A method, system, medium and product for monitoring structural safety of a super high-rise building construction process

Abstract

The application provides a kind of super high-rise building construction process structure safety monitoring method, system, medium and product, related to structural safety technical field.Utilize construction scheme to cut continuous monitoring data into silence period and active period in time domain, so that the calculation of subsequent thermal sensitivity coefficient matrix can be based on the pure temperature response environment without load disturbance, thereby ensuring the purity of the coefficient matrix.Then, pure coefficient is used to clean the active period data, and the temperature noise is stripped, and the third data segment that can truly reflect the stress state of the structure is produced.Further, the third data segment is used as a target constraint to force the finite element model to adjust its internal stiffness parameters to match the deformation, thereby inversely deducing the real stiffness matrix of the structure.Finally, the real stiffness matrix is combined with the cumulative stress algorithm, so that the output cumulative stress field not only corrects the model deviation, but also contains the historical stress memory, realizing the reconstruction of the global real stress.

Description

Technical Field

[0001] This application relates to the field of structural safety technology, and in particular to a method, system, medium and product for monitoring the structural safety of super high-rise buildings during construction. Background Technology

[0002] As landmark projects in modern cities, supertall buildings are characterized by long construction periods, complex structural system transformations, variable loads, and the evolution of material properties over time. During the long construction period, the structure not only bears increasing self-weight and construction loads but is also constantly subjected to drastic changes in the ambient temperature field. To ensure structural safety throughout the construction process, it is essential to conduct real-time monitoring and assessment of the structure's deformation and internal stress state throughout its entire lifecycle, enabling timely detection and early warning of potential structural instability risks.

[0003] The commonly used method for monitoring the structural safety of super high-rise buildings is "sensor monitoring + model correction." This involves deploying temperature and displacement sensors at key structural locations to acquire real-time response data. To eliminate the interference of temperature changes on deformation monitoring, existing solutions typically rely on long-term historical monitoring data and employ statistical methods such as least squares to establish an empirical regression formula between temperature changes and structural deformation. During monitoring, this empirical formula is used to estimate the thermal deformation component at the current temperature, and this component is subtracted from the total measured deformation to attempt to extract the mechanical deformation caused solely by the load. Subsequently, the extracted mechanical deformation is used as the target vector to inversely correct parameters such as the elastic modulus of the structural finite element model. The corrected model is then used to calculate the current stress of the structure and compared with design thresholds to determine safety.

[0004] However, in actual construction, the structure is constantly under the dual coupling of "construction load" and "ambient temperature," which can contaminate the thermal sensitivity law. This causes stress increments to accumulate continuously throughout the construction cycle, ultimately leading to a distortion of the calculated cumulative stress field and failing to provide a truly reliable basis for construction safety decisions. Summary of the Invention

[0005] This application provides a method, system, medium, and product for monitoring the structural safety of super high-rise buildings during construction, which can improve the accuracy of the cumulative stress field.

[0006] In a first aspect, this application provides a method for monitoring the structural safety of a super high-rise building during construction, comprising: collecting temperature field distribution data and deformation data from temperature sensors and displacement sensors inside and outside the building; generating a multi-source heterogeneous monitoring dataset by unifying the time stamp and three-dimensional spatial coordinates of the temperature field distribution data and deformation data; selecting a first data segment of the quiet period and a second data segment of the active period from the multi-source heterogeneous monitoring dataset based on the acquired construction plan and time stamp, wherein the quiet period is the non-construction time of the construction plan; the active period is the construction time of the construction plan; determining the thermal response coefficient matrix by utilizing the correspondence between temperature fluctuation data and deformation fluctuation data in the first data segment; substituting the temperature data of the second data segment into the thermal response coefficient matrix to determine the theoretical thermal deformation component; correcting the second data segment using the theoretical thermal deformation component to obtain a third data segment; correcting the finite element model using the third data segment as the convergence target and combining it with the theoretical boundary conditions corresponding to the construction plan to generate a stiffness matrix; determining the cumulative stress field of the super high-rise building based on the stiffness matrix and historical cumulative stress data; and outputting a risk command when the stress value of a node in the cumulative stress field is greater than a preset safety yield threshold.

[0007] By adopting the above technical solution, the continuous monitoring data is divided into quiet and active periods in the time domain using the construction plan. This allows the subsequent calculation of the thermal coefficient matrix to be based on a pure temperature response environment without load interference, thus ensuring the purity of the coefficient matrix. Next, the active period data is cleaned using the purity coefficient to remove temperature noise, producing a third data segment that accurately reflects the structural stress state. Furthermore, this third data segment is used as a target constraint, forcing the finite element model to adjust its internal stiffness parameters to match deformation, thereby deriving the true stiffness matrix of the structure. Finally, this true stiffness matrix is ​​combined with the cumulative stress algorithm, ensuring that the output cumulative stress field corrects model biases and incorporates historical stress memory, achieving the reconstruction of the true stress across the entire domain.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, the steps of collecting temperature field distribution data and deformation data based on temperature sensors and displacement sensors inside and outside the building specifically include: determining illumination angle information based on timestamps; calculating the coordinate set of light and shadow shading areas based on the illumination angle information and the location of obstructions in the construction plan; dividing the temperature sensors into disturbed sensors falling within the coordinate set of light and shadow shading areas and reference sensors falling outside the set; and using the temperature data of the reference sensors to calculate the equivalent structural temperature at the location of the disturbed sensors to obtain temperature field distribution data.

[0009] By employing the aforementioned technical solution, a dynamic coordinate set covering the building surface is generated through geometric calculations of the illumination angle and the position of the obstruction. Next, the sensor coordinates are compared with this set, and at the data source, the disturbed sensors distorted by obstruction are identified through physical spatial relationships, preventing erroneous data from entering subsequent processes. Furthermore, using reference sensor data from the same building but under direct sunlight, equivalent calculations are used to fill the data gaps in the disturbed area, eliminating the temperature field mapping holes caused by localized dynamic shadows.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, the step of modifying the finite element model and generating a stiffness matrix by taking the third data segment as the convergence target and combining the theoretical boundary conditions corresponding to the construction scheme, specifically includes: dividing the components in the finite element model into several stiffness homogeneous groups according to the concrete pouring batch records in the construction scheme; calculating the theoretical stiffness benchmark value and the correction allowable threshold interval for each stiffness homogeneous group at the timestamp; wherein, the age is negatively correlated with the correction allowable threshold interval; taking the third data segment as the target, under the constraint of the correction allowable threshold interval, adjusting the unified correction coefficient of each stiffness homogeneous group using a weighted iterative algorithm, and updating the material parameters of the components in each stiffness homogeneous group based on the adjusted unified correction coefficient to generate a stiffness matrix.

[0011] By employing the aforementioned technical solution, clustering massive amounts of components using construction batch records reduces the dimensionality of the inversion problem from a high-dimensional space to a low-dimensional group space. Next, a correction threshold negatively correlated with age is introduced. This constraint feature constructs a dynamic "constraint boundary" during the iteration process, limiting the algorithm's ability to modify parameters in older, stable regions. Furthermore, driven by the weighted iterative algorithm, deformation residuals are guided to newly cast, younger regions not locked by the "constraint boundary" for processing. This eliminates the mathematical ambiguity of identical parameters and outputs a stiffness parameter distribution that conforms to engineering logic.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, before determining the cumulative stress field of a super high-rise building based on the stiffness matrix and historical cumulative stress data, the method further includes: calculating the theoretical total self-weight of the super high-rise building at the corresponding timestamp based on the material weight data of the construction plan; selecting the horizontal section at the bottom of the building as the verification section, and calculating the sum of the measured vertical reactions of all components on the section based on the historical cumulative stress data; calculating the ratio of the theoretical total self-weight to the sum of the measured vertical reactions to determine the global drift correction coefficient; and using the global drift correction coefficient to normalize and correct the historical cumulative stress data.

[0013] By employing the aforementioned technical solution, the theoretical total self-weight based on the law of conservation of matter was calculated, establishing a physical truth scale. Next, by integrating the measured reaction force at the bottom cross-section, current data including accumulated errors was obtained. Then, by comparing these two sets of data, a drift coefficient was calculated, quantitatively capturing the degree of deviation relative to the absolute scale. Finally, the coefficient was used to normalize and correct historical data, cutting off the path of positive feedback amplification of errors over time and avoiding divergence problems caused by benchmark drift.

[0014] In some embodiments of the first aspect, the step of modifying the finite element model and generating the stiffness matrix by taking the third data segment as the convergence target and combining the theoretical boundary conditions corresponding to the construction scheme is specifically included as follows: using the theoretical boundary conditions of the active period in the construction scheme to construct a finite element reference model containing boundary conditions; applying the third data segment as a displacement constraint condition to the finite element reference model; and calculating the structural material parameters based on the principle of minimum potential energy to obtain the stiffness matrix.

[0015] By adopting the above technical solution, a benchmark model incorporating theoretical loads during the active phase is first constructed, establishing the mechanical framework for physical simulation. Next, a third data segment representing purely mechanical properties is applied to the model, enabling it to simultaneously possess both theoretical force and measured shape as boundary conditions. Furthermore, based on the principle of minimum potential energy, the algorithm automatically adjusts material parameters to reconcile the contradiction between force and shape while searching for the energy minimum, thus improving the accuracy of the stiffness matrix.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, the step of determining the cumulative stress field of a super high-rise building based on the stiffness matrix and historical cumulative stress data specifically includes: performing matrix operations using the stiffness matrix and theoretical boundary conditions to calculate the elastic stress increment under the current working condition; and superimposing the elastic stress increment onto the pre-stored historical cumulative stress data to generate the cumulative stress field.

[0017] By employing the aforementioned technical solution, forward mechanical calculations are performed using the corrected stiffness matrix combined with theoretical boundary conditions. Since the stiffness matrix has been corrected, the calculation process simulates the response of a real structure under the current load, thereby calculating the elastic stress increment. Furthermore, the increment is superimposed onto historical data, connecting discrete instantaneous conditions into a continuous stress history. This allows for the output of a stress field encompassing the cumulative effects from the foundation to the roof, providing a complete historical quantitative basis for determining whether the structure is approaching its yield limit.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, before the step of determining the cumulative stress field of a super high-rise building based on the stiffness matrix and historical cumulative stress data, the method further includes: performing time-varying correction on the historical cumulative stress data by incorporating a material attenuation function.

[0019] By employing the aforementioned technical solution and introducing a material attenuation function onto historical data, the physical process of concrete creep relaxation is simulated. This means that over time, old stresses are lost due to concrete creep and relaxation characteristics. This time-varying correction process eliminates the artificially high calculated values ​​caused by neglecting the relaxation effect, making the monitoring results closer to the true internal tension of the material and improving the confidence level of the safety assessment.

[0020] Secondly, this application provides a structural safety monitoring system for the construction process of a super high-rise building. The structural safety monitoring system for the construction process of a super high-rise building includes: one or more processors and a memory; the memory is coupled to one or more processors, and the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the structural safety monitoring system for the construction process of a super high-rise building to perform the method described in the first aspect and any possible implementation of the first aspect.

[0021] Thirdly, this application provides a computer program product containing instructions that, when run on a structural safety monitoring system for the construction process of a super high-rise building, cause the structural safety monitoring system for the construction process of a super high-rise building to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, this application provides a computer-readable storage medium including instructions that, when executed on a super high-rise building construction process structural safety monitoring system, cause the super high-rise building construction process structural safety monitoring system to perform the method described in the first aspect and any possible implementation thereof.

[0023] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0024] 1. By utilizing the construction plan, continuous monitoring data is divided into quiet and active periods in the time domain. This allows the subsequent calculation of the thermal coefficient matrix to be based on a pure temperature response environment without load interference, thus ensuring the purity of the coefficient matrix. Next, the active period data is cleaned using the purity coefficient to remove temperature noise, producing a third data segment that accurately reflects the structural stress state. Furthermore, this third data segment is used as a target constraint, forcing the finite element model to adjust its internal stiffness parameters to match deformation, thereby deriving the true stiffness matrix of the structure. Finally, this true stiffness matrix is ​​combined with a cumulative stress algorithm, ensuring that the output cumulative stress field corrects model biases and incorporates historical stress memory, achieving a reconstruction of the true stress across the entire domain.

[0025] 2. Through geometric calculations of the lighting angle and the position of obstructions, a dynamic coordinate set covering the building surface was generated. Next, the sensor coordinates were compared with this set, and at the data source, the disturbed sensors distorted by obstruction were located through physical spatial relationships, preventing erroneous data from entering subsequent processes. Furthermore, using reference sensor data from the same building but under direct sunlight, equivalent calculations were used to fill the data gaps in the disturbed area, eliminating the temperature field mapping holes caused by local dynamic shadows.

[0026] 3. By clustering massive amounts of components using construction batch records, the inversion problem is reduced from a high-dimensional space to a low-dimensional group space. Next, a correction threshold negatively correlated with age is introduced. This constraint feature constructs a dynamic "constraint boundary" during the iteration process, limiting the algorithm's ability to modify parameters in older, stable regions. Furthermore, driven by a weighted iterative algorithm, deformation residuals are guided to newly cast, younger regions not locked by the "constraint boundary" for processing. This eliminates the mathematical ambiguity of identical parameters and outputs a stiffness parameter distribution that conforms to engineering logic. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating a method in an embodiment of this application;

[0028] Figure 2 This is another flowchart illustrating the method in the embodiments of this application;

[0029] Figure 3 This is another flowchart illustrating the method in the embodiments of this application;

[0030] Figure 4 This is another flowchart illustrating the method in the embodiments of this application;

[0031] Figure 5 This is an exemplary hardware structure diagram of a structural safety monitoring system for the construction process of super high-rise buildings in this application embodiment. Detailed Implementation

[0032] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to and includes any or all possible combinations of one or more of the listed items.

[0033] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0034] Please see Figure 1 , Figure 1 This is a flowchart illustrating a method in an embodiment of this application;

[0035] A method for monitoring the structural safety of super high-rise buildings during construction includes:

[0036] S101. Collect temperature field distribution data and deformation data based on temperature sensors and displacement sensors inside and outside the building.

[0037] Among them, temperature field distribution data is used to represent the three-dimensional temperature gradient state of the building structure as a whole at a specific moment; deformation data is used to represent the spatial position change of the building structure relative to the initial reference state.

[0038] It should be noted that, due to the massive size of the super high-rise building, the data collection process followed the principle of spatial discretization, collecting not only the surface temperature of areas significantly affected by sunlight, such as the outer frame columns and exterior walls, but also the internal temperature deep within the core tube and shear walls. The deformation data collection focused on the inter-story displacement, verticality deviation, and deflection of key load-bearing components such as outrigger trusses.

[0039] In actual use, due to the long-term exposure of sensors to the harsh environment of the construction site, some sensors may be damaged or disconnected, resulting in data loss and incomplete temperature field reconstruction.

[0040] Therefore, in some preferred embodiments, the coordinates of the failed node in the monitoring network are first identified; then, all normally functioning neighboring sensors within a certain radius of the failed node are selected as sample points; the variation of the sample point temperature value with spatial distance is analyzed using a variogram to determine an appropriate weighting coefficient; finally, the sample point data is weighted and summed to estimate the theoretical temperature value at the failure location and fill in the data gaps.

[0041] S102. Generate a multi-source heterogeneous monitoring dataset by unifying the timestamps and three-dimensional spatial coordinates of the temperature field distribution data and deformation data.

[0042] Among them, the timestamp refers to the absolute time tag generated by the high-precision time server that marks the moment of data acquisition; the three-dimensional spatial coordinates are used to represent the precise location of the data acquisition point in the building coordinate system, usually described by the X, Y, and Z axis values; the multi-source heterogeneous monitoring dataset refers to a big data collection that includes temperature scalar field, displacement vector field and corresponding spatiotemporal metadata.

[0043] Specifically, due to the different sampling frequencies and communication protocols of different types of sensors, the raw data stream is often asynchronous and unstructured. By establishing a unified data alignment engine, firstly, all incoming data packets are time-synchronized and calibrated. For asynchronously sampled data, linear time interpolation is performed to align it to a unified sampling time point. Secondly, the physical installation locations of all sensors are mapped to the global coordinate system of the building BIM model, assigning a unique spatial index to each data point. Finally, the aligned temperature values ​​and deformation vectors are structured and encapsulated according to the spatiotemporal index, forming a standardized dataset that is easy for subsequent algorithms to call.

[0044] S103. Based on the obtained construction plan and timestamp, select the first data segment of the quiet period and the second data segment of the active period from the multi-source heterogeneous monitoring dataset. The quiet period is the time during which the construction plan is not under construction; the active period is the time during which the construction plan is under construction.

[0045] Among them, the construction plan refers to the technical documents that include detailed work plans, mechanical equipment operation schedules and material hoisting arrangements; the quiescent period refers to a relatively stable period in which the building structure is mainly affected by ambient temperature and there are no significant changes in external loads; the active period refers to the dynamic period in which the building structure is subjected to the superimposed effects of construction loads (such as tower crane operation and concrete pouring); the first data segment is used to represent a pure sample set containing only temperature effect characteristics; the second data segment is used to represent a mixed sample set containing temperature and mechanical coupling effects.

[0046] Specifically, digitized construction logs are read from the Project Management System (PMS) to extract the daily work sequence. Timestamps are used to match the timeline of the monitoring dataset with the construction process timeline. Periods without heavy machinery operation, such as nighttime shutdowns, lunch breaks, or holidays, are marked as quiet periods, and data from these periods is extracted as baseline samples (first data segment) to train the decoupling algorithm. Peak daytime construction periods, periods of frequent tower crane raising and lowering, or changes in material load are marked as active periods, and data from these periods is extracted as target samples to be corrected (second data segment). The purpose of this step is to achieve logical isolation of physical working conditions at the data source.

[0047] S104. Using the correspondence between temperature fluctuation data and deformation fluctuation data in the first data segment, determine the thermal response coefficient matrix.

[0048] Among them, temperature fluctuation data refers to the sequence of temperature changes relative to a certain reference value during a quiet period; deformation fluctuation data refers to the sequence of structural displacement changes relative to the reference position during the same quiet period; correspondence refers to the physical causal relationship between temperature changes and structural deformation; the thermal response coefficient matrix is ​​a set of parameters describing the sensitivity of the structure to temperature changes at different locations, and the matrix elements characterize the amount of deformation caused by a unit temperature change.

[0049] Specifically, in the first extracted data segment, since external load interference has been excluded, structural deformation is mainly caused by thermal expansion and contraction. Time-domain analysis is performed on the temperature and deformation sequences within this time period. Considering the hysteresis of structural heat conduction, the algorithm analyzes not only the correspondence at the current moment but also the cross-correlation with time delays. Through multivariate regression analysis or machine learning fitting, a mapping model from input (overall temperature distribution) to output (key point displacement) is established. This model is parameterized as a matrix, where the elements quantify the contribution weight of temperature changes in different regions of the structure to the displacement of specific monitoring points.

[0050] In some embodiments, firstly, a regression equation D = H·T + C is constructed, where D is the deformation vector, T is the temperature vector, and H is the coefficient matrix to be determined; secondly, the least squares criterion is used to minimize the sum of squared residuals between the measured deformation and the predicted deformation at all sampling points; finally, the system of equations is solved to obtain the optimal estimated regression coefficient matrix H, which is the thermal response coefficient matrix.

[0051] Optionally, principal component analysis combined with partial least squares (PLS) can be used. First, PCA is performed on the high-dimensional temperature field data to reduce its dimensionality and extract the principal components with the largest explained variance as temperature features. Second, the PLS algorithm is used to establish a latent variable relationship model between the temperature principal components and the deformation data. Finally, the latent variable model is restored to the original physical coordinate space, and the thermal response coefficients of each physical measurement point are calculated.

[0052] S105. Substitute the temperature data of the second data segment into the thermosensitive response coefficient matrix to determine the theoretical thermal deformation component.

[0053] The temperature data in the second data segment refers to the structural temperature field values ​​collected in real time during the active construction period; the theoretical thermal deformation component is used to represent the virtual displacement that should occur under the current temperature field, assuming that the structure only undergoes linear elastic thermal expansion without external force.

[0054] Specifically, once the construction period begins, the current temperature field data is read in real time. This data is then organized into an input vector that matches the dimensions of the thermal response coefficient matrix. Matrix multiplication is then performed, multiplying the input temperature vector by the coefficient matrix determined in step S104. The result is the theoretical deformation of the structure due to temperature change at that moment. This calculation process is based on the superposition principle, assuming that the temperature effect and the load effect are decoupled.

[0055] S106. The third data segment is obtained by correcting the second data segment using the theoretical thermal deformation component.

[0056] Among them, the correction operation refers to the mathematical operation process of removing a specific component from the total deformation; the third data segment is used to represent the pure mechanical deformation data that reflects only the action of external force load and the evolution of structural stiffness after removing the influence of ambient temperature. It is a direct representation of the actual stress state of the structure.

[0057] Specifically, obtain the total deformation data measured during the active period (the deformation portion in the second data segment). Use the theoretical thermal deformation component calculated in step S105 as the noise term.

[0058] Optionally, the instantaneous difference subtraction method can be used. First, the measured deformation and theoretical thermal deformation are synchronously aligned at each sampling point. Second, point-to-point numerical subtraction is performed directly. Finally, the difference sequence is smoothed and output as the pure mechanical deformation value for each moment.

[0059] S107. Using the third data segment as the convergence target, the finite element model is modified in combination with the theoretical boundary conditions corresponding to the construction scheme to generate the stiffness matrix.

[0060] Among them, the convergence objective refers to the benchmark value that the optimization algorithm attempts to approximate; the theoretical boundary conditions refer to the constraint form of the structure at the current construction stage and the known loads that have been applied, as determined by the construction plan; the finite element model refers to the digital twin of the building structure built in a computer; and the stiffness matrix is ​​a mathematical model that describes the structure's ability to resist deformation under external forces, reflecting the elastic modulus, cross-sectional properties, and connection stiffness of the components.

[0061] Specifically, the current structural finite element baseline model is retrieved. The pure mechanical deformation (third data segment) obtained from S106 is taken as the target that the model should present. The current floor height, tower crane position, and surcharge weight recorded in the construction plan are taken as the "known conditions" applied to the model. The parameter identification and inversion algorithm is started, and the elastic modulus or nodal stiffness of each structural component is taken as "undetermined variables". The algorithm iteratively optimizes and adjusts these undetermined variables, recalculates the theoretical deformation of the model under the known conditions, and compares it with the "target". When the error between the two is less than the preset accuracy, it is considered that the current parameter combination truly reflects the entity state. At this time, the overall stiffness matrix of the assembled model is extracted as the output.

[0062] In some specific embodiments, step S107 specifically includes:

[0063] Step S1071: Using the theoretical boundary conditions of the active period in the construction plan, construct a finite element reference model containing the boundary conditions.

[0064] Among them, theoretical boundary conditions refer to the structural physical constraints and load distribution parameters derived from the construction plan, covering the component's self-weight, mechanical load, wind load, and support constraint types; the finite element reference model refers to the structural mechanics simulation model initialized in the computing environment with BIM design data and the current construction progress as the framework.

[0065] Specifically, the process begins by analyzing the construction plan database to pinpoint the construction progress nodes corresponding to the active periods, clarifying the current topology of the structural system, such as the specific floor height reached and the connection status of key components. Based on this, corresponding nodes and mesh elements are generated in the simulation software to reconstruct the geometric model at that moment. Next, various load indices recorded in the construction plan are read, mapping constant loads, live loads, and environmental factors to nodal forces or surface loads applied to the model mesh. Simultaneously, displacement constraint boundaries at the bottom of the model are set according to the foundation design drawings.

[0066] S1072. Apply the third data segment as a displacement constraint to the finite element reference model; calculate the structural material parameters based on the principle of minimum potential energy to obtain the stiffness matrix.

[0067] Among them, displacement constraint condition refers to the requirement that the geometric position of a specific measuring point of the model must be consistent with the measured pure mechanical deformation; minimum potential energy principle refers to the physical law that the total potential energy takes a minimum value when the elastic system is in stable equilibrium; structural material parameter refers to the elastic modulus of the component to be solved; stiffness matrix is ​​a set of parameters that characterize the mechanical properties of the corrected model.

[0068] Specifically, the decoupled pure mechanical deformation data (third data segment) is extracted. Corresponding monitoring nodes are located in the finite element baseline model, and the pure mechanical deformation values ​​are set as the forced displacement boundaries for these nodes. At this point, the model is simultaneously constrained by both theoretical external forces and measured displacements. The system constructs an optimization objective based on the principle of minimum potential energy, namely, finding a set of optimal material stiffness parameters such that the difference between the work done by external forces and the structural strain energy is minimized under this parameter combination. By solving this variational problem, the algorithm automatically corrects the stiffness properties of each component until the internal mechanical equilibrium state of the model matches the externally observed displacements. Finally, the overall stiffness matrix of the model assembled from this set of optimal parameters is output.

[0069] In some embodiments, a Lagrangian function containing state variables is constructed; secondly, the adjoint equation is solved to obtain the sensitivity of the objective function to the stiffness parameter; finally, the stiffness parameter is iteratively updated using the gradient descent algorithm until convergence.

[0070] In some embodiments, a linear system of equations for displacement and stiffness matrices is established; secondly, the known external force vectors and displacement vectors are combined; finally, the correction terms of the stiffness matrix are solved using singular value decomposition techniques.

[0071] As can be seen, a baseline model incorporating theoretical loads during the active phase is first constructed, establishing the mechanical framework for the physical simulation. Next, a third data segment representing purely mechanical properties is applied to the model, giving it both theoretical force and measured shape as boundary conditions. Furthermore, based on the principle of minimum potential energy, the algorithm automatically adjusts material parameters to reconcile the contradiction between force and shape while searching for the energy minimum, thus improving the accuracy of the stiffness matrix.

[0072] S108. Determine the cumulative stress field of super high-rise buildings based on stiffness matrix and historical cumulative stress data.

[0073] Among them, historical cumulative stress data refers to the stress superposition value generated by all construction steps from the start of foundation construction to the previous moment, which is stored in the database; the cumulative stress field is used to represent the absolute total stress distribution state of all points inside the structure at the current moment, which includes the comprehensive cumulative effect of all historical factors such as self-weight, construction live load, and prestress.

[0074] Specifically, using the corrected true stiffness matrix (S107) and the current load increment vector, the elastic stress increment generated by the current construction step is calculated using Hooke's Law. This increment reflects the immediate internal force changes caused by the recent construction activity. Subsequently, a snapshot of the cumulative stress field up to the previous moment is retrieved from the database. The currently calculated increment is then superimposed with the historical snapshots using a tensor. During this process, a creep model may be introduced to correct for temporal decay in the historical data. The resulting full-field data fills in the data gaps caused by sensor damage, range drift, or the absence of sensors during construction, achieving full-domain stress transparency.

[0075] In some specific embodiments, step S108 specifically includes:

[0076] S1081. Perform matrix operations using the stiffness matrix and theoretical boundary conditions to calculate the elastic stress increment under the current working condition.

[0077] Elastic stress increment refers to the change in internal stress of the structure caused by the new load during the current construction phase.

[0078] Specifically, the corrected stiffness matrix output from S1072 is extracted. The theoretical boundary conditions from S1071 are extracted, focusing on the new load vector for this construction step. Based on the finite element stiffness equations, the displacement increment caused by the new load is solved through matrix inversion or decomposition. Subsequently, for each element, the strain increment is derived using the geometric equations, i.e., the strain-displacement relationship matrix. Finally, the stress increment is calculated using the physical equations, i.e., the stress-strain relationship matrix containing the corrected elastic modulus.

[0079] S1082. The elastic stress increment is superimposed on the pre-stored historical cumulative stress data to generate a cumulative stress field.

[0080] The cumulative stress field refers to the absolute total stress state borne by each point of the structure at the current moment.

[0081] Specifically, the process involves accessing the stress database and retrieving the historical cumulative stress data archived from the previous construction step. The elastic stress increment for the current step, calculated using S1081, is then obtained. A tensor superposition operation is performed, adding the incremental data point by point to the historical data. This operation transforms transient construction effects into persistent mechanical memory, synthesizes a new cumulative stress field, and writes it back to the database.

[0082] As can be seen, forward mechanical calculations are performed using the corrected stiffness matrix combined with theoretical boundary conditions. Since the stiffness matrix has been corrected, the calculation process simulates the response of the real structure under the current load, thereby calculating the elastic stress increment. Furthermore, the increment is superimposed on historical data, connecting discrete instantaneous conditions into a continuous stress history, which can output a stress field that includes the cumulative effect from the foundation to the roof, providing a complete historical quantitative basis for judging whether the structure is approaching the yield limit.

[0083] In some embodiments, prior to step S108, the method further includes:

[0084] By incorporating the material attenuation function, time-varying corrections are applied to the historical cumulative stress data.

[0085] Among them, the material decay function is a mathematical model that describes the creep and relaxation characteristics of concrete, reflecting the nonlinear evolution of stress or strain over time.

[0086] Specifically, before performing stress superposition, the system preprocesses the historical cumulative stress data. The historical data is divided into several loading age components based on the time of generation. For each component, its holding time is calculated, and the stress relaxation loss due to creep effect is calculated by substituting it into the material decay function. This loss is subtracted from the historical cumulative value to obtain the current effective retained stress. This step simulates the viscoelastic flow phenomenon of concrete material over time, ensuring that the superposition operation is based on the current real material state, rather than rigid elastic historical values.

[0087] In some embodiments, the aging coefficient and creep coefficient of concrete are calculated; secondly, the retained stress after relaxation is directly calculated using algebraic relationships; and finally, the historical records are updated.

[0088] As can be seen, introducing a material attenuation function onto historical data simulates the physical process of concrete creep relaxation. That is, over time, old stresses are lost due to concrete creep and relaxation characteristics. This time-varying correction process eliminates the artificially high calculated values ​​caused by neglecting the relaxation effect, making the monitoring results closer to the true internal tension of the material and improving the confidence of the safety assessment.

[0089] S109. If the stress value of a node in the cumulative stress field is greater than the preset safe yield threshold, output a risk command.

[0090] Among them, the stress value of a node refers to the equivalent stress at the mesh node or Gaussian integration point of the finite element model; the preset safe yield threshold refers to the upper limit of allowable stress determined according to the structural design specifications and material strength grade; and the risk command refers to the digital signal generated by monitoring and used to trigger alarm devices or automated control equipment.

[0091] Specifically, the system scans the global cumulative stress field generated by S108 in real time. It iterates through the stress values ​​of each critical structural node and compares them with the pre-set safe yield thresholds for each component in the database. If the stress value at any point exceeds the warning line, a high-level interruption request is immediately triggered.

[0092] As can be seen, by segmenting the continuous monitoring data into quiet and active periods in the time domain using the construction scheme, the subsequent calculation of the thermal coefficient matrix can be based on a pure temperature response environment without load interference, thus ensuring the purity of the coefficient matrix. Next, the active period data is cleaned using the purity coefficient, removing temperature noise and producing a third data segment that truly reflects the structural stress state. Furthermore, this third data segment is used as a target constraint, forcing the finite element model to adjust its internal stiffness parameters to match deformation, thereby deriving the true stiffness matrix of the structure. Finally, this true stiffness matrix is ​​combined with the cumulative stress algorithm, ensuring that the output cumulative stress field not only corrects model biases but also incorporates historical stress memory, achieving the reconstruction of the true stress across the entire domain.

[0093] The above embodiments achieve the effect of improving the accuracy of the cumulative stress field. However, in actual scenarios, due to the obstruction of tower cranes, surrounding buildings, and cloud movement, the sensor readings in step S101 often exhibit the characteristic of "local uneven illumination," which cannot represent the true heating state of the regional structure. If the low-temperature readings of the obstructed sensors are directly used to interpret the deformation of the surrounding structures exposed to direct sunlight, it will lead to errors in the calculation of the thermal response coefficient, thereby causing deformation decoupling failure.

[0094] Please see Figure 2 , Figure 2 This is another flowchart illustrating the method in the embodiments of this application;

[0095] Therefore, in some embodiments, another implementation of step S101 is as follows:

[0096] S201. Determine the illumination angle information based on the timestamp.

[0097] Among them, the illumination angle information refers to the vector parameters describing the direction of sunlight incident, which are usually expressed as the solar altitude angle and the solar azimuth angle.

[0098] Specifically, the monitoring system reads the standard timestamp from the data packet and combines it with the geographical latitude and longitude coordinates of the project location. It then invokes an astronomical model for calculating the sun's position, using the periodicity of the Earth's rotation and revolution to calculate the sun's relative position in the celestial coordinate system at that specific moment. Subsequently, through coordinate transformation, the sun's position is mapped to a horizontal coordinate system with the building's base as the origin, thereby obtaining precise values ​​for the sun's altitude and azimuth angles. This process provides directional light source parameters for subsequent calculations of light and shadow occlusion.

[0099] S202. Calculate the coordinate set of the light and shadow occlusion area based on the illumination angle information and the location of the obstruction in the construction plan.

[0100] The location of the occlusion refers to the geometric boundaries of the tower crane, elevator, surrounding buildings, and structural protrusions in three-dimensional space; the set of coordinates of the light and shadow occlusion area is used to represent a list of discrete points on the building's exterior surface covered by projected shadows.

[0101] Specifically, a 3D geometric scene is constructed, including the main building and surrounding obstructions. The calculated position of the sun is used as the direction of parallel light source emission. A spatial projection geometry algorithm is used to simulate the process of light rays being projected onto the building surface along the incident direction. The intersection of the light path with the geometry of the obstruction is detected. If the light ray intersects with the obstruction before reaching the building surface, the point of impact of the light ray is determined to be in the shadow area. All mesh nodes on the building surface are traversed, and the coordinates of nodes determined to be in the shadow area are extracted and compiled into a set of coordinates for the light and shadow occlusion area.

[0102] S203. Divide the temperature sensors into disturbed sensors that fall within the coordinate set of the light and shadow occlusion area and reference sensors that fall outside the set.

[0103] Among them, a disturbed sensor refers to a sensor whose readings are distorted due to the influence of shadows; a reference sensor refers to a sensor whose readings are reliable under normal lighting conditions.

[0104] Specifically, it iterates through all three-dimensional coordinates of the temperature sensor array. For each sensor coordinate, a geometric inclusion test is performed to determine whether it falls within the range defined by the set of coordinates of the light and shadow occlusion region generated by S202. If the coordinate point is contained within the set, it is assigned a disturbed attribute label; if the coordinate point is outside the set, it is assigned a baseline attribute label.

[0105] S204. Using the temperature data from the reference sensor, calculate the equivalent structural temperature at the location of the disturbed sensor to obtain temperature field distribution data.

[0106] The equivalent structural temperature refers to the theoretical temperature value that reflects the deep thermal equilibrium state of the structure after eliminating the influence of local shading.

[0107] Specifically, the measured data from the disturbed sensors are temporarily masked, and only the data from the reference sensor is used as the valid sample. Based on the assumption of spatial continuity of the structural temperature field, a spatial interpolation algorithm is used to estimate the temperature distribution in the disturbed area. The thermal conductivity of concrete is considered during the calculation, and thermal resistance weighting is applied to spatial distances. Finally, the calculated equivalent temperature values ​​are filled into the locations of the disturbed sensors to generate continuous and uniform temperature field distribution data covering the entire building.

[0108] As can be seen, a dynamic coordinate set covering the building surface was generated through geometric calculations of the illumination angle and the position of the obstruction. Next, the sensor coordinates were compared with this set, and at the data source, the disturbed sensors distorted by obstruction were located through physical spatial relationships, preventing erroneous data from entering subsequent processes. Furthermore, using reference sensor data from the same building but under direct sunlight, equivalent calculations were used to fill the data gaps in the disturbed area, eliminating the temperature field mapping holes caused by local dynamic shadows.

[0109] The above embodiments achieve the effect of improving the accuracy of the cumulative stress field. However, in step S107, since the structural degrees of freedom are far greater than the number of measuring points, and the difference in concrete age at different floors leads to non-uniform stiffness distribution, relying solely on deformation data constraints for inversion often faces the dilemma of mathematical ambiguity (for example, both column stiffness degradation and beam stiffness degradation can lead to the same lateral displacement). Without physical constraints, the algorithm may calculate incorrect parameter combinations that meet deformation conditions but violate common sense in engineering, resulting in an inability to truly reflect the actual physical properties of various parts of the structure.

[0110] Please see Figure 3 , Figure 3 This is another flowchart illustrating the method in the embodiments of this application;

[0111] Therefore, another way to implement step S107 is:

[0112] S301. Based on the concrete pouring batch records in the construction plan, divide the components in the finite element model into several stiffness homogeneous groups.

[0113] Among them, the stiffness homogeneous group refers to a set of components that have consistent physical properties and construction history; the pouring batch record refers to a log that records the construction time and material information.

[0114] Specifically, the construction log data is analyzed to extract the timestamp, location range, and material designation for each concrete pouring operation. Element objects corresponding to these spatial ranges are then retrieved from the finite element model. Elements with the same pouring time and material designation are grouped together and assigned a unique group identifier. It is assumed that components constructed in the same batch have similar material property deviations. This step reduces a massive number of independent component parameters to a small number of group parameters.

[0115] S302. Calculate the theoretical stiffness benchmark value and the correction allowable threshold range for each stiffness homogeneous group at the corresponding timestamp; where age is negatively correlated with the correction allowable threshold range.

[0116] Among them, the theoretical stiffness benchmark value refers to the expected elastic modulus calculated according to the specifications; the correction allowable threshold range refers to the range of allowable fluctuations of the inversion parameters; and the age refers to the time since the component was poured.

[0117] Specifically, for each group, the age is determined by calculating the difference between the current time and the pouring time. Based on the concrete hardening curve model, the theoretical elastic modulus at that age is calculated. A range width function that decays with age is constructed, so that groups with shorter ages correspond to wider correction ranges, and groups with longer ages correspond to narrower correction ranges.

[0118] S303. Taking the third data segment as the target, under the constraint of the correction allowable threshold range, the weighted iterative algorithm is used to adjust the unified correction coefficient of each stiffness homogeneous group, and the material parameters of the components in each stiffness homogeneous group are updated based on the adjusted unified correction coefficient to generate the stiffness matrix.

[0119] Among them, the unified correction coefficient refers to the adjustment ratio of the overall stiffness of the group relative to the benchmark value; the weighted iterative algorithm refers to the optimization solution process that takes into account the sensitivity weights of different groups.

[0120] Specifically, an optimization objective is constructed that minimizes the residual between model deformation and pure mechanical deformation. The threshold interval determined in S302 is added to the optimization model as a hard constraint. During the iterative optimization process, an age-weighted factor is introduced, giving higher gradient weights to younger age groups to guide the algorithm to prioritize adjusting parameters in newly constructed areas. After iterative convergence, the obtained optimal correction coefficients are applied to all components in each group to update their material properties, and the final overall structural stiffness matrix is ​​generated.

[0121] As can be seen, clustering massive amounts of components using construction batch records reduces the dimensionality of the inversion problem from a high-dimensional space to a low-dimensional group space. Next, a correction threshold negatively correlated with age is introduced. This constraint feature constructs a dynamic "constraint boundary" during the iteration process, limiting the algorithm's ability to modify parameters in older, stable regions. Furthermore, driven by the weighted iterative algorithm, deformation residuals are guided to newly cast, younger regions not locked by the "constraint boundary" for digestion. This eliminates the mathematical ambiguity of identical parameters and outputs a stiffness parameter distribution that conforms to engineering logic.

[0122] The above embodiments achieve the effect of improving the accuracy of the cumulative stress field. However, in real-world scenarios, the construction period of super high-rise buildings in step S108 can last for several years. The pre-embedded sensors are prone to irreversible zero-point drift due to environmental aging, and the external measurement base station itself may settle. Under these circumstances, simply relying on the incremental superposition algorithm will infinitely amplify small systematic deviations, causing the calculated total stress field to gradually deviate from the true physical state over time.

[0123] Please see Figure 4 , Figure 4 This is another flowchart illustrating the method in the embodiments of this application;

[0124] Therefore, in some embodiments, before step S108, the method further includes:

[0125] S401. Based on the material weight data in the construction plan, calculate the total theoretical self-weight of the super high-rise building at the corresponding timestamp.

[0126] Among them, the theoretical total self-weight refers to the theoretical value of the structural gravity load calculated based on the law of conservation of matter.

[0127] Specifically, the total weight of all components completed in the construction records is added together. The concrete volume is multiplied by the standard density, and then the weights of the steel structure, curtain wall, and fixed equipment are added. This value represents the total physical weight the structure should have, serving as the absolute true value benchmark for calibration.

[0128] S402. Select the horizontal section at the bottom of the building as the verification section, and calculate the sum of the measured vertical reactions of all components on the section based on historical cumulative stress data.

[0129] Among them, the verification section refers to the horizontal section used for force balance verification; the sum of the measured vertical reaction forces refers to the total bottom support force calculated based on the monitoring data.

[0130] Specifically, identify all vertical load-bearing components on the verification section. Extract the stress monitoring value of each component at the current moment. Multiply the stress value by the cross-sectional area of ​​the component to obtain the axial force of the individual component. Sum the axial forces of all components to obtain the measured total reaction force at the bottom of the entire building.

[0131] S403. Calculate the ratio of the theoretical total self-weight to the sum of the measured vertical reaction forces to determine the global drift correction coefficient.

[0132] Among them, the global drift correction coefficient is a dimensionless factor that quantifies the overall deviation.

[0133] Specifically, a division operation is performed, dividing the theoretical total self-weight by the sum of the measured vertical reactions. This ratio reflects the degree of deviation of the monitoring from the physical true value. If the ratio is not equal to the unit value, it indicates the existence of cumulative error or drift. This coefficient will serve as the basis for subsequent corrections.

[0134] S404. Normalize and correct historical cumulative stress data using global drift correction coefficients.

[0135] Normalization correction refers to the process of adjusting data to conform to physical conservation constraints.

[0136] Specifically, all historical cumulative stress data in the database are multiplied by the global drift correction factor determined in S403. Alternatively, a translation correction is performed based on the drift type. This step forcibly adjusts the sum of the monitoring data to be consistent with the theoretical physical value, eliminating the cumulative error that diverges over time due to sensor drift.

[0137] As can be seen, by calculating the theoretical total self-weight based on the conservation of matter, a physical truth scale was established. Next, by integrating the measured reaction force at the bottom section, current data including accumulated errors was obtained. Then, by comparing these two sets of data, a drift coefficient was calculated, which quantitatively captures the degree of deviation relative to the absolute scale. Finally, the coefficient was used to normalize and correct historical data, cutting off the path of positive feedback amplification of errors over time and avoiding the divergence problem caused by benchmark drift.

[0138] The following describes the structural safety monitoring system 500 for the construction process of super high-rise buildings provided in the embodiments of this application. Figure 5 This is an exemplary hardware structure diagram of the structural safety monitoring system 500 for the construction process of super high-rise buildings provided in this application embodiment.

[0139] In some embodiments, the structural safety monitoring system 500 for the construction process of a super high-rise building is a computer device or includes a computer device. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores data. The network interface of the computer device is used to communicate with other external terminals or servers via a network connection. In some embodiments, the network interface can be a wired network interface; in some embodiments, the network interface can also be a wireless network interface. When the computer program is executed by the processor, it implements the methods in the embodiments of this application.

[0140] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0141] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0142] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0143] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0144] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for monitoring the structural safety of super high-rise buildings during construction, characterized in that, include: Temperature field distribution data and deformation data are collected using temperature and displacement sensors inside and outside the building; The temperature field distribution data and the deformation data are unified with timestamps and three-dimensional spatial coordinates to generate a multi-source heterogeneous monitoring dataset. Based on the acquired construction plan and the timestamp, a first data segment of the quiet period and a second data segment of the active period are selected from the multi-source heterogeneous monitoring dataset. The quiet period is the time during which the construction plan is not under construction; the active period is the time during which the construction plan is under construction. The thermal response coefficient matrix is ​​determined by using the correspondence between temperature fluctuation data and deformation fluctuation data in the first data segment; Substitute the temperature data of the second data segment into the thermosensitive response coefficient matrix to determine the theoretical thermal deformation component; The third data segment is obtained by correcting the second data segment using the theoretical thermal deformation component. Using the third data segment as the convergence target, the finite element model is modified in combination with the theoretical boundary conditions corresponding to the construction scheme to generate a stiffness matrix; The cumulative stress field of the super high-rise building is determined based on the stiffness matrix and historical cumulative stress data. If the stress value at a node in the cumulative stress field exceeds a preset safe yield threshold, a risk command is output.

2. The method for monitoring the structural safety of a super high-rise building during construction according to claim 1, characterized in that, The steps of collecting temperature field distribution data and deformation data based on temperature sensors and displacement sensors inside and outside the building specifically include: The illumination angle information is determined based on the timestamp; Based on the illumination angle information and the location of the obstruction in the construction plan, calculate the coordinate set of the light and shadow occlusion area; The temperature sensors are divided into disturbed sensors that fall within the coordinate set of the light and shadow occlusion area and reference sensors that fall outside the set. Using the temperature data from the reference sensor, the equivalent structural temperature at the location of the disturbed sensor is calculated to obtain the temperature field distribution data.

3. The method for monitoring the structural safety of a super high-rise building during construction according to claim 1, characterized in that, The step of modifying the finite element model and generating the stiffness matrix by using the third data segment as the convergence target and combining it with the theoretical boundary conditions corresponding to the construction scheme, specifically includes: Based on the concrete pouring batch records in the construction plan, the components in the finite element model are divided into several stiffness homogeneous groups. Calculate the theoretical stiffness baseline value and the correction allowable threshold range for each of the stiffness homogeneous groups at the timestamp; wherein, age is negatively correlated with the correction allowable threshold range. Using the third data segment as the target, and under the constraint of the correction allowable threshold range, a weighted iterative algorithm is used to adjust the uniform correction coefficient of each stiffness homogeneous group, and the material parameters of the components in each stiffness homogeneous group are updated based on the adjusted uniform correction coefficient to generate the stiffness matrix.

4. The method for monitoring the structural safety of a super high-rise building during construction according to claim 1, characterized in that, Before the step of determining the cumulative stress field of the super high-rise building based on the stiffness matrix and historical cumulative stress data, the method further includes: Based on the material weight data of the construction plan, calculate the theoretical total self-weight of the super high-rise building corresponding to the timestamp. The horizontal section at the bottom of the building is selected as the verification section, and the sum of the measured vertical reactions of all components on the section based on the historical cumulative stress data is calculated. Calculate the ratio of the theoretical total self-weight to the sum of the measured vertical reaction forces to determine the global drift correction coefficient; The historical cumulative stress data is normalized and corrected using the global drift correction coefficient.

5. The method for monitoring the structural safety of a super high-rise building during construction according to claim 1, characterized in that, The step of modifying the finite element model and generating the stiffness matrix by using the third data segment as the convergence target and combining it with the theoretical boundary conditions corresponding to the construction scheme, specifically includes: Using the theoretical boundary conditions of the active period in the construction plan, a finite element reference model containing the boundary conditions is constructed. The third data segment is applied as a displacement constraint to the finite element reference model; based on the principle of minimum potential energy, the structural material parameters are calculated to obtain the stiffness matrix.

6. The method for monitoring the structural safety of a super high-rise building during construction, as described in claim 5, is characterized in that... The step of determining the cumulative stress field of a super high-rise building based on the stiffness matrix and historical cumulative stress data specifically includes: Matrix operations are performed using the stiffness matrix and the theoretical boundary conditions to calculate the elastic stress increment under the current working condition; The elastic stress increment is superimposed on the pre-stored historical cumulative stress data to generate a cumulative stress field.

7. The method for monitoring the structural safety of a super high-rise building during construction according to claim 1, characterized in that, Before the step of determining the cumulative stress field of the super high-rise building based on the stiffness matrix and historical cumulative stress data, the method further includes: The historical cumulative stress data is time-varyingly corrected by incorporating the material attenuation function.

8. A structural safety monitoring system for the construction process of super high-rise buildings, characterized in that, The super high-rise building construction process structural safety monitoring system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the super high-rise building construction process structural safety monitoring system to perform the method as described in any one of claims 1-7.

9. A computer program product containing instructions, characterized in that, When the computer program product is run on the super high-rise building construction process structural safety monitoring system, the super high-rise building construction process structural safety monitoring system performs the method as described in any one of claims 1-7.

10. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is run on the super high-rise building construction process structural safety monitoring system, the super high-rise building construction process structural safety monitoring system performs the method as described in any one of claims 1-7.

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