Reservoir dam safety monitoring method and system based on digital twinning

Abstract

The invention relates to the technical field of dam safety operation and maintenance, and provides a reservoir dam safety monitoring method and system based on digital twinning, and the method comprises the steps: building a change relation between the stress and displacement of a dam body through a water level stress transmission path built through a reservoir dam digital twinning model, and historical stress and displacement data; after real-time stress and weather prediction data are obtained, a physical mechanism model and a big data driving algorithm are combined, water level stress can be predicted based on the change condition of the water level, dam body displacement prediction caused by water level change is further obtained, monitoring and early warning are conducted on the dam body displacement prediction, and the dam body displacement prediction accuracy is improved. Dynamic simulation and safety evaluation of the dam operation state are realized, and early recognition capability and prediction precision of dam potential safety hazards are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of dam safety operation, and particularly relates to a reservoir dam safety monitoring method and system based on digital twinning. BACKGROUND

[0002] The core task of a reservoir dam is water storage and regulation, so compared with a simple hydropower dam or a flood control dam, it has the characteristics of large reservoir capacity and significant water level change with weather change, so that the load borne by the reservoir dam will change rapidly with the rapid change of the weather; the residual stress inside the reservoir dam will not change with the water level in a short time, and the traditional monitoring method of setting stress sensors has the problems of data isolation and response lag, which affects the identification ability of dam safety hidden dangers. SUMMARY

[0003] The present application provides a reservoir dam safety monitoring method based on digital twinning, which is used to solve the problem of dam safety monitoring response lag in the prior art.

[0004] The present application provides a reservoir dam safety monitoring method based on digital twinning, which is used to solve the problem of dam safety monitoring response lag in the prior art. Based on the position information of the dam digital twinning and each stress sensor, a water level stress transmission path is constructed; historical stress data and displacement data are obtained, and a dam body stress and displacement change relationship is constructed based on the water level stress transmission path; Real-time stress data and weather prediction data are obtained, water level stress distribution data are predicted according to the weather prediction data, real-time stress data and water level stress distribution data are superimposed to obtain predicted stress data; the predicted stress is substituted into the dam body stress and displacement change relationship to obtain dam body displacement prediction data; The dam body displacement prediction data is monitored, and if the dam body displacement prediction value exceeds the preset range, an early warning signal is sent.

[0005] Optionally, the predicted stress is substituted into the dam body stress and displacement change relationship to obtain dam body displacement prediction data, specifically: The predicted stress is substituted into the dam body stress and displacement change relationship according to time to obtain dam body displacement prediction data; after obtaining the dam body displacement prediction data for a preset time length, the water level stress transmission path is corrected based on the dam body displacement prediction data, and the dam body stress and displacement change relationship is updated.

[0006] Optionally, the predicted stress is substituted into the dam body stress and displacement change relationship to obtain dam body displacement prediction data, specifically: establish a water level stress time-varying function based on precipitation prediction data in the weather prediction data, and establish a temperature stress time-varying function based on temperature prediction data in the weather prediction data; superimpose the real-time stress data, the water level stress and the temperature stress based on time to obtain data of the prediction stress varying with time.

[0007] Optionally, after the historical stress data and the displacement data are acquired, the method further includes: acquiring a historical water level and seepage change time relationship to construct a seepage response time; after the water level stress time-varying function is established based on the precipitation prediction data in the weather prediction data, the method further includes: correcting the water level stress according to the seepage response time.

[0008] The second aspect of the present application provides a reservoir dam safety monitoring system based on digital twinning, including: a stress and displacement relationship construction module, configured to construct a water level stress transmission path based on a dam digital twin and position information of each stress sensor, acquire historical stress data and displacement data, and construct a dam body stress and displacement change relationship based on the water level stress transmission path; a displacement prediction module, configured to acquire real-time stress data and weather prediction data, predict water level stress distribution data according to the weather prediction data, superimpose the real-time stress data and the water level stress distribution data to obtain prediction stress data, and substitute the prediction stress into the dam body stress and displacement change relationship to obtain dam body displacement prediction data; a safety monitoring module, configured to monitor the dam body displacement prediction data, and issue a warning signal if the dam body displacement prediction value exceeds a preset range.

[0009] Optionally, in the displacement prediction module, the prediction stress is substituted into the dam body stress and displacement change relationship to obtain the dam body displacement prediction data, and specifically: the prediction stress is substituted into the dam body stress and displacement change relationship according to time to obtain the dam body displacement prediction data; after the dam body displacement prediction data of a preset time length is obtained, the water level stress transmission path is corrected based on the dam body displacement prediction data to update the dam body stress and displacement change relationship.

[0010] Optionally, in the displacement prediction module, the water level stress distribution data is predicted according to the weather prediction data, and the real-time stress data and the water level stress distribution data are superimposed to obtain the prediction stress data, and specifically: a water level stress time-varying function is established based on precipitation prediction data in the weather prediction data, a temperature stress time-varying function is established based on temperature prediction data in the weather prediction data, and real-time stress data, water level stress and temperature stress are superimposed based on time to obtain data of the prediction stress varying with time.

[0011] Optionally, in the stress-displacement relationship construction module, after obtaining the historical stress data and displacement data, the method further comprises: obtaining a historical water level-seepage change time relationship, and constructing a seepage response time; In the displacement prediction module, after establishing the water level stress time-varying function with the precipitation prediction data in the weather prediction data, the method further comprises: correcting the water level stress according to the seepage response time.

[0012] The third aspect of the present application provides a reservoir dam safety monitoring method and device based on digital twinning, the device comprising a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the method according to the instructions in the program code.

[0013] The fourth aspect of the present application provides a computer readable storage medium for storing program code, the program code being used to execute the method according to any one of the first aspect of the present application.

[0014] From the above technical solutions, the present application has the following advantages: the water level stress transmission path established by the reservoir dam digital twinning model, and the change relationship between the dam body stress and displacement constructed by the historical stress and displacement data, after obtaining the real-time stress and weather prediction data, combining the physical mechanism model and the big data driven algorithm, the water level stress can be predicted based on the change of the water level, and then the dam body displacement prediction caused by the change of the water level is obtained, the dam body displacement prediction is monitored and warned, the dynamic simulation and safety evaluation of the dam operation state are realized, and the early identification ability and prediction accuracy of the dam safety hidden danger are improved. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of these drawings.

[0016] Figure 1 It is a flowchart of a reservoir dam safety monitoring method based on digital twinning; Figure 2 It is a structural diagram of a reservoir dam safety monitoring system based on digital twinning. DETAILED DESCRIPTION

[0017] In order to make the application purposes, features and advantages of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the following described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0018] The present application provides a reservoir dam safety monitoring method based on digital twinning, which is used to solve the problem of dam safety monitoring response lag in the prior art.

[0019] Please refer to Figure 1 , Figure 1 The first flowchart of a reservoir dam safety monitoring method based on digital twinning provided by the embodiments of the present application.

[0020] S100, based on the position information of the dam digital twin and each stress sensor, a water level stress transmission path is constructed; historical stress data and displacement data are obtained, and a dam body stress and displacement change relationship is constructed based on the water level stress transmission path; It should be noted that the dam digital twin is constructed according to the dam structure model, and the displacement of each point of the dam is monitored in real time according to the real-time displacement sensor, and the structure model of the dam digital twin is updated in real time. The displacement sensor can be a total station or a precision level gauge, which measures the three-dimensional coordinate change of the dam surface measuring point. According to the dam digital twin, the corresponding stiffness matrix is constructed. For the dam, which is a continuum, the global stiffness matrix is gradually assembled by discretization, i.e. divided into finite elements. The stiffness matrix is related to the material elastic modulus, Poisson's ratio and geometric shape. The overall structure displacement of the dam is then redistributed by the load transmission path. The geometric shape of the stiffness matrix is obtained according to the real-time displacement data of the dam digital twin, and each node in the stiffness matrix can be established according to the distribution position of the stress sensor. In this way, the stress detection position of the historical stress data corresponds to the position of the stress sensor, so that the historical stress data corresponds to the displacement data. The material elastic modulus and Poisson's ratio are obtained according to the concrete or soil material quality of the dam body. In this embodiment, the dam body stiffness matrix is set as the water level stress transmission path. The stiffness matrix of the dam body also changes after displacement, i.e. the stress transmission path also changes. The water level stress transmission path reflects the stress situation of the entire dam body caused by the pressure on the water at a certain place in the dam body. The stress deformation of the dam body can be regarded as the displacement of the dam body. The cumulative value of the horizontal displacement of the top of the dam is allowed to be within 80 mm, and the change rate is allowed to be within 20 mm / d. The specific value needs to be determined in combination with factors such as dam type, dam height, and geological conditions. The displacement exceeding the limit will cause harm to the dam body. The stress causing the deformation of the dam body can be divided into internal and external according to the source. The external load is the hydrostatic pressure brought by the water level, which brings shear stress to the dam body. The application scenario of the reservoir dam in the embodiment can ignore the influence of wave force, while the internal stress is mainly the self-weight stress and temperature stress. The historical stress data detected by each stress sensor is the sum of the internal and external stresses at its location. According to the historical stress difference of the sensor at multiple time points and the displacement difference corresponding to the time points, the relationship between the stress and displacement changes of the dam body is constructed based on the elastic theory. The relationship can be simplified as {ε} = [K] -1 {σ}, wherein {ε} is a strain vector, [K] -1 is a stiffness matrix of the dam body, and {σ} is a stress vector. For some fixed positions in the dam body, such as the dam toe or the bottom of the dam foundation, the corresponding node rows and columns in the stiffness matrix should be removed or corrected. Further, a neural network with physical constraints such as PINNs can be trained, the mechanical equation is taken as a loss function, machine learning is combined, and the stress brought by each water level is calculated and predicted based on the digital twin of the reservoir dam, so as to correct the relationship between the stress and displacement changes of the dam body.

[0021] In S200, real-time stress data and weather prediction data are obtained, the water level stress distribution data is predicted according to the weather prediction data, the real-time stress data and the water level stress distribution data are superimposed to obtain predicted stress data, and the predicted stress is substituted into the relationship between the stress and displacement changes of the dam body to obtain dam displacement prediction data. It should be noted that the stress sensor can monitor the stress size and direction of each part of the dam in real time. Real-time stress data mainly consists of residual stress and water level stress of the dam. The residual stress includes stress caused by internal and external temperature difference during pouring and cooling, self-weight stress, external load such as earthquake and blasting, and residual stress after water pressure unloading. The water level stress is related to real-time water level. In different temperature conditions, temperature stress direction in the dam appears different due to thermal expansion and cold contraction. In combination with self-weight stress and water level stress, the stress direction of each point may also be different. Weather prediction data includes precipitation, which can be obtained from the meteorological bureau. The millimeter number corresponding to the precipitation has a direct linear relationship with the water level rising height of the reservoir. The future water level rising condition is obtained by precipitation prediction, and the water level stress condition of the dam is determined according to different water level heights. In the case of large precipitation, the water level of the reservoir may rise rapidly. The internal residual stress of the dam will not dissipate in a short time due to the rapid rise of the water level, and can be considered to be consistent with the internal stress in the current real-time stress data. The difference between the stress of the predicted water level and the stress of the current water level can be calculated. The real-time stress data is corrected by the difference to realize superposition. For example, the residual stress state of the dam is tensile stress, and the high precipitation of the rainstorm makes the water level rise rapidly, and the water pressure increases rapidly. The water level stress and the residual stress direction are consistent and superimposed on the downstream slope. The dam body as a whole is displaced downstream, the pressure stress of the dam toe increases, and the future stress of each part of the dam is obtained through the prediction of the water level. In the case of rapid rainfall or extreme high temperature, the water level of the reservoir may rise or fall rapidly, and the water level stress will change rapidly. However, the residual stress in the internal part of the reservoir dam will not dissipate rapidly in a short time, but will form a superposition effect, which will affect the structure of the dam. The predicted stress of each part of the dam is substituted into the relationship between the stress and displacement change of the dam body constructed in the foregoing steps to obtain the dam body strain at the position of the predicted stress, which reflects the displacement direction and distance of the dam body after precipitation. Further, in the case that the internal part of the dam body is considered as a whole without cracks, stress in the dam is continuous, and real-time stress data of multiple points can be obtained under multiple discrete distributed stress sensors. Then, the complete dam real-time stress distribution condition is fitted and restored based on the digital twin of the dam. After stress prediction superposition, the stress prediction condition of each point of the whole digital twin can be obtained, so that the deformation and stress prediction of the area where the sensor is not set or the position of the sensor is changed due to displacement can be carried out in subsequent monitoring.

[0022] S300, monitoring dam displacement prediction data, and if the dam displacement prediction value exceeds the preset range, a warning signal is sent.

[0023] It should be noted that the precipitation prediction data is updated in real time, and the dam displacement prediction data corresponding to the stress change caused by the precipitation in the foregoing steps will also be updated. In this updating process, the dam displacement prediction data needs to be monitored; the displacement preset range is set according to the construction specification requirements of different reservoir dams. If the dam displacement prediction value exceeds the preset range, there is a risk of dam cracking or collapse, and a warning signal needs to be sent to request manual decision-making to handle the scheme, and according to the specific dam displacement prediction situation, the regional simulation crack propagation path and dam collapse process are assisted to develop an emergency plan; the displacement of the key position of the dam can be monitored. The most concerned part of the dam structure is the stress concentration area such as the dam heel, the dam shoulder and the seepage weak area such as the core wall. Small deformation of these parts may cause disastrous consequences through a chain reaction. Therefore, displacement monitoring or early warning priority can be set for these parts to reduce the consumption of computing power.

[0024] In this embodiment, the water level stress transmission path established by the digital twin model of the reservoir dam and the historical stress and displacement data are used to build the change relationship between the dam stress and displacement. After obtaining the real-time stress and weather prediction data, the physical mechanism model and the big data driven algorithm are combined to predict the water level stress based on the change of the water level, and then the dam displacement prediction caused by the change of the water level is obtained. The dam displacement prediction is monitored and warned to realize the dynamic simulation and safety evaluation of the dam operation state, and to improve the early identification ability and prediction accuracy of the dam safety hidden danger.

[0025] The above is a detailed description of a first embodiment of a reservoir dam safety monitoring method based on digital twinning provided by the present application. The following is a detailed description of a second embodiment of a reservoir dam safety monitoring method based on digital twinning provided by the present application.

[0026] In this embodiment, a reservoir dam safety monitoring method based on digital twinning is further provided. Please refer to Figure 2 In the foregoing step S200, the water level stress distribution data is predicted according to the weather prediction data, and the real-time stress data and the water level stress distribution data are superimposed to obtain the prediction stress data, specifically as follows: The water level stress change function with time is established by the precipitation prediction data in the weather prediction data, and the temperature stress change function with time is established by the temperature prediction data in the weather prediction data; the real-time stress data, the water level stress and the temperature stress are superimposed based on time to obtain the data of the prediction stress change with time; It should be noted that the foregoing step S200 is generally aimed at short-term water level sudden change, and the residual stress in the dam body will not change significantly, and in extreme weather with continuous heavy rain for several days, the change of temperature stress needs to be considered. The precipitation prediction data in the weather prediction data is the precipitation in millimeters in a certain time period, for example, the 24-hour predicted precipitation in the rare and extremely heavy rain will reach more than 250 millimeters, but the precipitation is not evenly distributed in 24 hours, but can be distributed according to the specific prediction data, for example, 50 millimeters, 20 millimeters, 15 millimeters, etc. in each hour interval, and then the water level change function with time can be established according to the predicted precipitation in each time, and the water level stress change function with time can be established according to the relationship between water level and stress; in extreme weather with continuous heavy rain for several days, the temperature will generally change, generally the temperature will continue to drop until the highest temperature is 10-12℃ lower than before the rain, and the multi-day temperature drop will cause the temperature stress in the dam body to change with the temperature, that is, the temperature stress change function with time can be established according to the thermal expansion and contraction coefficient of the dam body material; According to the current temperature and the current water level, the real-time temperature stress and the water level stress can be obtained, and the real-time stress data is the superposition of the residual stress, the temperature stress and the water level stress, and then based on the water level stress change function with time and the temperature stress change function with time, the increase and decrease and the direction of the temperature stress and the water level stress can be predicted, the total stress change with time at each position of the dam body is obtained, and the predicted stress data is obtained.

[0027] Further, in the foregoing step S200, the predicted stress is substituted into the dam stress and displacement change relationship to obtain dam displacement prediction data, specifically: the predicted stress is substituted into the dam stress and displacement change relationship according to time to obtain dam displacement prediction data; after obtaining the dam displacement prediction data after the preset time length, the water level stress transmission path is corrected based on the dam displacement prediction data, and the dam stress and displacement change relationship is updated; It should be noted that the stress on the dam changes over time, and the displacement deformation of the dam also changes, and the water level stress transmission path in the foregoing step S100, i.e., the dam body stiffness matrix, is constructed based on the structure data of the dam digital twin, when the structure changes after the displacement deformation of the dam, the element nodes in the stiffness matrix will change correspondingly, and the path of stress transmission on the dam will also change, then the effect of stress causing deformation displacement will change with deformation; Therefore, this embodiment will correct the water level stress transmission path and the relationship between dam stress and displacement change according to the deformation caused by the predicted stress in the previous period, and then use the updated relationship between dam stress and displacement change for dam displacement prediction in the next period; The shorter the preset time length, the more accurate the displacement prediction effect brought by updating the stiffness matrix and the change relationship, but the higher the requirement for computing power, and the longer the preset time length, the greater the cumulative error caused by the un-updated stiffness matrix and the change relationship, so the appropriate time length needs to be set according to the monitoring safety requirement of the dam body; While correcting the water level stress transmission path and the stiffness matrix, the dam digital twin can also be updated and predicted, and the predicted dam displacement condition can be displayed to the reservoir dam operation and maintenance personnel in the form of three-dimensional virtual visualization.

[0028] Further, after obtaining the historical stress data and displacement data in the foregoing step S100, it further includes: obtaining the historical water level and seepage change time relationship, and constructing the seepage response time; After establishing the water level stress time-varying function with the precipitation prediction data in the weather prediction data, it further includes: correcting the water level stress according to the seepage response time.

[0029] It should be noted that for earth and rock material reservoir dam, there will be obvious seepage condition, based on the principle of effective stress, the rainfall seeps into the dam body hole system, the internal saturation of the dam body increases, the internal hole system water pressure increases, the effective stress will decrease; the seepage water forms pore water uplift pressure in the dam foundation or the dam body, offsets part of the effective self weight of the dam body, reduces the anti sliding stability, causes the dam body to lift or horizontal displacement, the seepage of the core wall or the dam foundation of the earth and rock dam may cause local softening, increase the settlement risk, and when the water flows through the dam body or the foundation, it applies a drag force to the soil particles or the concrete micro cracks, which may cause internal erosion and cause local deformation, wherein the seepage field and the stress field interact to form a positive feedback, that is, high seepage pressure leads to displacement expansion, and then the permeability increases, the seepage flow increases, and the displacement intensifies, the typical phenomenon is that the dam heel cracks due to the combined action of seepage pressure and self weight at the bottom of gravity dam; although water level change is the direct reason for seepage change, seepage is not real-time response to change, the first few hours of water level change will have fast and small seepage change through small cracks, and then in the following days or even weeks, there will be significant seepage condition, therefore, the seepage response time needs to be established according to the historical change time of water level and the change time of seepage pressure; the seepage corresponding change can be determined based on the water level change over time and the seepage response time, and then the seepage pressure change over time is obtained, after the water level stress change function is obtained, the stress is corrected based on the seepage pressure change over time, the accuracy of subsequent displacement prediction is improved, and the influence of seepage on dam displacement is added.

[0030] Further, in the foregoing step S100, before the position information of the dam digital twin and each stress sensor is based, a digital twin is established based on the BIM model of the reservoir dam, and a plurality of types of sensors are set to construct a sensor network to obtain temperature, displacement and seepage data for real-time data updating of the digital twin; the IoT, BIM and GNSS technologies are used to set the sensor network, monitoring instruments and engineering entities to compose the physical layer of the dam digital twin, to establish a time sequence database (InfluxDB) and a data lake to realize a structured database, real-time data flow and historical archives, to construct a data layer, to use FEA, DEM-CFD coupling and reduced order modeling technology to set a multi-scale simulation model and a machine learning agent model, to construct a model layer, to use digital thread and knowledge graph technology to build an early warning system, decision support and visualization platform, and to construct a service layer.

[0031] The above is a detailed description of a digital twin-based reservoir dam safety monitoring method according to the first aspect of the present application, and the following is a detailed description of an embodiment of a digital twin-based reservoir dam safety monitoring system according to the second aspect of the present application.

[0032] Please refer to Figure 2 , Figure 2A structural diagram of a reservoir dam safety monitoring system based on digital twinning is provided. The embodiment provides a reservoir dam safety monitoring system based on digital twinning, which comprises: A stress and displacement relationship construction module 10 is configured to construct a water level stress transmission path based on a dam digital twin and position information of each stress sensor, obtain historical stress data and displacement data, and construct a dam body stress and displacement change relationship based on the water level stress transmission path. The dam digital twin can utilize BIM+GIS technology to supplement a three-dimensional virtual visualization display interface of the system and utilize artificial intelligence technology to perfect a system auxiliary decision function. Based on a GIS platform and a BIM platform, geographic space technology and three-dimensional simulation technology are utilized to realize multi-dimensional information visualization display and management of reservoir engineering safety monitoring, including three-dimensional layout of monitoring instruments, three-dimensional display of monitoring data, three-dimensional positioning of early warning and alarm, and the like, support virtual visualization query, specific time period data query, and the like, and simultaneously provide visual simulation information support for modeling analysis, safety judgment and decision support modules.

[0033] A displacement prediction module 20 is configured to obtain real-time stress data and weather prediction data, predict water level stress distribution data according to the weather prediction data, superimpose the real-time stress data and the water level stress distribution data to obtain prediction stress data, and substitute the prediction stress into the dam body stress and displacement change relationship to obtain dam body displacement prediction data. A safety monitoring module 30 is configured to monitor the dam body displacement prediction data, and if the dam body displacement prediction value exceeds a preset range, an early warning signal is sent.

[0034] Further, in the displacement prediction module 20, the prediction stress is substituted into the dam body stress and displacement change relationship to obtain dam body displacement prediction data, specifically: The prediction stress is substituted into the dam body stress and displacement change relationship according to time to obtain dam body displacement prediction data. After obtaining the dam body displacement prediction data after a preset time length, the dam body displacement prediction data is used to correct the water level stress transmission path, and the dam body stress and displacement change relationship is updated.

[0035] Further, in the displacement prediction module 20, the prediction stress is substituted into the dam body stress and displacement change relationship to obtain dam body displacement prediction data, specifically: A water level stress change function with time is established based on precipitation prediction data in the weather prediction data, and a temperature stress change function with time is established based on temperature prediction data in the weather prediction data. Real-time stress data, water level stress and temperature stress are superimposed based on time to obtain prediction stress data changing with time.

[0036] Further, in the stress and displacement relationship construction module 10, after obtaining the historical stress data and the displacement data, the method further comprises: Obtain the relationship between the historical water level and the seepage change time, and construct the seepage response time; In the displacement prediction module 20, after the water level stress time-varying function is established based on the precipitation prediction data in the weather prediction data, the method further includes: correcting the water level stress according to the seepage response time.

[0037] The third aspect of the application further provides a reservoir dam safety monitoring method and device based on digital twinning, comprising a processor and a memory: the memory is used to store program code and transmit the program code to the processor; the processor is used to execute the above-mentioned reservoir dam safety monitoring method based on digital twinning according to the instructions in the program code.

[0038] The fourth aspect of the application provides a computer readable storage medium, characterized in that the computer readable storage medium is used to store program code, and the program code is used to execute the above-mentioned reservoir dam safety monitoring method based on digital twinning.

[0039] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-mentioned device and equipment can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0040] In several embodiments provided in the application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the above-mentioned device embodiments are only schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0041] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on multiple network units. According to actual needs, part or all of the units can be selected to achieve the purpose of the embodiment scheme.

[0042] In addition, each functional unit in each embodiment of the application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The above integrated unit can be realized in the form of hardware or in the form of software functional unit.

[0043] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application or the entire or part of the technical solutions that essentially contribute to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0044] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some technical features; and these modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A reservoir dam safety monitoring method based on digital twinning, characterized in that include: Based on the location information of the dam's digital twin and various stress sensors, a water level stress transmission path is constructed; Historical stress and displacement data are acquired, and the relationship between dam stress and displacement changes is constructed based on the water level stress transmission path. Real-time stress data and weather forecast data are acquired. Water level stress distribution data is predicted based on weather forecast data. The real-time stress data and water level stress distribution data are superimposed to obtain predicted stress data. The predicted stress is then substituted into the relationship between dam stress and displacement to obtain dam displacement prediction data. The system monitors the predicted displacement data of the dam body, and if the predicted displacement value of the dam body exceeds the preset range, an early warning signal is issued.

2. The reservoir dam safety monitoring method based on digital twinning according to claim 1, characterized in that, The process of substituting the predicted stress into the relationship between dam stress and displacement to obtain dam displacement prediction data is as follows: The predicted stress is substituted into the relationship between dam stress and displacement change according to time to obtain the dam displacement prediction data; after obtaining the dam displacement prediction data after a preset time length, the water level stress transmission path is corrected based on the dam displacement prediction data, and the relationship between dam stress and displacement change is updated.

3. The reservoir dam safety monitoring method based on digital twinning according to claim 1, characterized in that, The process of predicting water level stress distribution data based on weather forecast data and then overlaying the real-time stress data with the water level stress distribution data to obtain the predicted stress data is as follows: A function for water level stress versus time is established using precipitation forecast data from weather forecast data, and a function for temperature stress versus time is established using temperature forecast data from weather forecast data. Based on time, real-time stress data, water level stress, and temperature stress are superimposed to obtain data on the predicted stress versus time.

4. The method for monitoring the safety of a reservoir dam based on digital twins according to claim 1, characterized in that, After acquiring historical stress data and displacement data, the process also includes: The process involves obtaining the historical relationship between water level and seepage change over time to construct the seepage response time. After establishing the water level stress change function over time using precipitation forecast data from weather forecast data, the process further includes correcting the water level stress based on the seepage response time.

5. A reservoir dam safety monitoring system based on digital twins, characterized in that, include: The stress-displacement relationship construction module is used to construct the water level stress transmission path based on the location information of the dam digital twin and various stress sensors; Historical stress and displacement data are acquired, and the relationship between dam stress and displacement changes is constructed based on the water level stress transmission path. The displacement prediction module is used to acquire real-time stress data and weather forecast data, predict water level stress distribution data based on weather forecast data, and superimpose the real-time stress data and water level stress distribution data to obtain predicted stress data; the predicted stress is then substituted into the relationship between dam stress and displacement change to obtain dam displacement prediction data. The safety monitoring module is used to monitor the dam displacement prediction data. If the dam displacement prediction value exceeds the preset range, an early warning signal will be issued.

6. A reservoir dam safety monitoring system based on digital twins according to claim 5, characterized in that, In the displacement prediction module, the predicted stress is substituted into the relationship between dam stress and displacement to obtain dam displacement prediction data, specifically: The predicted stress is substituted into the relationship between dam stress and displacement change according to time to obtain the dam displacement prediction data; after obtaining the dam displacement prediction data after a preset time length, the water level stress transmission path is corrected based on the dam displacement prediction data, and the relationship between dam stress and displacement change is updated.

7. A reservoir dam safety monitoring system based on digital twins according to claim 5, characterized in that, In the displacement prediction module, water level stress distribution data is predicted based on weather forecast data, and the real-time stress data is superimposed with the water level stress distribution data to obtain the predicted stress data, specifically: A function for water level stress versus time is established using precipitation forecast data from weather forecast data, and a function for temperature stress versus time is established using temperature forecast data from weather forecast data. Based on time, real-time stress data, water level stress, and temperature stress are superimposed to obtain data on the predicted stress versus time.

8. A reservoir dam safety monitoring system based on digital twins according to claim 5, characterized in that, The stress-displacement relationship construction module, after acquiring historical stress data and displacement data, also includes: Obtain the historical relationship between water level and seepage change over time, and construct the seepage response time. The displacement prediction module, after establishing the water level stress change function with time based on precipitation prediction data from weather forecast data, also includes: correcting the water level stress according to the corresponding seepage time.

9. A reservoir dam safety monitoring device based on digital twins, characterized in that, The device includes a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute, according to the instructions in the program code, a reservoir dam safety monitoring method based on digital twins as described in any one of claims 1-4.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store program code for executing the reservoir dam safety monitoring method based on digital twins as described in any one of claims 1-4.

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