Dam safety monitoring system and method based on digital twinning

Abstract

The invention discloses a dam safety monitoring system and method based on digital twinning, and relates to the technical field of hydraulic engineering, and the system comprises a data collection management module, a dam finite element model and three-dimensional virtual scene module, a dam structural state analysis module, a real-time monitoring and early warning module and an engineering decision support module. According to the system, detection data can be deleted and selected through the data deletion and selection module, meanwhile, a signal gain index is added, gain can be carried out on attenuation of signals generated after online sensor detection signals are transmitted to a data collector, and the accuracy of deletion and selection of the detection data is improved; the dam structure state analysis module can acquire real-time monitoring data or prediction data from the data acquisition management module according to a user instruction or system setting, and performs dam structure state analysis, so as to perform real-time simulation on the current situation of the dam and perform simulation derivation on the state of the dam.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy engineering, and in particular to a dam safety monitoring system and method based on digital twins. Background Art

[0002] Reservoir dams are crucial structures for human utilization of water resources and flow regulation. Their safety and stability are directly linked to the safety of life and property in downstream areas, as well as the stable economic and social development. Therefore, dam safety monitoring is of profound and significant importance. Dam safety monitoring involves the regular or continuous observation and testing of various parts of a dam using a range of instruments and equipment to obtain data on its operating status, performance changes, and potential risks, thereby assessing its safety and stability.

[0003] According to patent number: CN118656655A, patent name: A multi-system linkage method and device for dam safety monitoring under emergency conditions, hereinafter collectively referred to as the reference patent, it records "a multi-system linkage solution for dam safety monitoring based on the Flink CDC streaming data synchronization tool. This solution forms an efficient data synchronization mechanism through real-time data monitoring and processing, and transforms data monitoring and response tasks into real-time data processing tasks, solving the problems of large data processing delays and untimely responses in traditional monitoring systems. At the same time, it can solve the problem of insufficient data synchronization and response capabilities caused by limited processing capabilities in existing methods when processing large-scale data streams and considering complex correlations between data." From this, those skilled in the art can know that the existing technologies all monitor safety protection through data monitoring, lack subsequent simulation analysis for dams, and reduce the early warning and monitoring effect of dams.

[0004] In summary, a dam safety monitoring system and method based on digital twins are designed. Summary of the Invention

[0005] In order to overcome the above-mentioned shortcomings, the present invention provides a dam safety monitoring system and method based on digital twins.

[0006] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0007] A digital twin-based dam safety monitoring system includes a data acquisition and management module for real-time monitoring of various dam parameters;

[0008] Dam finite element model and 3D virtual scene module, used to model the dam based on the original dam data;

[0009] The dam structural performance analysis module is used to perform dam structural performance analysis based on the real-time data collected by the data acquisition management module and the initial modeling data of the dam finite element model and the three-dimensional virtual scene module;

[0010] Real-time monitoring and early warning module, used to monitor the graded monitoring indicators of dam deformation, graded monitoring indicators of seepage and pressure, and graded monitoring indicators of stress and strain;

[0011] The engineering decision support module is used to generate monitoring reports and statistical analysis results to provide a scientific basis for decision makers.

[0012] Preferably, the data acquisition and management module includes a data collector, a signal transmission module, and several sensors. Each sensor is installed on the dam, and the data collector is electrically connected to each sensor. The data collector is wirelessly connected to the dam control room via the signal transmission module. The data acquisition and management module monitors various parameters of the dam in real time through various high-precision sensors installed on the dam. These sensors can accurately measure the dam's displacement, inclination, seepage, pore water pressure, stress and strain, and key temperature information, and record this data in the form of digital signals. The frequency of data collection can be set according to monitoring requirements to ensure the timeliness and accuracy of the data. The high-precision sensors include displacement sensors, stress sensors, water level sensors, pressure sensors, and flow meters.

[0013] A dam safety monitoring method based on digital twins as described above comprises the following steps:

[0014] S1. Data collection: The data collection management module automatically collects environmental data of the dam site, dam deformation, seepage pressure, stress and strain, and temperature monitoring data at regular intervals. The collected data of different monitoring items are then subjected to gross error discrimination and calibration, and the filtered data are classified and stored according to the monitoring items.

[0015] S2, dam modeling and scene virtualization, the dam finite element model and three-dimensional virtual scene module includes modeling software, which is used to model the dam based on the original design and construction information of the dam, and uses virtual reality and augmented reality technology to create scene virtualization;

[0016] S3. Dam structural performance analysis: The dam structural performance analysis module can obtain real-time monitoring data or prediction data from the data acquisition management module according to user instructions or system settings to perform dam structural performance analysis. After the analysis is completed, the temperature, deformation, stress and seepage time history curves of the corresponding positions are extracted from the finite element results of the dam finite element model and the three-dimensional virtual scene module according to the layout of each sensor, and cloud maps of the temperature field, deformation field, stress field and seepage field of the dam and bedrock are drawn;

[0017] S4, early warning: The real-time monitoring and early warning module monitors the dam deformation classification monitoring indicators, seepage and pressure classification monitoring indicators, and stress and strain classification monitoring indicators. When these three indicators are abnormal, the early warning mechanism is triggered to ensure timely response measures;

[0018] S5. Decision-making: The engineering decision support module generates monitoring reports and statistical analysis results to provide a scientific basis for decision makers. Modeling software is used to build a digital model based on existing data and synchronize it with the actual dam.

[0019] Preferably, in the step S1, the data acquisition management module includes a data selection module, which selects the detection data of each sensor. The detection data falling within (μ-2iσ, μ+2iσ) is regarded as correct data, μ is the average value of the detection data, σ is the standard deviation, i is the signal gain index, and any data point exceeding the interval of (μ-2iσ, μ+2iσ), that is, the data point whose absolute deviation is greater than 2σ, is regarded as an outlier or a gross error and should be eliminated. i is the signal gain index. As time goes by, the online sensor detection signal will attenuate after being transmitted to the data collector, and the signal is gained through i.

[0020] Preferably, the modeling software includes ANSYS and Abaqus. ANSYS and Abaqus have powerful modeling and analysis capabilities and can meet the needs of building a dam finite element model.

[0021] Preferably, the S3 step includes the following steps:

[0022] S31, data acquisition and preprocessing, obtaining real-time data of deformation, temperature, and seepage pressure monitored by sensors through the data acquisition management module;

[0023] S32, finite element model call, call the corresponding model file in the pre-stored finite element model library according to the current working condition;

[0024] S33, multi-field coupling analysis, used for temperature field analysis, deformation field analysis, stress field analysis and seepage field analysis. Temperature field analysis analyzes the coupling effect of concrete hydration heat, ambient temperature and reservoir water temperature to generate temperature gradient cloud maps; deformation field analysis compares monitored displacement with model predictions, identifies abnormal deformation areas, and generates displacement vector diagrams to display deformation direction and magnitude; stress field analysis combines material constitutive models to calculate principal stress distribution; seepage field analysis calculates the infiltration line position and seepage pressure distribution;

[0025] S34, three-dimensional graph display, showing two curves in the same coordinate system, one of which is the monitoring data curve and the other is the model prediction curve;

[0026] S35. Simultaneous deduction of monitoring data curves and model prediction curves. Based on the finite element method, the interaction between temperature field, stress field and seepage field is simulated to predict the cumulative effects of the long-term operation of the dam, thereby simultaneously deducing the monitoring data curves and model prediction curves.

[0027] Preferably, in step S4, the real-time monitoring and early warning module formulates hierarchical monitoring indicators for dam deformation using the confidence interval method and the limit state method. The confidence interval method is based on statistical principles and determines the normal fluctuation range of dam deformation, i.e., the confidence interval, based on historical monitoring data. When the monitored deformation data exceeds this range, it is considered that the dam deformation is abnormal. The limit state method sets the limit value of dam deformation based on the dam's design parameters and safety standards. When the monitored deformation data approaches or exceeds this limit value, an early warning mechanism will be triggered. By combining these two methods, the dam deformation situation can be more comprehensively and accurately assessed and corresponding hierarchical monitoring indicators can be formulated.

[0028] Preferably, in step S4, the real-time monitoring and early warning module establishes tiered monitoring indicators for seepage and pressure using a standardized approach. These indicators are typically based on relevant industry standards and specifications, and are determined based on the dam's type, structural characteristics, and geological conditions. These indicators typically include seepage flow and pressure values, and are graded based on actual conditions. When the monitored seepage and pressure data exceeds the set monitoring indicators, the system automatically triggers an early warning.

[0029] Preferably, in step S4, the real-time monitoring and early warning module formulates graded stress and strain monitoring indicators using a low-probability method. This method, based on the principles of probability statistics, analyzes historical dam stress and strain monitoring data to determine the probability of an anomaly occurring. Based on this probability, different levels of monitoring indicators can be set. When the monitored stress and strain data exceeds the set low-probability range, the dam's stress and strain state is considered abnormal, and the system triggers an early warning.

[0030] The beneficial effects of the present invention are as follows: In the dam safety monitoring system and method based on digital twins:

[0031] 1. The data selection module can be used to select the detection data. At the same time, the signal gain index is added to increase the signal attenuation that occurs after the online sensor detection signal is transmitted to the data collector, thereby improving the accuracy of the detection data selection;

[0032] 2. The dam structural performance analysis module can obtain real-time monitoring data or prediction data from the data acquisition management module according to user instructions or system settings, conduct dam structural performance analysis, simulate the current status of the dam in real time, and deduce the simulation of the dam status;

[0033] 3. The real-time monitoring and early warning module formulates graded monitoring indicators for dam deformation through the confidence interval method and the limit state method. By combining these two methods, the deformation of the dam can be evaluated more comprehensively and accurately, and corresponding graded monitoring indicators can be formulated.

[0034] 4. The real-time monitoring and early warning module formulates the seepage and pressure grading monitoring indicators through the standard method. When the monitored seepage and pressure data exceeds the set monitoring indicators, the system will automatically trigger an early warning.

[0035] 5. The real-time monitoring and early warning module formulates stress and strain graded monitoring indicators through the low-probability method. When the monitored stress and strain data exceeds the set low-probability range, it is considered that the stress and strain state of the dam is abnormal, and the system will trigger an early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The present invention will now be described by way of example with reference to the accompanying drawings, in which:

[0037] Figure 1 It is a system principle diagram of the present invention;

[0038] Figure 2 It is a diagram of the method steps of the present invention. DETAILED DESCRIPTION

[0039] The present invention will now be described in further detail with reference to the accompanying drawings, which are simplified schematic diagrams that illustrate the basic structure of the present invention in a schematic manner.

[0040] like Figure 1 As shown, a dam safety monitoring system based on digital twins includes a data acquisition and management module for real-time monitoring of various parameters of the dam;

[0041] Dam finite element model and 3D virtual scene module, used to model the dam based on the original dam data;

[0042] The dam structural performance analysis module is used to perform dam structural performance analysis based on the real-time data collected by the data acquisition management module and the initial modeling data of the dam finite element model and the three-dimensional virtual scene module;

[0043] Real-time monitoring and early warning module, used to monitor the graded monitoring indicators of dam deformation, graded monitoring indicators of seepage and pressure, and graded monitoring indicators of stress and strain;

[0044] The engineering decision support module is used to generate monitoring reports and statistical analysis results to provide a scientific basis for decision makers.

[0045] Specifically, the data acquisition and management module includes a data collector, a signal transmission module, and several sensors. Each sensor is installed on the dam. The data collector is electrically connected to each sensor and wirelessly connected to the dam control room via the signal transmission module. The data acquisition and management module uses various high-precision sensors installed on the dam to monitor various dam parameters in real time. These sensors can accurately measure key information such as displacement, inclination, seepage, pore water pressure, stress and strain, and temperature, and record this data as digital signals. The frequency of data collection can be set according to monitoring requirements to ensure the timeliness and accuracy of the data. The high-precision sensors include displacement sensors, stress sensors, water level sensors, pressure sensors, and flow meters.

[0046] like Figure 2 As shown, a dam safety monitoring method based on digital twins as described above includes the following steps:

[0047] S1. Data collection: The data collection management module automatically collects environmental data of the dam site, dam deformation, seepage pressure, stress and strain, and temperature monitoring data at regular intervals. The collected data of different monitoring items are then subjected to gross error discrimination and calibration, and the filtered data are classified and stored according to the monitoring items.

[0048] S2, dam modeling and scene virtualization, the dam finite element model and three-dimensional virtual scene module includes modeling software, which is used to model the dam based on the original design and construction information of the dam, and uses virtual reality and augmented reality technology to create scene virtualization;

[0049] S3. Dam structural performance analysis: The dam structural performance analysis module can obtain real-time monitoring data or prediction data from the data acquisition management module according to user instructions or system settings to perform dam structural performance analysis. After the analysis is completed, the temperature, deformation, stress and seepage time history curves of the corresponding positions are extracted from the finite element results of the dam finite element model and the three-dimensional virtual scene module according to the layout of each sensor, and cloud maps of the temperature field, deformation field, stress field and seepage field of the dam and bedrock are drawn;

[0050] S4, early warning: The real-time monitoring and early warning module monitors the dam deformation classification monitoring indicators, seepage and pressure classification monitoring indicators, and stress and strain classification monitoring indicators. When these three indicators are abnormal, the early warning mechanism is triggered to ensure timely response measures;

[0051] S5. Decision-making: The engineering decision support module generates monitoring reports and statistical analysis results to provide a scientific basis for decision makers. Modeling software is used to build a digital model based on existing data and synchronize it with the actual dam.

[0052] Specifically, the modeling software includes ANSYS and Abaqus. ANSYS and Abaqus have powerful modeling and analysis capabilities and can meet the needs of building a dam finite element model.

[0053] Specifically, the S3 step includes the following steps:

[0054] S31, data acquisition and preprocessing, obtaining real-time data of deformation, temperature, and seepage pressure monitored by sensors through the data acquisition management module;

[0055] S32, finite element model call, call the corresponding model file in the pre-stored finite element model library according to the current working condition;

[0056] S33. Multi-field coupling analysis is used to perform temperature field analysis, deformation field analysis, stress field analysis, and seepage field analysis. Temperature field analysis analyzes the coupling effect of concrete hydration heat, ambient temperature, and reservoir water temperature to generate a temperature gradient cloud map. Deformation field analysis compares monitored displacements with model predictions, identifies abnormal deformation areas, and generates displacement vector diagrams to display the direction and magnitude of deformation. Stress field analysis, combined with material constitutive models, calculates the principal stress distribution. For example, the compressive stress at the dam heel reaches 3.2 MPa, close to the compressive strength of C25 concrete. Key monitoring areas include stress concentration areas, such as the circumferential stress mutation area around the orifice. Seepage field analysis calculates the location of the infiltration line and the distribution of seepage pressure. For example, the water head of the piezometer behind the anti-seepage curtain is reduced to 85% of the design value.

[0057] S34, three-dimensional graph display, showing two curves in the same coordinate system, one of which is the monitoring data curve and the other is the model prediction curve;

[0058] S35. Simultaneous deduction of monitoring data curves and model prediction curves. Based on the finite element method, the interaction between temperature field, stress field and seepage field is simulated to predict the cumulative effects of the long-term operation of the dam, thereby simultaneously deducing the monitoring data curves and model prediction curves.

[0059] Specifically, in step S4, the real-time monitoring and early warning module formulates graded monitoring indicators for dam deformation using the confidence interval method and the limit state method. The confidence interval method, based on statistical principles, determines the normal fluctuation range of dam deformation, i.e., the confidence interval, based on historical monitoring data. When the monitored deformation data exceeds this range, the dam deformation is considered abnormal. The limit state method sets a limit value for dam deformation based on the dam's design parameters and safety standards. When the monitored deformation data approaches or exceeds this limit value, an early warning mechanism is triggered. By combining these two methods, a more comprehensive and accurate assessment of dam deformation can be achieved, and corresponding graded monitoring indicators can be formulated.

[0060] The limit state method is used to formulate the hierarchical monitoring index of dam deformation. First, the limit state equation Z=g(X1,X2,…X n )=0, where Z is the function function used to describe the working state of the structure; g is the specific form of the function; X1, X2,…, Xn are basic variables that affect the working state of the structure, such as load, material strength, geometric dimensions, etc.; Z>0 means that the structure is in a safe state, that is, the function of the structure can meet the design requirements; Z=0 means that the structure is in a limit state, that is, the function of the structure just meets the design requirements, but if any small load or adverse factors are added, the structure may fail; Z<0: means that the structure is in a failure state, that is, the function of the structure can no longer meet the design requirements.

[0061] Specifically, in step S4, the real-time monitoring and early warning module establishes tiered monitoring indicators for seepage and pressure using a standardized approach. These indicators are typically based on relevant industry standards and specifications, and are determined based on the dam's type, structural characteristics, and geological conditions. These indicators typically include seepage volume and seepage pressure, and are graded based on actual conditions. If the monitored seepage and pressure data exceeds the set monitoring indicators, the system will automatically trigger an early warning.

[0062] Specifically, in step S4, the real-time monitoring and early warning module uses a low-probability method to develop graded stress and strain monitoring indicators. This method, based on the principles of probability statistics, analyzes historical dam stress and strain monitoring data to determine the probability of an anomaly. Based on this probability, different levels of monitoring indicators can be set. If the monitored stress and strain data exceeds the set low-probability range, the system deems the dam's stress and strain state abnormal, triggering an early warning.

[0063] Specifically, in the S1 step, the data acquisition management module includes a data selection module, which selects the detection data of each sensor. The detection data falling within (μ-2iσ, μ+2iσ) is regarded as correct data, μ is the average value of the detection data, σ is the standard deviation, and i is the signal gain index. Any data point exceeding the interval of (μ-2iσ, μ+2iσ), that is, the data point whose absolute deviation is greater than 2σ, is regarded as an outlier or a gross error and should be eliminated. i is the signal gain index. As time goes by, the online sensor detection signal will attenuate after being transmitted to the data collector. Signal gain is performed through i, and the product of the signal attenuation index and the signal gain index is 1.

[0064] The signal gain index of i is related to the year. Different signals have different decay rates. The table is as follows:

[0065] Signal gain index (i) 1-5 years 6-10 years 11-15 years 16-20 years 21-25 years Displacement of the dam (m) 1.05 1.11 1.16 1.19 1.21 Slope of the dam (°) 1.07 1.10 1.12 1.15 1.18 Dam seepage rate (L / h) 1.04 1.06 1.08 1.10 1.12 Pore ​​water pressure of the dam (MPa) 1.03 1.09 1.11 1.15 1.18 Dam stress and strain (N) 1.01 1.04 1.06 1.11 1.14 Dam temperature (℃) 1.02 1.03 1.07 1.11 1.13

[0066] After each sensor is replaced, the signal gain index must be reset.

[0067] The above description is for inspiration. Based on the above description, relevant personnel can make various changes and modifications without departing from the technical concept of this invention. The technical scope of this invention is not limited to the content of the specification, but must be determined according to the scope of the claims.

Claims

1. A dam safety monitoring system based on digital twins, characterized by: include Data acquisition and management module, used for real-time monitoring of various parameters of the dam; Dam finite element model and 3D virtual scene module, used to model the dam based on the original dam data; The dam structural performance analysis module is used to perform dam structural performance analysis based on the real-time data collected by the data acquisition management module and the initial modeling data of the dam finite element model and the three-dimensional virtual scene module; Real-time monitoring and early warning module, used to monitor the graded monitoring indicators of dam deformation, graded monitoring indicators of seepage and pressure, and graded monitoring indicators of stress and strain; The engineering decision support module is used to generate monitoring reports and statistical analysis results to provide a scientific basis for decision makers.

2. A dam safety monitoring system based on digital twins according to claim 1, characterized in that: The data acquisition management module includes a data collector, a signal transmission module and several sensors. Each sensor is installed on the dam. The data collector is electrically connected to each sensor. The data collector is wirelessly connected to the dam control room through the signal transmission module.

3. A dam safety monitoring method based on digital twins according to any one of claims 1-2, characterized in that: The following steps are involved: S1. Data collection: The data collection management module automatically collects environmental data of the dam site, dam deformation, seepage pressure, stress and strain, and temperature monitoring data at regular intervals. The collected data of different monitoring items are then subjected to gross error discrimination and calibration, and the filtered data are classified and stored according to the monitoring items. S2, dam modeling and scene virtualization, the dam finite element model and three-dimensional virtual scene module includes modeling software, which is used to model the dam based on the original design and construction information of the dam, and uses virtual reality and augmented reality technology to create scene virtualization; S3. Dam structural performance analysis: The dam structural performance analysis module can obtain real-time monitoring data or prediction data from the data acquisition management module according to user instructions or system settings to perform dam structural performance analysis. After the analysis is completed, the temperature, deformation, stress and seepage time history curves of the corresponding positions are extracted from the finite element results of the dam finite element model and the three-dimensional virtual scene module according to the layout of each sensor, and cloud maps of the temperature field, deformation field, stress field and seepage field of the dam and bedrock are drawn; S4, early warning: The real-time monitoring and early warning module monitors the dam deformation classification monitoring indicators, seepage and pressure classification monitoring indicators, and stress and strain classification monitoring indicators. When these three indicators are abnormal, the early warning mechanism is triggered to ensure timely response measures; S5. Decision-making: The engineering decision support module generates monitoring reports and statistical analysis results to provide a scientific basis for decision makers. Modeling software is used to build a digital model based on existing data and synchronize it with the actual dam.

4. A dam safety monitoring method based on digital twins according to claim 3, characterized in that: In step S1, the data acquisition management module includes a data selection module, which selects the detection data of each sensor. The detection data falling within (μ-2iσ, μ+2iσ) is considered to be correct data, where μ is the average value of the detection data, σ is the standard deviation, and i is the signal gain index.

5. The dam safety monitoring method based on digital twin according to claim 3 is characterized by: The modeling software includes ANSYS and Abaqus.

6. The dam safety monitoring method based on digital twin according to claim 3 is characterized by: The S3 step includes the following steps: S31, data acquisition and preprocessing, obtaining real-time data of deformation, temperature, and seepage pressure monitored by sensors through the data acquisition management module; S32, finite element model call, call the corresponding model file in the pre-stored finite element model library according to the current working condition; S33, multi-field coupling analysis, used for temperature field analysis, deformation field analysis, stress field analysis and seepage field analysis; S34, three-dimensional graph display, showing two curves in the same coordinate system, one of which is the monitoring data curve and the other is the model prediction curve; S35. Simultaneous deduction of monitoring data curves and model prediction curves. Based on the finite element method, the interaction between temperature field, stress field and seepage field is simulated to predict the cumulative effect of the long-term operation of the dam, thereby simultaneously deducing the monitoring data curves and model prediction curves.

7. The dam safety monitoring method based on digital twin according to claim 3 is characterized by: In the step S4, the real-time monitoring and early warning module formulates graded monitoring indicators of dam deformation through the confidence interval method and the limit state method.

8. The dam safety monitoring method based on digital twin according to claim 3 is characterized by: In the step S4, the real-time monitoring and early warning module formulates the seepage and pressure grading monitoring indicators through the standard method.

9. The dam safety monitoring method based on digital twin according to claim 3 is characterized by: In the step S4, the real-time monitoring and early warning module formulates stress and strain graded monitoring indicators through a small probability method.

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