direction of each joint are extracted. By analyzing the segmented connection points in the boundary line data, the joint locations are accurately mapped onto the panel contour, clarifying the joint length, width, and correspondence with adjacent structural units. Data on the design reinforcement measures, gap width, and material sealing scheme at the joints are recorded to form a joint location information dataset. This data contains detailed spatial positioning and structural parameters, providing fundamental joint information for constructing a three-dimensional coordinate system and simulation model. This ensures that the joint data fully reflects the actual construction connection structure.

[0026] Step S147: Construct a three-dimensional rectangular coordinate system for the panel using the panel seam location information and the panel outer contour boundary line data; In this embodiment of the invention, a three-dimensional Cartesian coordinate system for the panel is constructed based on panel seam location information and external contour boundary line data. Using the reference point on the design drawing as the origin, the X, Y, and Z axes in three-dimensional space are determined. The X-axis is parallel to the longest side of the external contour, the Y-axis is perpendicular to the X-axis, and the Z-axis is perpendicular to the plane and pointing upwards. Each node point in the seam location information is converted into a three-dimensional coordinate system according to this coordinate system, forming spatial point cloud data of the seam. Combined with the two-dimensional coordinates of the boundary line, height information is assigned along the Z-axis direction to generate the three-dimensional contour framework of the panel. Spatial data processing algorithms are used to mesh the seam points and contour points in the coordinate system, forming a unified three-dimensional spatial coordinate system. This three-dimensional coordinate system accurately reflects the structural boundaries and connection positions of the panel, laying the foundation for the geometric representation and stress analysis of the subsequent three-dimensional simulation model.

[0027] Step S148: Construct a three-dimensional simulation model of the panel based on the panel's three-dimensional rectangular coordinate system and the panel's structural load-bearing strength information to obtain the panel's three-dimensional simulation model.

[0028] In this embodiment of the invention, a three-dimensional simulation model of the panel is constructed by combining the panel's three-dimensional Cartesian coordinate system and structural load-bearing strength information. Node and boundary data from the three-dimensional coordinate system are imported into the simulation platform, and mechanical properties such as elastic modulus and compressive strength are assigned to materials in different regions based on the structural load-bearing strength data. Using mesh generation technology, the panel is divided into finite elements, refined to the joint areas and regions with significant changes in load-bearing strength, ensuring the model's accuracy and detail. During the construction process, the node connection methods, boundary conditions, and applied loads are defined to realistically reflect the stress state of the designed structure. After completing the simulation model construction, a three-dimensional simulation model file containing geometry, material properties, and load conditions is output. This model serves as the basis for subsequent dynamic deformation monitoring and data analysis during operation, supporting the accurate simulation and prediction of the panel structure's performance.

[0029] Preferably, step S2 includes the following steps: Step S21: Determine the stress state of the panel structure based on the three-dimensional simulation model of the panel; In this embodiment of the invention, a three-dimensional simulation model of the panel is used to perform stress analysis on the panel structure. Specifically, by importing the three-dimensional simulation model data, finite element analysis (FEM) technology is employed to calculate the stress distribution of the overall panel structure and its key components. During the FEM analysis, based on the geometry, material properties, and connection relationships of each segment in the three-dimensional model, the panel is divided into multiple elements. Design loads, including self-weight, wind loads, and seismic loads, are applied to each element. By solving for the stress-strain state of each element, the stress data of the entire panel is obtained. The obtained data includes information such as the magnitude, direction, and strain distribution of forces at each key node of the panel. This stress data provides fundamental data support for subsequent foundation settlement analysis and structural stability assessment. This step ensures the accurate quantification of the structural stress, fully describing the mechanical response of the panel under design conditions.

[0030] Step S22: Detect the heterogeneous settlement of the panel foundation based on the stress condition of the panel structure; In this embodiment of the invention, based on the stress data of the panel structure, the heterogeneous settlement of the panel foundation is detected and analyzed. Physical property data of the panel foundation, including soil layer distribution, bearing capacity parameters, and groundwater level changes, are obtained through geological survey reports and field test data collection. Then, combined with the structural stress distribution characteristics, settlement calculation methods are applied to calculate the settlement amount in different foundation areas, focusing on the foundation settlement response in areas with higher stress. The settlement calculation employs the elastic theory of foundation soil and the finite difference numerical calculation method, combined with the actual soil heterogeneity, to calculate the settlement differences and heterogeneity indicators in each area. Finally, a data report on the heterogeneous settlement of the foundation is generated, including a settlement distribution map, heterogeneity degree values, and corresponding geographical locations. This data provides the basic input for subsequent tilt risk assessment.

[0031] Step S23: Estimate the panel tilt risk trend based on the heterogeneous settlement of the panel foundation to obtain the panel tilt risk trend; In this embodiment of the invention, settlement time-series data from different regions are collected from a foundation settlement monitoring system. Regions with significant settlement differences and fluctuations are prioritized for analysis to ensure that the selected regions accurately reflect the heterogeneity of the foundation. The settlement time-series data for these regions are preprocessed to remove outliers and noise, ensuring data quality. Subsequently, time-series analysis techniques, including trend decomposition, stationarity testing, and autoregressive models, are applied to systematically identify the long-term trend and short-term fluctuations of settlement changes, clarifying the patterns and rates of foundation settlement changes. Combined with statistical curve fitting methods, multinomial regression or exponential smoothing techniques are used to fit the settlement data to obtain a continuous curve of foundation settlement changes. Based on the fitting results, the continuation trend of foundation settlement within a certain future timeframe is inferred. Furthermore, considering the mechanical properties and stress distribution of the panel structure, mechanical analysis methods are used to assess the impact of settlement changes on the overall and local structures of the panel, calculating the changes in structural tilt angles caused by foundation settlement and their spatial distribution, and identifying key areas with high tilt risk. By calculating the tilt rate, i.e., the rate of change of the tilt angle per unit time, and combining the settlement trend and structural response, a panel tilt risk trend dataset is formed. This dataset specifically includes information such as the predicted tilt angle change value, the spatial coordinates of the tilted location, and its tilt rate. The quantitative results of this tilt risk trend provide a scientific and accurate basis for subsequent judgment of the structural composite instability state, supporting structural safety monitoring and maintenance decisions.

[0032] Step S24: Determine the composite stress instability state of the panel based on the panel tilt risk trend and the stress condition of the panel structure.

[0033] In this embodiment of the invention, the tilt angle change value and its rate information contained in the tilt risk trend data are spatially and temporally correlated with the stress distribution data of the panel structure. By superimposing the geometric deformation effect caused by the tilt angle onto the original structural stress state, the influence of tilt on key nodes and stress units of the panel is analyzed, clarifying the additional bending moment, shear force, and axial force changes caused by tilt. Combining the basic principles of structural mechanics, using equilibrium equations and mechanical constraints, the stress and strain distribution of the structure under different tilt conditions is calculated, and the overall and local stress state is evaluated. Based on the calculation results, a panel instability discrimination model is constructed. This model determines whether the structure has entered the critical instability region by comparing the actual stress on the structure with the ultimate bearing capacity of the materials and components. The model focuses on the additional internal forces caused by tilt, especially the superposition effect of bending moment and shear force, to identify whether the structure shows signs of yielding, plastic hinge formation, or local yielding, and to determine whether it exceeds the bearing limit. The instability discrimination process includes the calculation of the structural safety factor, which is defined as the ratio of the structural bearing capacity to the actual stress, used to quantify the structural stability margin. By further combining tilt rate information, risk stratification technology is used to classify instability risks into different levels, distinguishing between high-risk, medium-risk, and low-risk areas. The output panel composite stress instability state data includes structural safety factor values, instability risk levels, and corresponding spatial distribution information, providing clear quantitative indicators and instability state descriptions for structural safety assessment.

[0034] Preferably, step S22 includes the following steps: Step S221: Detect the over-limit condition of the foundation bearing capacity based on the stress condition of the panel structure, and obtain the over-limit condition of the foundation bearing capacity; In this embodiment of the invention, this step detects the overload condition of the foundation bearing capacity based on the stress data of the panel structure, obtaining the stress data transmitted from the panel structure to the foundation, specifically including static and dynamic load information at the column base, foundation slab, and bearing surface. Using soil layer classification and bearing capacity test results from the geological survey report, combined with the bearing capacity limit indicators of the foundation soil (such as allowable bearing capacity and ultimate bearing capacity), the bearing capacity threshold of each region of the foundation is determined through numerical analysis. The structural stress data is mapped onto the foundation bearing surface for spatial distribution calculation, calculating the actual bearing pressure of each bearing region. By comparing the calculated pressure with the foundation bearing capacity threshold, overloaded areas are identified, and the extent of the overload is quantitatively analyzed. The detection process uses the soil bearing capacity module in finite element software, combined with the discrete element method to simulate the nonlinear deformation characteristics of the soil, improving detection accuracy. The output data includes the spatial coordinates of the specific overloaded areas, the bearing pressure value, and the overload ratio, providing accurate input for subsequent foundation stress distribution analysis.

[0035] Step S222: Analyze the foundation stress distribution based on the excessive bearing capacity of the foundation to obtain foundation stress distribution data; In this embodiment of the invention, based on the obtained data on the excessive bearing capacity of the foundation, further analysis of the foundation stress distribution is conducted. The excessive area of ​​the foundation is divided into multiple finite analysis units. Based on soil mechanics parameters such as soil elastic modulus and Poisson's ratio, three-dimensional finite element analysis software is used to mesh the foundation and apply loads. By applying the excessive pressure distribution identified in step S221, the stress tensor in each unit is calculated, including vertical stress, horizontal stress, and shear stress components. During the analysis, considering the interlayer interface conditions and soil heterogeneity, a layered coupling calculation method is adopted to accurately reflect the stress transmission and distribution characteristics in complex soil layers. The calculation results form a stress cloud map and stress curve inside the foundation, marking the stress peak area and stress gradient change trend. The output stress distribution data includes a regional stress value distribution table, contour map, and three-dimensional stress cloud map, serving as the basis for judging the local stress concentration trend.

[0036] Step S223: Determine the local stress concentration trend of the foundation based on the foundation stress distribution data; In this embodiment of the invention, based on the foundation stress distribution, the local stress concentration trend is determined. Statistical analysis is performed on the stress distribution data to calculate indices such as mean, variance, and peak value, identifying high-stress areas that significantly deviate from the mean. Spatial clustering algorithms are used to cluster high-stress points, defining the range and boundaries of stress concentration areas. Combining stress gradient changes, the intensity and diffusion trend of local stress concentration areas are quantified, and time-series data comparison analysis is used to determine the evolution direction of stress concentration areas. The analysis considers the nonlinear behavior of soil materials and stress hysteresis effects, using a multivariate statistical model to correct the analysis results and output local stress concentration trend data of the foundation, including the location coordinates, area size, strength indices, and future development trend estimates of the stress concentration areas, serving as input data for foundation fatigue analysis.

[0037] Step S224: Perform foundation time-stress fatigue analysis based on the local stress concentration trend of the foundation to obtain foundation time-stress fatigue data; In this embodiment of the invention, the implementation steps for foundation time-stress fatigue analysis targeting local stress concentration trends include collecting load time series data and related environmental change parameters during long-term foundation operation, such as fluctuations in groundwater level and temperature changes. By collecting this data, the dynamic working conditions and environmental impacts on the foundation can be fully reflected. Combined with previously identified stress concentration zones, a local soil fatigue model is established. Based on cumulative damage theory, the model uses Miner's rule to superimpose fatigue damage calculations on the foundation soil at different time periods. During the fatigue analysis, the failure mechanism of the soil microstructure is considered in detail. By simulating the initiation and propagation of internal cracks in soil particles and the evolution of plastic deformation, the influence of stress amplitude changes on soil strength and stiffness is revealed. To accurately capture the dynamic response of the foundation, the time-domain finite element analysis method is used to simulate the stress change process in local areas of the foundation under periodic loads, focusing on analyzing the cyclic characteristics and evolution of stress and strain. By inputting the actual time series load into the finite element model, the dynamic stress distribution is obtained. Furthermore, the fatigue damage value is calculated using the cumulative damage model, resulting in a fatigue damage accumulation curve that varies with time. The output foundation time-stress fatigue data includes detailed fatigue life prediction curves, clearly defining the remaining service life of the foundation under current load and environmental conditions; cumulative fatigue damage values, quantifying the degree of structural degradation of local soil masses due to fatigue; and time-varying trend graphs, showing the increase in fatigue damage over operating time. This analysis systematically reveals the fatigue damage development trend of the foundation in local stress concentration areas, providing a solid data foundation and technical support for subsequent assessment and early warning of soil loosening conditions.

[0038] Step S225: Determine the looseness of the foundation soil particle structure based on the foundation time-stress fatigue data; In this embodiment of the invention, the looseness of the foundation soil particle structure is determined based on foundation time-stress fatigue data. Combining soil laboratory physical test data (such as particle size distribution, porosity, and compaction degree), micromechanical analysis methods are used to calculate the impact of fatigue damage on the bonding force and contact state between soil particles. The degree of looseness of the soil particle arrangement is assessed by calculating the changes in interparticle friction and adsorption forces under fatigue. Digital image analysis technology is used to quantitatively extract features from the microscopic images of soil samples taken by scanning electron microscopy (SEM), and a looseness index system is established in conjunction with fatigue data. The output data on the looseness of the foundation soil particle structure includes particle looseness values, spatial distribution of loose areas, and trend diagrams, serving as the basis for shear strength attenuation detection.

[0039] Step S226: Detect the degree of shear strength attenuation of the foundation based on the looseness of the soil particle structure and the foundation time-stress fatigue data; In this embodiment of the invention, based on the looseness of the foundation soil particle structure and time-stress fatigue data, the degree of shear strength attenuation is detected. A constitutive model from soil shear test results is applied, combined with a looseness index, to establish a quantitative relationship between shear strength and particle structure state. Subsequently, numerical simulation techniques (such as the finite element method or discrete element method) are used to simulate the shear strength variation process of soil under different loose states and fatigue damage. Using field shear wave velocity tests and laboratory triaxial shear test data, the numerical model parameters are calibrated to improve calculation accuracy, resulting in a curve showing the change of shear strength with time and fatigue accumulation. The data on the degree of foundation shear strength attenuation, including the current shear strength value, attenuation rate, and attenuation region distribution, provides quantitative support for settlement assessment.

[0040] Step S227: Detect the heterogeneous settlement of the panel foundation based on the degree of shear strength attenuation.

[0041] In this embodiment of the invention, the heterogeneous settlement of the panel foundation is detected based on the degree of shear strength attenuation. The soil elastoplastic deformation theory is employed, incorporating soil deformation caused by shear strength changes into the settlement calculation model. A multi-layer soil settlement calculation method is used, combining the shear strength variations in different areas of the foundation to calculate the settlement of each soil layer. Foundation settlement monitoring data (such as leveling measurements and tiltmeter records) is used for correction, and combined with numerical simulation results, a three-dimensional heterogeneous settlement distribution map is constructed. The settlement amplitude, rate, and spatial differences are analyzed to identify settlement concentration areas and potential risk areas. The heterogeneous settlement data of the panel foundation is output, specifically including settlement values, spatial coordinate distribution, and dynamic trends, providing a data foundation for subsequent tilt risk trend estimation.

[0042] Preferably, step S24 includes the following steps: Step S241: Extract panel tilt angle evolution information based on panel tilt risk trend; In this embodiment of the invention, panel tilt risk trend data is used as input to extract detailed evolution information of the panel tilt angle. Real-time data is collected from high-precision tilt sensors (such as MEMS tiltmeters) and inertial measurement units (IMUs) deployed at key nodes of the panel structure. These sensors collect the structure's attitude angles at high frequencies (e.g., above 10Hz), covering roll, pitch, and yaw angles. After collection, the raw data is imported into a professional data processing platform, where multi-step data purification is performed using time-series signal preprocessing techniques, including removing sensor zero-point drift, environmental vibration interference, and short-term abnormal jumps. Noise suppression and signal smoothing are achieved through moving window filtering and wavelet transform techniques to ensure that the tilt angle evolution curve reflects the true structural changes. Subsequently, the rate of change of the tilt angle is calculated using the differential method on the purified data, supplemented by time series decomposition techniques (such as Empirical Mode Decomposition, EMD) to analyze the multi-scale evolution trend of the tilt angle, marking key inflection points and acceleration mutation segments. A structured time-series data file is output, containing timestamps, angle values ​​in each direction, rate of change, and trend analysis results, providing accurate data support for subsequent center of gravity trajectory identification.

[0043] Step S242: Based on the evolution information of the panel tilt angle, identify the center of gravity offset trajectory of the panel to obtain the center of gravity offset trajectory data of the panel; In this embodiment of the invention, based on the tilt angle evolution information obtained in step S241, the panel's center of gravity offset trajectory is identified, a three-dimensional spatial coordinate system of the panel structure is established, and a rigid body dynamics model is constructed by combining the panel's geometric dimensions and mass distribution parameters. The temporal change of the panel's overall attitude is calculated by converting the three-axis tilt angles into rotation matrices. A sensor network fusion algorithm (such as a Kalman filter) is used to fuse tilt angle data from multiple monitoring points, improving attitude estimation accuracy. Based on the attitude change and combined with the structural weight distribution, the spatial offset of the structure's center of gravity relative to its initial position is calculated. The time-series center of gravity offset is smoothed to eliminate occasional jumps. A continuous and smooth center of gravity offset trajectory is generated using trajectory fitting techniques (such as B-spline curve fitting). The output includes a three-dimensional center of gravity coordinate sequence containing the time series, trajectory curve parameters, and offset rate, providing a spatial displacement basis for detecting bottom force asymmetry.

[0044] Step S243: Detect the asymmetry of the bottom force based on the panel's center of gravity offset trajectory data to obtain the asymmetry data of the bottom force of the panel; In this embodiment of the invention, based on the panel's center of gravity offset trajectory data, the asymmetry of the force at the bottom is detected. Force sensor data from multiple support points at the bottom of the panel is collected, including real-time values ​​from pressure sensors and strain gauges. Through structural static equilibrium analysis, the theoretical force distribution at each support point is calculated based on the additional moment and shear force generated by the center of gravity offset. The measured sensor data is compared with the theoretical calculation results, and residual analysis and statistical methods (such as principal component analysis, PCA) are used to identify abnormal force distributions. Combined with a multivariate anomaly detection algorithm, the degree of force asymmetry is quantified, and the asymmetry coefficient and its spatial distribution are calculated. A bottom force asymmetry data file is output, containing the force value at each support point, the asymmetry index, and the asymmetry direction vector, supporting data input for the tensile stress growth detection step.

[0045] Step S244: Detect the tensile stress growth of the panel-foundation based on the asymmetric stress data at the bottom of the panel; In this embodiment of the invention, based on the asymmetric stress data at the bottom, the tensile stress growth at the panel-foundation connection interface is detected. Using the asymmetric stress data, combined with the geometry of the connection interface and material physical parameters (elastic modulus, bond strength, etc.), a stress conversion formula is employed to calculate the tensile stress distribution at the connection surface. Fiber Bragg grating (FBG) strain sensors are deployed on-site to monitor minute strain changes at the connection interface in real time. After acquiring FBG sensor data, spectral demodulation is performed to convert it into strain values. Combined with structural stress data, an inversion algorithm is used to calculate the temporal variation curve of tensile stress. Comparison with historical data determines the magnitude and trend of tensile stress growth. The tensile stress growth data is output, including the temporal stress curve, peak position, growth rate, and corresponding timestamp, providing crucial data information for subsequent contact failure risk assessment.

[0046] Step S245: Assess the risk of contact failure at the connection interface based on the tensile stress growth of the panel-foundation; In this embodiment of the invention, when assessing the risk of contact failure at the joint interface using tensile stress growth data, a microcrack propagation model is constructed based on fracture mechanics theory. This model uses peak tensile stress and loading frequency as core parameters, combined with the fracture toughness and critical tensile stress threshold of the connecting materials, to describe the crack initiation and propagation process at the joint interface. Specifically, the peak tensile stress is obtained through dynamic data collected by stress sensors or strain gauges, while the loading frequency reflects the periodic changes in the actual structural stress. Fracture toughness and critical tensile stress threshold are determined by material testing data to ensure the model's physical realism. Subsequently, numerical simulation software is used to solve the microcrack propagation model numerically, simulating the crack initiation location, propagation path, and propagation speed under different tensile stresses. The simulation process employs the finite element method, refining the interface mesh to accurately capture the crack response in stress concentration areas and dynamically simulate the impact of crack propagation on the overall structural stress. Parallel acoustic emission sensor monitoring collects high-frequency elastic wave signals generated by crack activity in real time through multiple sensing points deployed at the connection interface. Time-frequency analysis techniques are used to process the signals, identifying the characteristic frequencies and energy variation trends of crack propagation, thereby confirming the timing and location of crack activity. The accuracy of crack propagation prediction is ensured by comparing the acoustic emission signals with numerical simulation results. Based on the critical tensile stress threshold, the simulated crack propagation path and acoustic emission data are combined to calculate the risk probability distribution of contact failure, quantifying the probability of crack-induced failure. The output contact failure risk data includes a crack distribution heatmap, which uses color gradients to display crack density and propagation extent. The failure probability is presented as a percentage or probability density, and a potential crack path map is also provided, detailing the direction of crack propagation and the location of key nodes.

[0047] Step S246: Identify local overload areas of the panel based on the asymmetric stress data at the bottom of the panel and the stress condition of the panel structure; In this embodiment of the invention, the process of identifying local overload areas of the panel by combining the asymmetric stress at the bottom with the stress data of the panel structure includes the following steps: Based on the actual size of the panel and the density of sensor placement, a sensor grid is used to cover the panel surface, dividing the panel into several small units of equal area or based on structural characteristics. Each unit corresponds to monitoring data from several pressure sensors and strain gauges, ensuring the detail and comprehensiveness of local stress monitoring. Subsequently, for each small unit, its corresponding real-time stress data is collected, mainly including the pressure and strain values ​​measured by the sensors. This data is preprocessed to eliminate noise and outliers, ensuring data accuracy and reliability. Based on this data, the stress concentration factor of each small unit is calculated. This is achieved by comparing the current stress value with the average stress value and standard deviation during the unit's historical normal operation. A standardization method is used to convert the current stress condition into a dimensionless concentration factor index. Specifically, the standardization process is performed using the formula (current stress value minus historical average value, then divided by standard deviation) to quantify the degree of stress anomaly. If the concentration factor exceeds a preset threshold, it is determined that the unit is overloaded. Next, considering the impact of the additional load generated by the asymmetric force at the bottom on the overall and local stress state of the panel, based on the principle of static superposition, the additional force and moment generated by the asymmetric force are transformed into a local additional load distribution, and superimposed on the original force data of each small unit to form a dynamic local force superposition field, thereby reflecting the strengthening effect of the asymmetric force on the local structure. Subsequently, combined with real-time monitoring data from sensors, a data fusion algorithm is used to dynamically verify the superimposed force state, ensuring that the theoretically calculated overload area matches the actual monitoring data, further improving the identification accuracy. The system automatically generates spatial coordinate information of the overload area, clearly identifying the location of the small unit with excessive force, and outputting the corresponding force amplitude of each unit, forming a complete force distribution map of the overload area. Based on time series analysis, the system also plots the dynamic change curve of the overload area, reflecting the fluctuation trend of the local overload degree over time, and assisting in the analysis of the force change law of the structure during operation. The overload area identification result serves as an important input for the determination of the composite force instability state, supporting the subsequent comprehensive assessment of the overall structural safety and stability.

[0048] Step S247: Determine the composite stress instability state of the panel based on the local overload area of ​​the panel and the risk of contact failure at the connection interface.

[0049] In this embodiment of the invention, the determination process of composite stress instability is specifically unfolded as a multi-level, multi-factor coupled analysis, combining the contact failure risk data and local overload area data obtained in the steps. The contact failure risk data obtained in the steps is spatially mapped to clarify the failure probability and risk level of each key contact part in the structure. Subsequently, the spatial distribution information of the local overload area is superimposed onto the spatial coordinate system of the contact failure risk data to form a multi-dimensional risk superposition field. This superposition process is based on strict matching of spatial coordinates, ensuring that each structural unit simultaneously possesses two types of risk information, achieving comprehensive control over the composite stress state. Next, a mathematical model combining structural mechanics and risk assessment is used to comprehensively consider overload strength, failure probability, material fatigue characteristics, and historical damage data to calculate the overall stability influence index of the structure. This index reflects the comprehensive risk level of structural instability caused by composite stress. The calculation process involves weighted superposition of the risk weights of each unit and transforming them into a unified influence index through a nonlinear mapping function to quantitatively express the severity of composite instability. Subsequently, combined with dynamic parameters such as stress changes, deformation, and temperature field in real-time monitoring data, a risk stratification algorithm is used to classify the overall structural state. The stratification standard divides the structural state into several levels based on different ranges of the influence index, from a safe state to a high-risk instability state, ensuring that the judgment results are discriminative and targeted. The judgment results are presented in a variety of intuitive forms, including a three-dimensional risk distribution map, which clearly shows the instability risk intensity of each area through color depth and spatial location; a level classification table that details the risk level and risk factor weight of each unit or area; and a trend prediction curve that infers future risk change trends based on historical and real-time data, supporting early warning and decision-making during the structural operation period.

[0050] Preferably, step S3 includes the following steps: Step S31: Obtain data on changes in the surrounding environment of the panel; In this embodiment of the invention, multiple high-precision sensors deployed on the panel and its surrounding environment enable continuous acquisition of environmental change data. The sensor system includes distributed temperature sensors, humidity sensors, wind speed and direction sensors, vibration sensors, and radiation intensity sensors. Each sensor acquires environmental parameters in real time according to a preset sampling frequency (e.g., once per minute). The acquired parameters include temperature, humidity, wind speed, wind direction, vibration amplitude and frequency, and solar radiation intensity. The acquisition device transmits the data to a data processing center via wired or wireless means. The data processing center uses signal filtering algorithms to remove noise and abnormal data, ensuring the accuracy and completeness of the environmental data. After preprocessing, the data is managed using a time-series database, which can store environmental change information at multiple time scales, supporting subsequent in-depth analysis of short-term fluctuations and long-term trends. Among the environmental change data, temperature change curves and radiation intensity fluctuations are particularly critical, directly affecting the evolution of panel thermal fatigue. This step ensures that through multi-parameter, multi-dimensional data acquisition and processing, detailed and real-time environmental foundation data are provided for thermal fatigue estimation and structural state analysis, forming a dynamic dataset of environmental changes around the panel.

[0051] Step S32: Based on the three-dimensional simulation model of the panel, estimate the thermal fatigue growth of the panel by analyzing the data on changes in the surrounding environment of the panel, and obtain the thermal fatigue growth status of the panel. In this embodiment of the invention, the environmental change data collected in step S31 is used as input, and thermal fatigue growth estimation is carried out in conjunction with a three-dimensional thermodynamic finite element simulation model of the panel. The three-dimensional simulation model includes the panel's geometry, material thermophysical properties (such as coefficient of thermal expansion, thermal conductivity, heat capacity, etc.), and boundary conditions. By using real-time ambient temperature and radiation intensity change data as thermal boundary conditions, the time-varying distribution of the internal temperature field and the corresponding thermal stress response of the panel are calculated. Finite element software is used to divide the panel into fine element meshes, and each element independently calculates temperature changes and the resulting thermal stress. Combining the cumulative damage theory of cyclic thermal stress, the Miner linear cumulative damage method is used to handle multi-cycle thermal stress cycles, quantifying the degree of thermal fatigue damage in each element. The output of the thermal fatigue growth status is the damage index for each node and element, reflecting the spatiotemporal distribution of thermal fatigue damage in different regions. This data provides a direct basis for quantitatively assessing the cumulative fatigue damage of structural materials caused by environmental thermal changes, supporting subsequent analysis of structural deterioration and degradation trends.

[0052] Step S33: Determine the structural deterioration of the panel based on the composite stress instability and thermal fatigue growth of the panel. In this embodiment of the invention, based on the composite stress instability state data and the thermal fatigue growth status obtained in step S32, spatial-temporal alignment and multi-factor overlay analysis are used to match the spatial coordinates of the instability region identified in the composite stress instability state with the high thermal fatigue damage region. A weighted overlay method is used to combine the influence of both to generate a comprehensive damage index. The weights are quantitatively calculated based on the contribution of the two types of damage to structural stability. Numerical statistical methods are used to process data from multiple time periods to analyze the growth rate and distribution changes of structural damage. During the statistical analysis, multiple linear regression or principal component analysis methods are used to reveal the key driving factors of deterioration and their interaction laws. The output results are presented as a spatial distribution map showing the degree of structural deterioration and its evolution trend, and time series curves are used to clarify the speed and magnitude of structural performance decline. This structural deterioration growth status provides basic data support for accurately monitoring the changes in the panel's operating status and guiding maintenance decisions.

[0053] Step S34: Estimate the chain degradation trend of the panel structure based on the panel structure deterioration growth and panel thermal fatigue growth.

[0054] In this embodiment of the invention, a dynamic health state transition model is established using structural deterioration growth data and thermal fatigue growth data to simulate the chain evolution process of a panel from localized damage to overall degradation. The time-series features of structural deterioration and thermal fatigue data are extracted, and the deterioration rate and acceleration indicators are calculated. A health state transition model is established by constructing a Markov process or Bayesian network and defining the transition probability matrix between each health state. The model input is the current degree of deterioration of each structural unit. The transition probabilities are trained using historical data to achieve dynamic prediction of the structural health state. Based on this model, the deterioration path and risk areas of the structure at different future time points are predicted, and a chain degradation trend curve and early warning indicators are output. This degradation trend reveals the transmission law and potential diffusion path of damage, assisting in the assessment of the long-term stability of the panel during operation. By continuously inputting new monitoring data, the state transition model is dynamically updated to ensure the timeliness and accuracy of degradation trend prediction. Data is transferred sequentially between steps to ensure that structural state determination and degradation trend prediction form a closed loop, constructing a complete dynamic monitoring data analysis system for panel deformation during operation.

[0055] Preferably, step S32 includes the following steps: Step S321: Perform environmental change time series analysis based on the changes in the surrounding environment of the panel to obtain the surrounding environment change time series data; In this embodiment of the invention, environmental change data around the panel is continuously collected by various environmental sensor devices. Data types include temperature, humidity, wind speed, wind direction, and solar radiation intensity. Time-series analysis of environmental changes is performed on the collected environmental parameter data, preprocessing it with time as the axis, including data cleaning, noise reduction, and outlier removal to ensure data quality. Subsequently, time-series data analysis methods are used to extract the periodicity, trend, and fluctuation characteristics of each environmental parameter. Time-series decomposition techniques are used to divide the environmental parameters into trend terms, seasonal terms, and residual terms. Frequency domain analysis is performed on the fluctuation patterns of solar radiation intensity and temperature to reveal periodic variation characteristics. The environmental change time-series data is stored in a multi-dimensional time series format, accurately reflecting environmental dynamics at different time scales. During the analysis, statistical methods are used to verify the autocorrelation and cross-correlation of the data to ensure the inherent consistency of the time-series data. This time-series data provides a foundation for subsequent detailed analysis of solar radiation intensity fluctuations and thermal stress response, ensuring the accuracy and completeness of environmental inputs in the estimation of thermal fatigue growth.

[0056] Step S322: Analyze the fluctuations in solar radiation intensity based on the time series data of changes in the surrounding environment to obtain data on the fluctuations in solar radiation intensity; In this embodiment of the invention, solar radiation intensity parameters are extracted from the environmental change time-series data obtained in step S321 for in-depth analysis. The radiation intensity data is smoothed to remove sudden anomalies and short-term noise interference. The moving average and standard deviation of solar radiation intensity are calculated using the sliding window statistical method to reveal the short-term fluctuation amplitude of radiation intensity. Subsequently, the radiation intensity time-series data is converted to the frequency domain using the Fourier transform method to identify the dominant periodic components and fluctuation frequencies, and to determine the main variation period and amplitude of radiation intensity. Segmented analysis of solar radiation changes over a specific time period is performed, and statistical tests are used to verify the significant differences in radiation intensity changes across different time periods. The generated solar radiation intensity fluctuation data includes the fluctuation amplitude, fluctuation frequency, periodic characteristics, and temporal distribution of the time series, reflecting in detail the potential impact of solar conditions on the panel's thermal response. This fluctuation data serves as input data for thermal stress response detection, ensuring that subsequent analysis is based on accurate and dynamic radiation change information.

[0057] Step S323: Based on the three-dimensional simulation model of the panel, detect the thermal stress response of the panel by analyzing the fluctuation data of solar radiation intensity, and obtain the thermal stress response status of the panel. In this embodiment of the invention, a three-dimensional thermodynamic finite element simulation model is constructed using the structural parameters, material thermal properties, and boundary conditions of the panel. The solar radiation intensity fluctuation data obtained in step S322 is used as the thermal boundary input and applied to the model surface to simulate the temperature distribution of the panel at different time points. The model is divided into a fine finite element mesh, and each element calculates the temperature field based on the principles of heat conduction and convection. Furthermore, the thermal stress distribution caused by the temperature gradient is calculated by combining the coefficient of thermal expansion. The thermal stress data from multiple time periods are time-series superimposed to obtain the thermal stress cycle characteristics. By extracting the thermal stress curves of the nodes with the maximum thermal stress and key areas in the model, the thermal stress response status of the panel is output, specifically including the maximum thermal stress value, distribution range, and trend. This thermal stress response data provides a quantitative basis for subsequent monitoring of abnormal thermal expansion and material degradation, ensuring that the estimation of thermal fatigue growth has a scientific basis.

[0058] Step S324: Monitor the abnormal thermal expansion of the panel based on the panel's thermal stress response; In this embodiment of the invention, based on thermal stress response data and combined with the thermal expansion physical characteristics of the panel, a thermal expansion calculation formula is used to quantitatively estimate the thermal expansion deformation of each structural unit. The difference between the thermal expansion deformation and the historical normal range is analyzed to identify abnormal thermal expansion regions exceeding the design allowable range. The determination of abnormal thermal expansion conditions is based on a set thermal deformation threshold, and the calculation results are verified using real-time deformation monitoring data from sensors. By establishing a correlation curve between thermal expansion deformation and thermal stress, the influence mechanism of thermal stress on thermal expansion is further analyzed. Abnormal thermal expansion regions are output in the form of spatial coordinates and deformation amplitude, forming a thermal expansion anomaly distribution map. This abnormal thermal expansion data provides key input for panel structural safety analysis, supporting subsequent material thermal degradation detection and thermal fatigue growth estimation.

[0059] Step S325: Detect the thermal degradation of the panel material based on the abnormal thermal expansion and thermal stress response of the panel; In this embodiment of the invention, thermal expansion anomaly regions and thermal stress response data are combined to conduct material thermal degradation detection. Using the thermal aging characteristic parameters of the panel material, the cumulative amount of thermal fatigue damage is calculated based on the peak thermal stress and thermal expansion deformation frequency. Employing material damage mechanics theory, a fatigue life model is used to correlate thermal stress cycles with material performance degradation, estimating the degree of material hardness reduction, elastic modulus change, and microcrack formation. Non-destructive testing techniques, such as infrared thermography and acoustic emission monitoring, are incorporated during the detection process to assist in confirming the location and extent of thermal degradation. The output thermal degradation data includes the spatial distribution of the degraded area, degradation level indicators, and rate of change. This data provides fundamental information for estimating thermal fatigue growth, reflecting the structural degradation state of the material caused by thermal stress.

[0060] Step S326: Estimate the thermal fatigue growth of the panel based on the thermal deterioration of the panel material to obtain the thermal fatigue growth status of the panel.

[0061] In this embodiment of the invention, a thermal fatigue cumulative damage model is constructed using thermal degradation data and historical thermal stress response data. Based on Miner's linear cumulative damage theory, multi-cycle thermal stress cycling is integrated to quantitatively assess the cumulative damage to the material. The model calculates the percentage of material life consumed and the fatigue crack propagation rate by inputting time-series inputs of thermal degradation degree changes and thermal stress peak values. Based on the damage evolution rate, the thermal fatigue growth trend curve is estimated to clarify the fatigue development stage of each structural unit. The output thermal fatigue growth status is expressed in the form of numerical indicators and spatial distribution maps, reflecting the temporal and spatial evolution of material degradation. This result is an important component in the comprehensive structural condition monitoring system, supporting structural safety assessment and maintenance strategy formulation.

[0062] Preferably, step S34 includes the following steps: Step S341: Determine the evolution characteristics of the panel degradation path based on the panel structure deterioration growth and panel thermal fatigue growth; In this embodiment of the invention, this step is based on data on the deterioration and growth of panel structure and data on panel thermal fatigue, performing a comprehensive spatial and temporal analysis of these two types of data. Spatial analysis employs spatial autocorrelation analysis from spatial statistics to quantitatively identify clusters of highly deteriorated areas in the data. Temporal analysis utilizes the sliding window method to extract the changing trends and acceleration points of the time series. By combining spatial hotspots with time series trends, a spatial-temporal coupled scenario of deterioration growth is formed. Subsequently, a trajectory clustering algorithm is used to aggregate the change trajectories of highly deteriorated areas in the data, extracting the main diffusion paths of deterioration and forming a set of spatially connected deterioration paths. The evolution characteristics of the paths include indicators such as the path's starting point, ending point, expansion speed, expansion direction, and path width. The deterioration path evolution characteristic data structure, quantified using statistical methods, includes a sequence of spatial coordinate points, corresponding timestamps, and path evolution rates, forming a continuous spatial-temporal deterioration evolution trajectory. This data lays the foundation for subsequent microcrack monitoring, location, and prediction, ensuring the spatial focus and temporal continuity of panel deterioration monitoring.

[0063] Step S342: Monitor panel microcrack propagation data based on panel degradation path evolution characteristics; In this embodiment of the invention, based on the degradation path evolution characteristics obtained in step S341, the key microcrack latent areas to be monitored on the panel are determined. An acoustic emission sensor array and an ultrasonic detection device are deployed, with sensor placement based on key nodes and connection areas along the degradation path to ensure high sensitivity and high spatial coverage. Acoustic emission signal acquisition includes high-frequency elastic waves generated by the microcracks. The signals are digitally filtered and subjected to time-frequency transformations (such as short-time Fourier transform and continuous wavelet transform) to extract characteristic parameters, including peak signal energy, dominant frequency components, and duration. Ultrasonic detection uses time-difference imaging to determine the spatial location of the crack by emitting pulses and receiving reflected waves. A multi-sensor fusion algorithm synchronously processes signals acquired by different sensors to improve crack location accuracy. Trend analysis is performed on the acquired crack activity signals to assess the crack propagation rate and direction, forming spatiotemporal distribution data of microcrack propagation signs. The data structure includes crack event number, occurrence time, spatial coordinates, propagation velocity, and crack length variation, providing dynamic monitoring data support for anomaly penetration prediction.

[0064] Step S343: Predict the abnormal penetration of microcracks in the panel based on the microcrack propagation indication data; In this embodiment of the invention, the microcrack propagation data from step S342 is used, combined with fracture mechanics principles, to predict abnormal crack penetration behavior. The stress intensity factor (K value) at the tip of each crack is calculated, and combined with the material's critical fracture toughness (K_IC), it is determined whether the crack is in a stable propagation state or tending towards penetration. Through numerical simulation software or finite element analysis, the mutual influence of multiple cracks within the panel is superimposed to identify the spatial regions where cracks connect. A crack merging model is constructed to simulate the path of multiple microcracks expanding and merging to form a through crack. Using a probabilistic statistical model, combined with historical crack propagation rates and accumulated material damage data, the probability and time prediction interval of abnormal penetration events are calculated. The output includes an abnormal penetration risk distribution map, risk level classification, and the changing trend of abnormal penetration within a future time period, assisting structural safety management departments in developing maintenance plans in advance to prevent sudden structural failures.

[0065] Step S344: Based on the abnormal penetration of microcracks in the panel and the data on the propagation of microcracks in the panel, perform a panel structural integrity weakening analysis to obtain panel structural integrity weakening data; In this embodiment of the invention, information on the abnormal penetration and propagation of microcracks is integrated, and a finite element method combining fracture mechanics and damage mechanics is used to perform local nonlinear mechanical simulation of the panel structure. The size, location, and penetration state of the crack are used as input structural defects to simulate their impact on the overall stiffness and load-bearing capacity of the structure. The stress concentration factor at the crack tip and the disturbance of the overall stress field by crack propagation are calculated to quantify the degree of weakening of the structure's load-bearing capacity. Based on the simulation results, the decrease in local safety factor, strain concentration areas, and the impact of cracks on the structure's fatigue life are obtained. A dynamic evolution map of structural integrity weakening is plotted using multi-time monitoring data. The output integrity weakening data specifically includes the spatial distribution of the structural safety factor, the percentage change in local load-bearing capacity, and the coordinates of the crack influence range, providing input conditions for subsequent internal stress redistribution analysis.

[0066] Step S345: Based on the panel structure integrity weakening data, predict the stress redistribution of the internal structure of the panel to assess the deterioration growth of the panel structure. In this embodiment of the invention, after inputting integrity-weakening data, a finite element model of a panel containing cracks is established using static structural mechanics analysis. For local areas with reduced load-bearing capacity, the changes in load-bearing capacity are mapped to the nodal stiffness matrix, and the overall structural stiffness matrix is ​​corrected. An iterative solution algorithm is used to calculate the nodal stress distribution under stress conditions. The stress concentration and stress migration path around cracks and in deteriorated areas are analyzed in detail. The stress fields before and after structural integrity weakening are compared to identify newly generated stress concentration areas and their spatial expansion trends. The stress values ​​and stress gradient changes of key nodes after stress redistribution are calculated, and the internal structural stress redistribution data, including node numbers, spatial coordinates, and corresponding stress values, are output, providing a mechanical basis for estimating chain-like degradation paths.

[0067] Step S346: Estimate the degradation path of the spatial chain connection of the panel based on the stress redistribution of the internal structure of the panel. In this embodiment of the invention, by analyzing stress redistribution data, a graph theory-based diagram of the panel structure's nodes and connections is established, with nodes as vertices and connections between stress concentration regions as edges. The shortest path algorithm is applied to identify the most frequent paths of stress migration, and the maximum flow algorithm is used to determine degradation propagation channels. Combining material fatigue theory, path nodes where stress concentration exceeds the fatigue limit are selected, forming potential degradation chains. Spatial coordinate mapping is performed on these paths to generate a spatial chain-like degradation path map of the panel structure. Each node on the path includes its stress value, estimated remaining fatigue life, and node connection strength. The overall path represents a spatial network structure where structural degradation spreads from local deterioration to the entire structure. This path provides a spatial framework and key node information for predicting degradation trends.

[0068] Step S347: Estimate the chain degradation trend of the panel structure based on the degradation path of the panel space chain connection.

[0069] In this embodiment of the invention, based on the spatial chain-like degradation path in step S346 and combined with historical degradation data, time series prediction techniques (such as autoregressive moving average models and exponential smoothing) are applied to analyze the degradation state changes of each node on the degradation path. A degradation expansion dynamics model is established by combining the connection strength between nodes and the path expansion rate to simulate the spatial propagation characteristics during the degradation process. The expansion range, speed, and state evolution of key nodes of the degradation path are predicted in future time periods. The output includes a spatial distribution map of the degradation trend, degradation time curves of key nodes, and a predicted range for future degradation, quantitatively reflecting the dynamic changes of the structural chain-like degradation. This data provides a basis for adjusting maintenance strategies and issuing safety warnings during operation, enabling dynamic safety management throughout the entire life cycle of the structure.

[0070] Preferably, step S4 includes the following steps: Step S41: Monitor the stability degradation of the panel structure based on the chain degradation trend of the panel structure; In this embodiment of the invention, based on the degradation path and trend data of the panel space chain connection, stress, strain, and vibration response data collected by the structural health monitoring system are used to detect the structural stability status of the panel through frequency domain and time domain analysis methods. The stress concentration values ​​of key nodes on the degradation path are compared with their historical baseline data to identify the magnitude of stress increase and its spatial distribution changes. Secondly, structural modal parameter identification technology is employed to determine the structural stiffness decay by measuring the panel's natural frequency, damping ratio, and mode shape changes. Vibration signals are used to extract feature parameters through Fast Fourier Transform (FFT) and continuous wavelet transform to reflect structural stability. By combining stress changes and the decay trend of modal parameters, a structural stability decay index is calculated, which quantifies the degree of degradation of the structure's safety boundary. The output structural stability decay data includes the percentage of local stiffness loss, vibration modal change curves, and the time series of the structural stability decay index, providing a basis for subsequent deformation monitoring.

[0071] Step S42: Monitor the deformation of the panel structure based on the stability decay of the panel composite under stress. In this embodiment of the invention, the structural stability decay data obtained in step S41 is used as input, combined with real-time panel deformation data collected by multi-point displacement sensors, to assess the instability state under combined stress. Strain gauges and laser displacement sensors are used to collect minute displacement and deformation data of key stress-bearing parts of the panel. Multi-sensor data fusion technology is employed to spatially correct and temporally synchronize data from different sensors, obtaining a high-precision deformation field distribution. A surface fitting algorithm is used to reconstruct the deformation data in three dimensions, obtaining the actual deformed surface of the panel. Combined with structural stability decay indices, the abnormal growth trend and instability modes of the deformation data are analyzed. The focus is on identifying composite deformation forms such as bending, torsion, and warping of the panel surface, calculating the maximum deformation value and its spatial distribution. The output shows the panel's operational deformation status, including a deformation amplitude distribution map, deformation growth rate, and deformation mode classification information, for subsequent crack detection.

[0072] Step S43: Detect the cracking status of panel joints based on the panel structure deformation and stability decay status to obtain the cracking status of panel joints. In this embodiment of the invention, the deformation field data from step S42 and the stability decay index from step S41 are used in conjunction with microcrack sensors (such as resistance strain gauges or fiber optic sensors) placed at the panel joints to detect cracking conditions. The sensors monitor strain changes in the joint area in real time, particularly stress concentration zones in high deformation regions. Through signal amplification and filtering, environmental noise interference is eliminated to obtain the strain abrupt change signal when a microcrack occurs at the joint. The strain signal is coupled with the structural stability decay trend for analysis to determine whether the strain abrupt change corresponds to a joint cracking event. The degree of cracking is determined using fracture mechanics critical thresholds and classified into initial cracks, propagating cracks, and through cracks. The output joint cracking condition data includes crack location coordinates, crack length, crack width changes, and cracking time series, forming a joint cracking dynamic monitoring report.

[0073] Step S44: Based on the panel joint cracking status and panel structure deformation status, evaluate the panel design defects of the panel design data to obtain panel design defect data; In this embodiment of the invention, the joint cracking data from step S43 and the operational deformation data from step S42 are used as the basis for comparative analysis between the actual operating state and the initial design parameters. The design data includes joint structural dimensions, material performance parameters, maximum allowable deformation, and stress limits. A difference analysis method is used to calculate the deviation between the actual crack distribution and the design expectations, with particular attention paid to areas of insufficient joint strength and excessive deformation. Through parameter regression and deviation clustering analysis, structural weaknesses and areas of abnormal stress concentration in the design are identified. The evaluation results are stored in the form of defect category, location, and severity, forming design defect data. The specific content of the design defect data includes defect type (such as insufficient joint size or substandard material strength), defect spatial distribution, corresponding crack characteristics, and statistical information on excessive deformation, providing data support for design optimization.

[0074] Step S45: Optimize the panel design based on the panel design defect data to obtain panel design optimization data.

[0075] In this embodiment of the invention, based on the design defect data from step S44, specific design optimization schemes are proposed for areas and types of concentrated defects. During the optimization process, engineering structural optimization theory is utilized to improve design strength and stability through parameter adjustments and structural reinforcement measures. For areas with insufficient joint dimensions, schemes to increase joint width or strengthen material thickness are developed; for areas with insufficient material performance, higher-strength materials or the addition of local reinforcing ribs are recommended. The optimized design data is presented in the form of a structural parameter modification list, including the spatial coordinates of the modified parts, parameter adjustment values, and expected improvement effects. The design optimization data includes joint dimension adjustment schemes, material replacement suggestions, local structural reinforcement arrangements, and allowable deformation and stress limit values ​​after design parameter correction. This data provides clear guidance for subsequent structural design implementation and manufacturing, ensuring that the panel structure has higher stability and durability during operation.

[0076] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for analyzing dynamic monitoring data of panel deformation during operation, characterized in that, Includes the following steps: Step S1: Obtain the design data of the concrete panel of the rockfill dam; collect the construction material data of the panel based on the design data of the concrete panel of the rockfill dam; construct a three-dimensional simulation model of the panel based on the design data of the concrete panel of the rockfill dam and the construction material data of the panel; Step S2: Determine the stress state of the panel structure based on the three-dimensional simulation model of the panel; detect the heterogeneous settlement of the panel foundation based on the stress state of the panel structure; determine the composite stress instability state of the panel based on the heterogeneous settlement of the panel foundation and the stress state of the panel structure. Step S3: Obtain data on changes in the surrounding environment of the panel; estimate the thermal fatigue growth of the panel based on the data on changes in the surrounding environment of the panel using the 3D simulation model of the panel, and obtain the thermal fatigue growth status of the panel; estimate the chain degradation trend of the panel structure based on the composite stress instability state of the panel and the thermal fatigue growth status of the panel. Step S4: Monitor the deformation of the panel structure based on the chain degradation trend of the panel structure according to the unstable state of the panel composite under stress; Panel design defects are assessed based on panel structural deformation data to obtain panel design defect data. Panel design optimization data is obtained by optimizing the panel design based on the panel design defect data.

2. The method for analyzing dynamic monitoring data of panel deformation during operation according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Obtain the design data for the concrete panel of the rockfill dam; Step S12: Obtain the panel geometry based on the design data of the concrete panel of the rockfill dam, thereby obtaining the panel geometry data; Step S13: Collect the material data for the concrete panel construction based on the design data of the rockfill dam concrete panel to obtain the material data for the panel construction. Step S14: Construct a three-dimensional simulation model of the panel based on the panel construction material data and panel geometric structure data to obtain the panel three-dimensional simulation model.

3. The method for analyzing dynamic monitoring data of panel deformation during operation according to claim 2, characterized in that, Step S14 includes the following steps: Step S141: Determine the concrete section of the panel based on the panel geometry data to obtain the panel concrete section data; Step S142: Monitor the foundation strength of the panel based on the data of the panel concrete section; Step S143: Evaluate the compressive strength of the panel construction material based on the panel construction material data; Step S144: Determine the load-bearing capacity information of the panel structure based on the compressive strength of the panel construction material, the strength of the panel foundation, and the data of the panel concrete section; Step S145: Determine the outer contour boundary line data of the panel based on the panel geometry data; Step S146: Determine the panel seam location information based on the panel's outer contour boundary line data and panel geometric structure data; Step S147: Construct a three-dimensional rectangular coordinate system for the panel using the panel seam location information and the panel outer contour boundary line data; Step S148: Construct a three-dimensional simulation model of the panel based on the panel's three-dimensional rectangular coordinate system and the panel's structural load-bearing strength information to obtain the panel's three-dimensional simulation model.

4. The method for analyzing dynamic monitoring data of panel deformation during operation according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Determine the stress state of the panel structure based on the three-dimensional simulation model of the panel; Step S22: Detect the heterogeneous settlement of the panel foundation based on the stress condition of the panel structure; Step S23: Estimate the panel tilt risk trend based on the heterogeneous settlement of the panel foundation to obtain the panel tilt risk trend; Step S24: Determine the composite stress instability state of the panel based on the panel tilt risk trend and the stress condition of the panel structure.

5. The method for analyzing dynamic monitoring data of panel deformation during operation according to claim 4, characterized in that, Step S22 includes the following steps: Step S221: Detect the over-limit condition of the foundation bearing capacity based on the stress condition of the panel structure, and obtain the over-limit condition of the foundation bearing capacity; Step S222: Analyze the foundation stress distribution based on the excessive bearing capacity of the foundation to obtain foundation stress distribution data; Step S223: Determine the local stress concentration trend of the foundation based on the foundation stress distribution data; Step S224: Perform foundation time-stress fatigue analysis based on the local stress concentration trend of the foundation to obtain foundation time-stress fatigue data; Step S225: Determine the looseness of the foundation soil particle structure based on the foundation time-stress fatigue data; Step S226: Detect the degree of shear strength attenuation of the foundation based on the looseness of the soil particle structure and the foundation time-stress fatigue data; Step S227: Detect the heterogeneous settlement of the panel foundation based on the degree of shear strength attenuation.

6. The method for analyzing dynamic monitoring data of panel deformation during operation according to claim 4, characterized in that, Step S24 includes the following steps: Step S241: Extract panel tilt angle evolution information based on panel tilt risk trend; Step S242: Based on the evolution information of the panel tilt angle, identify the center of gravity offset trajectory of the panel to obtain the center of gravity offset trajectory data of the panel; Step S243: Detect the asymmetry of the bottom force based on the panel's center of gravity offset trajectory data to obtain the asymmetry data of the bottom force of the panel; Step S244: Detect the tensile stress growth of the panel-foundation based on the asymmetric stress data at the bottom of the panel; Step S245: Assess the risk of contact failure at the connection interface based on the tensile stress growth of the panel-foundation; Step S246: Identify local overload areas of the panel based on the asymmetric stress data at the bottom of the panel and the stress condition of the panel structure; Step S247: Determine the composite stress instability state of the panel based on the local overload area of ​​the panel and the risk of contact failure at the connection interface.

7. The method for analyzing dynamic monitoring data of panel deformation during operation according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Obtain data on changes in the surrounding environment of the panel; Step S32: Based on the three-dimensional simulation model of the panel, estimate the thermal fatigue growth of the panel by analyzing the data on changes in the surrounding environment of the panel, and obtain the thermal fatigue growth status of the panel. Step S33: Determine the structural deterioration of the panel based on the composite stress instability and thermal fatigue growth of the panel. Step S34: Estimate the chain degradation trend of the panel structure based on the panel structure deterioration growth and panel thermal fatigue growth.

8. The method for analyzing dynamic monitoring data of panel deformation during operation according to claim 7, characterized in that, Step S32 includes the following steps: Step S321: Perform environmental change time series analysis based on the changes in the surrounding environment of the panel to obtain the surrounding environment change time series data; Step S322: Analyze the fluctuations in solar radiation intensity based on the time series data of changes in the surrounding environment to obtain data on the fluctuations in solar radiation intensity; Step S323: Based on the three-dimensional simulation model of the panel, detect the thermal stress response of the panel by analyzing the fluctuation data of solar radiation intensity, and obtain the thermal stress response status of the panel. Step S324: Monitor the abnormal thermal expansion of the panel based on the panel's thermal stress response; Step S325: Detect the thermal degradation of the panel material based on the abnormal thermal expansion and thermal stress response of the panel; Step S326: Estimate the thermal fatigue growth of the panel based on the thermal deterioration of the panel material to obtain the thermal fatigue growth status of the panel.

9. The method for analyzing dynamic monitoring data of panel deformation during operation according to claim 7, characterized in that, Step S34 includes the following steps: Step S341: Determine the evolution characteristics of the panel degradation path based on the panel structure deterioration growth and panel thermal fatigue growth; Step S342: Monitor panel microcrack propagation data based on panel degradation path evolution characteristics; Step S343: Predict the abnormal penetration of microcracks in the panel based on the microcrack propagation indication data; Step S344: Based on the abnormal penetration of microcracks in the panel and the data on the propagation of microcracks in the panel, perform a panel structural integrity weakening analysis to obtain panel structural integrity weakening data; Step S345: Based on the panel structure integrity weakening data, predict the stress redistribution of the internal structure of the panel to assess the deterioration growth of the panel structure. Step S346: Estimate the degradation path of the spatial chain connection of the panel based on the stress redistribution of the internal structure of the panel. Step S347: Estimate the chain degradation trend of the panel structure based on the degradation path of the panel space chain connection.

10. The method for analyzing dynamic monitoring data of panel deformation during operation according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Monitor the stability degradation of the panel structure based on the chain degradation trend of the panel structure; Step S42: Monitor the deformation of the panel structure based on the stability decay of the panel composite under stress. Step S43: Detect the cracking status of panel joints based on the panel structure deformation and stability decay status to obtain the cracking status of panel joints. Step S44: Based on the panel joint cracking status and panel structure deformation status, evaluate the panel design defects of the panel design data to obtain panel design defect data; Step S45: Optimize the panel design based on the panel design defect data to obtain panel design optimization data.

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