Historical building structure evaluation system based on digital twinning

Abstract

The invention relates to the technical field of digital twinning, and particularly discloses a historical building structure evaluation system based on digital twinning, which comprises a multi-source data acquisition module, a dynamic digital twinning modeling module, a structure health evaluation module, an intelligent maintenance decision module and a collaborative optimization analysis module. According to the scheme, geometric, material and environment information of the historical building is captured, omnibearing monitoring of the historical building is realized through a digital twin modeling technology, an intelligent evaluation model is constructed in combination with finite element analysis and an SVM algorithm, a high-risk area is rapidly identified, and authenticity and accuracy of an evaluation result are ensured; a reinforcement learning algorithm is used to adapt to complex and changeable environmental requirements of historical buildings, a maintenance strategy is automatically generated, cooperation efficiency between modules is continuously optimized through collaborative optimization analysis, the overall operation performance is improved, evaluation precision is guaranteed, and meanwhile system resource consumption is effectively controlled.

Description

Technical Field

[0001] The present invention relates to the field of digital twin technology, and in particular to a historical building structure assessment system based on digital twins. Background Art

[0002] Digital twin technology, by constructing virtual models of physical entities and combining them with real-time data collection and analysis, offers a novel solution for monitoring and evaluating building structures. However, existing assessment technologies for historical building structures still lack comprehensive data collection, dynamic model construction, and intelligent assessment methods. Typical maintenance decision-making processes are highly subjective, making it difficult to quantify risks and benefits, and the overall system resource overhead and costs are high. Summary of the Invention

[0003] In response to the above situation, in order to overcome the shortcomings of the existing technology, the present invention provides a historical building structure assessment system based on digital twins. In view of the fact that the existing historical building structure assessment technology still has deficiencies in the comprehensiveness of data collection, the dynamics of model construction and the intelligence of the assessment method, this solution captures the geometry, material and environmental information of historical buildings, and realizes all-round monitoring of historical buildings through digital twin modeling technology. It combines finite element analysis and SVM (support vector machine) algorithm to build an intelligent assessment model to quickly identify high-risk areas and ensure the authenticity and accuracy of the assessment results; in view of the fact that the general maintenance decision-making process is highly subjective, it is difficult to quantify the assessment of risks and benefits, and the overall system resource overhead is large and the cost is high, this solution uses reinforcement learning algorithms to adapt to the complex and changeable environmental requirements of historical buildings, automatically generates maintenance strategies, and continuously optimizes the collaboration efficiency between modules through collaborative optimization analysis, thereby improving the overall operating performance, ensuring the assessment accuracy and effectively controlling the system resource consumption.

[0004] The present invention provides a historical building structure assessment system based on digital twins, which includes a multi-source data acquisition module, a dynamic digital twin modeling module, a structural health assessment module, an intelligent maintenance decision module, and a collaborative optimization analysis module;

[0005] The multi-source data acquisition module comprehensively collects the time series of the geometric form, material properties, structural response and environmental parameter information of the historical buildings, pre-processes the data samples collected at each time point, obtains the pre-processed data and sends it to the dynamic digital twin modeling module;

[0006] The dynamic digital twin modeling module uses the preprocessed data to build a digital twin model of the historical building, updates the model parameters based on real-time data samples to reflect the current status of the historical building, and sends the digital twin model and its parameters to the structural health assessment module;

[0007] The structural health assessment module uses the finite element analysis method to construct a multidimensional feature vector of the historical building, combines the SVM algorithm to quantitatively assess the structural status of the historical building, identifies potential risk areas in the historical building, and outputs the structural status grade;

[0008] The intelligent maintenance decision module uses a reinforcement learning algorithm to learn maintenance strategies based on the multi-dimensional feature vectors and structural status levels of historical buildings and automatically generates maintenance plans;

[0009] The collaborative optimization analysis module constructs a multi-objective optimization model to optimize the collaboration efficiency between modules and achieve reasonable allocation of resources.

[0010] Furthermore, the dynamic digital twin modeling module includes a model building unit, a structural characteristic loading unit, a state updating unit and a model output unit;

[0011] The model building unit builds a basic three-dimensional model of the historical building based on the geometric data in the data sample;

[0012] The structural property loading unit simulates the thermal conductivity and mechanical response of historical building materials through physical simulation and data analysis based on the material properties and structural response data in the data sample, and adds material properties and structural response information to the basic three-dimensional model;

[0013] The state update unit uses the Kalman filter algorithm to update the model parameters according to real-time data samples to reflect the current state of the historical building. The formula used is as follows:

[0014] ;

[0015] ;

[0016] Where, and Represent the current time point and the previous time point respectively. Represents the predicted state, represents the state estimate at the previous moment, Represents the state transition matrix of the historical building from the previous time point to the current time point, The control input matrix representing the impact of environmental parameter information on the status of historical buildings, The control vector representing the impact of environmental parameter information on the status of historical buildings, represents the forecast error covariance, represents the error covariance at the previous time point, represents the transposed matrix of the state transition matrix, represents the covariance of the uncertain process noise in the evolution of historical buildings;

[0017] The model output unit dynamically adjusts the basic three-dimensional model based on the updated model parameters to generate a visual digital twin model.

[0018] Furthermore, the structural health assessment module includes: a condition loading unit, a feature extraction unit and a structural state classification unit;

[0019] The conditional loading unit applies boundary conditions and loading conditions to the digital twin model, and uses the finite element analysis method to calculate the stress distribution, displacement field and strain energy of key areas of the historical building to obtain changes in model parameters;

[0020] The feature extraction unit identifies potential risk areas in historical buildings based on changes in model parameters, extracts representative features, and constructs a multi-dimensional feature vector of the historical buildings;

[0021] The structural status classification unit uses SVM to classify the structural status of historical buildings. It inputs the multidimensional feature vectors of historical buildings in time series, automatically selects support vectors with representative samples, and outputs the structural category label of each time point, which is mapped to the structural status level of the historical buildings. The formula used is as follows:

[0022] ;

[0023] Where, represents a multidimensional feature vector, represents the support vector, represents the index of the support vector, represents the number of support vectors, represents the category label of the support vector, represents the Lagrange multiplier, represents the kernel function, represents the bias term, Represents the symbolic function for determining the classification result, Indicates the classification result.

[0024] Furthermore, the intelligent maintenance decision module includes a state action recognition unit, a maintenance strategy learning unit and a maintenance strategy generation unit;

[0025] The state action recognition unit receives the multidimensional feature vector and encodes it into a state variable, and defines the action space for maintenance operations on the historical building in combination with the structural state level;

[0026] The maintenance strategy learning unit is based on a reinforcement learning algorithm and learns maintenance strategies through a state-action value function. It combines maintenance cost, construction difficulty, and protection effect objectives to maximize long-term maintenance benefits. The formula used is as follows:

[0027] ;

[0028] ;

[0029] Where, Indicates the current structural status of a historic building, Indicates the currently available maintenance action. represents the state-action value function, represents the learning rate, Indicates the immediate benefits brought by the current maintenance action, represents the discount factor, Indicates the new state after the maintenance operation is performed, Indicates the next maintenance action, represents the maximum function, 、 、 represents the target weight coefficient, Indicates the protection effect. represents the maintenance cost, Indicates the difficulty of construction;

[0030] The maintenance strategy generation unit selects the action with the largest state-action value function as the optimal maintenance action in the current state, generates and outputs a maintenance plan, and uses the following formula:

[0031] ;

[0032] Where, represents the action space, represents the maximum independent variable point set function, represents the optimal maintenance action.

[0033] The beneficial effects achieved by the present invention using the above scheme are as follows:

[0034] (1) In view of the fact that existing historical building structure assessment technologies still have shortcomings in terms of comprehensiveness of data collection, dynamic model construction and intelligent assessment methods, this solution captures the geometric, material and environmental information of historical buildings, realizes all-round monitoring of historical buildings through digital twin modeling technology, and combines finite element analysis and SVM algorithm to build an intelligent assessment model to quickly identify high-risk areas and ensure the authenticity and accuracy of the assessment results.

[0035] (2) In response to the problems that the general maintenance decision-making process is highly subjective, difficult to quantify and assess risks and benefits, and the overall system resource overhead and cost are high, this solution uses reinforcement learning algorithms to adapt to the complex and changing environmental requirements of historical buildings, automatically generates maintenance strategies, and continuously optimizes the collaboration efficiency between modules through collaborative optimization analysis, improving overall operational performance, ensuring assessment accuracy, and effectively controlling system resource consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a schematic diagram of a historical building structure assessment system based on digital twins proposed in this invention.

[0037] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0039] Example 1, see Figure 1 , the present invention provides a historical building structure assessment system based on digital twin, which includes a multi-source data acquisition module, a dynamic digital twin modeling module, a structural health assessment module, an intelligent maintenance decision module and a collaborative optimization analysis module;

[0040] The multi-source data acquisition module integrates high-precision laser scanners, infrared thermal imaging equipment, vibration sensors, and environmental monitoring sensors to comprehensively collect the time series of the geometric form, material properties, structural response, and environmental parameter information of historical buildings. The data samples collected at each time point are pre-processed to obtain the pre-processed data and send it to the dynamic digital twin modeling module;

[0041] The dynamic digital twin modeling module uses the preprocessed data to build a digital twin model of the historical building, updates the model parameters based on real-time data samples to reflect the current status of the historical building, and sends the digital twin model and its parameters to the structural health assessment module;

[0042] The structural health assessment module uses the finite element analysis method to construct a multidimensional feature vector of the historical building, combines the SVM algorithm to quantitatively assess the structural status of the historical building, identifies potential risk areas in the historical building, and outputs the structural status grade;

[0043] The intelligent maintenance decision module uses a reinforcement learning algorithm to learn maintenance strategies based on the multi-dimensional feature vectors and structural status levels of historical buildings and automatically generates maintenance plans;

[0044] The collaborative optimization analysis module constructs a multi-objective optimization model to optimize the collaboration efficiency between modules and achieve reasonable allocation of resources.

[0045] Example 2, see Figure 1 This embodiment is based on the above embodiment. The multi-source data acquisition module pre-processes the data samples collected at each time point. Specifically, the data points of the four dimensions of geometry, material properties, structural response, and environmental parameter information in each data sample are cleaned and normalized respectively to eliminate noise interference and unify the data format. An anomaly detection algorithm based on a convolutional neural network is used to calculate the anomaly probability of the data sample. If the calculated anomaly probability result is higher than a threshold of 0.9, the data sample is marked as abnormal data and recorded in the log. The formula used is as follows:

[0046] ;

[0047] Where, represents the time index of the data sample, Represents the dimension index of the data point, Indicates the number of dimensions of data points contained in the data sample, Indicates the In the data sample The value of the data point, Indicates the Abnormal events of data samples, Indicates the The probability that a data sample is abnormal, is the activation function, Indicates the The weight coefficient of each data point, is the bias term, Represents a nonlinear transformation.

[0048] Example 3, see Figure 1 ,This embodiment is based on the above embodiment, and the dynamic digital twin modeling module includes a model building unit, a structural characteristic loading unit, a state updating unit and a model output unit;

[0049] The model building unit builds a basic three-dimensional model of the historical building based on the geometric data in the data sample;

[0050] The structural property loading unit simulates the thermal conductivity and mechanical response of historical building materials through physical simulation and data analysis based on the material properties and structural response data in the data sample, and adds material properties and structural response information to the basic three-dimensional model;

[0051] The state update unit uses the Kalman filter algorithm to update the model parameters according to real-time data samples to reflect the current state of the historical building. The formula used is as follows:

[0052] ;

[0053] ;

[0054] Where, and Represent the current time point and the previous time point respectively. Represents the predicted state, represents the state estimate at the previous moment, Represents the state transition matrix of the historical building from the previous time point to the current time point, The control input matrix representing the impact of environmental parameter information on the status of historical buildings, The control vector representing the impact of environmental parameter information on the status of historical buildings, represents the forecast error covariance, represents the error covariance at the previous time point, represents the transposed matrix of the state transition matrix, represents the covariance of the uncertain process noise in the evolution of historical buildings;

[0055] The model output unit dynamically adjusts the basic three-dimensional model based on the updated model parameters to generate a visual digital twin model.

[0056] Example 4, see Figure 1 ,This embodiment is based on the above embodiment, and the structural health assessment module includes a condition loading unit, a feature extraction unit and a structural state classification unit;

[0057] The conditional loading unit applies boundary conditions and loading conditions to the digital twin model, and uses the finite element analysis method to calculate the stress distribution, displacement field and strain energy of key areas of the historical building to obtain changes in model parameters;

[0058] The feature extraction unit identifies potential risk areas in historical buildings based on changes in model parameters, extracts representative features, and constructs a multi-dimensional feature vector of the historical buildings;

[0059] The structural status classification unit uses SVM to classify the structural status of historical buildings. It inputs the multidimensional feature vectors of historical buildings in time series, automatically selects support vectors with representative samples, and outputs the structural category label of each time point, which is mapped to the structural status level of the historical buildings. The formula used is as follows:

[0060] ;

[0061] Where, represents a multidimensional feature vector, represents the support vector, represents the index of the support vector, represents the number of support vectors, represents the category label of the support vector, represents the Lagrange multiplier, represents the kernel function, represents the bias term, Represents the symbolic function for determining the classification result, Indicates the classification result.

[0062] By performing the above operations, in view of the shortcomings of existing historical building structure assessment technologies in terms of the comprehensiveness of data collection, the dynamism of model construction, and the intelligence of assessment methods, this solution captures the geometric, material, and environmental information of historical buildings, realizes all-round monitoring of historical buildings through digital twin modeling technology, and constructs an intelligent assessment model by combining finite element analysis and SVM algorithm to quickly identify high-risk areas and ensure the authenticity and accuracy of the assessment results.

[0063] Example 5, see Figure 1 ,This embodiment is based on the above embodiment, and the intelligent maintenance decision module includes a state action recognition unit, a maintenance strategy learning unit and a maintenance strategy generation unit;

[0064] The state action recognition unit receives the multidimensional feature vector and encodes it into a state variable, and defines the action space for maintenance operations on the historical building in combination with the structural state level;

[0065] The maintenance strategy learning unit is based on a reinforcement learning algorithm and learns maintenance strategies through a state-action value function. It combines maintenance cost, construction difficulty, and protection effect objectives to maximize long-term maintenance benefits. The formula used is as follows:

[0066] ;

[0067] ;

[0068] Where, Indicates the current structural status of a historic building, Indicates the currently available maintenance action. represents the state-action value function, represents the learning rate, Indicates the immediate benefits brought by the current maintenance action, represents the discount factor, Indicates the new state after the maintenance operation is performed, Indicates the next maintenance action, represents the maximum function, 、 、 represents the target weight coefficient, Indicates the protection effect. represents the maintenance cost, Indicates the difficulty of construction;

[0069] The maintenance strategy generation unit selects the action with the largest state-action value function as the optimal maintenance action in the current state, generates and outputs a maintenance plan, and uses the following formula:

[0070] ;

[0071] Where, represents the action space, represents the maximum independent variable point set function, represents the optimal maintenance action.

[0072] Example 6, see Figure 1 Based on the above embodiment, this embodiment uses a collaborative optimization analysis module to build a multi-objective optimization model to optimize the collaborative efficiency between modules. Specifically, the resource allocation strategy of each module is set, including: the optimized data acquisition frequency of the multi-source data acquisition module, the model update cycle of the dynamic digital twin modeling module, the state assessment resolution of the structural health assessment module, and the computing space size of the intelligent maintenance decision module. The overall cost of the system is minimized and the collaborative efficiency between modules is maintained. The formula used is as follows:

[0073] ;

[0074] Where, Indicates the module index, Indicates the number of modules, Indicates the The goal of each module is represents the weight coefficient, represents the penalty term of the constraint condition, represents the penalty coefficient, Indicates the resource allocation strategy of each module, represents the multi-objective optimization model, represents the minimum function.

[0075] By executing the above operations, this solution uses reinforcement learning algorithms to adapt to the complex and changing environmental requirements of historical buildings, automatically generate maintenance strategies, and continuously optimize the collaboration efficiency between modules through collaborative optimization analysis, thereby improving overall operational performance, ensuring assessment accuracy, and effectively controlling system resource consumption. This addresses the problems of high subjectivity in general maintenance decision-making processes, making it difficult to quantify risks and benefits, and high overall system resource overhead and cost.

[0076] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0077] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

[0078] The present invention and its embodiments are described above. This description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if a person skilled in the art is inspired by this and, without departing from the purpose of the present invention, designs structures and embodiments similar to this technical solution without inventiveness, they shall fall within the scope of protection of the present invention.

Claims

1. A historical building structure assessment system based on digital twins, characterized by: It includes multi-source data acquisition module, dynamic digital twin modeling module, structural health assessment module, intelligent maintenance decision module and collaborative optimization analysis module; The multi-source data acquisition module comprehensively collects the time series of the geometric form, material properties, structural response and environmental parameter information of the historical buildings, pre-processes the data samples collected at each time point, obtains the pre-processed data and sends it to the dynamic digital twin modeling module; The dynamic digital twin modeling module uses the preprocessed data to build a digital twin model of the historical building, updates the model parameters based on real-time data samples to reflect the current status of the historical building, and sends the digital twin model and its parameters to the structural health assessment module; The structural health assessment module uses the finite element analysis method to construct a multidimensional feature vector of the historical building, combines the SVM algorithm to quantitatively assess the structural status of the historical building, identifies potential risk areas in the historical building, and outputs the structural status grade; The intelligent maintenance decision module uses a reinforcement learning algorithm to learn maintenance strategies based on the multi-dimensional feature vectors and structural status levels of historical buildings and automatically generates maintenance plans; The collaborative optimization analysis module constructs a multi-objective optimization model to optimize the collaboration efficiency between modules and achieve reasonable allocation of resources.

2. The historical building structure assessment system based on digital twin according to claim 1 is characterized by: The dynamic digital twin modeling module includes a model building unit, a structural characteristic loading unit, a state updating unit and a model output unit; The model building unit builds a basic three-dimensional model of the historical building based on the geometric data in the data sample; The structural property loading unit simulates the thermal conductivity and mechanical response of historical building materials through physical simulation and data analysis based on the material properties and structural response data in the data sample, and adds material properties and structural response information to the basic three-dimensional model; The state updating unit uses a Kalman filter algorithm to update model parameters based on real-time data samples to reflect the current state of the historical building; The model output unit dynamically adjusts the basic three-dimensional model based on the updated model parameters to generate a visual digital twin model.

3. The historical building structure assessment system based on digital twin according to claim 1 is characterized by: The structural health assessment module includes: a condition loading unit, a feature extraction unit and a structural state classification unit; The conditional loading unit applies boundary conditions and loading conditions to the digital twin model, and uses the finite element analysis method to calculate the stress distribution, displacement field and strain energy of key areas of the historical building to obtain changes in model parameters; The feature extraction unit identifies potential risk areas in historical buildings based on changes in model parameters, extracts representative features, and constructs a multi-dimensional feature vector of the historical buildings; The structural status classification unit uses SVM to classify the structural status of historical buildings, inputs the multidimensional feature vectors of historical buildings according to time series, automatically screens support vectors with representative samples, outputs the structural category label of each time point, and maps it to the structural status level of the historical buildings.

4. The historical building structure assessment system based on digital twin according to claim 1 is characterized by: The intelligent maintenance decision module includes a state action recognition unit, a maintenance strategy learning unit and a maintenance strategy generation unit; The state action recognition unit receives the multidimensional feature vector and encodes it into a state variable, and defines the action space for maintenance operations on the historical building in combination with the structural state level; The maintenance strategy learning unit is based on a reinforcement learning algorithm, learns maintenance strategies through a state-action value function, and combines multiple objectives to maximize long-term maintenance benefits; The maintenance strategy generation unit selects an action with the largest state-action value function as the optimal maintenance action in the current state, and generates and outputs a maintenance plan.

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