CN115759378A - Dam safety analysis early warning system and method based on digital twins - Google Patents
Dam safety analysis early warning system and method based on digital twins Download PDFInfo
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Abstract
The invention discloses a dam safety analysis early warning system and method based on digital twins, belongs to the field of intelligent water conservancy construction, and comprises a data acquisition management module, a dam finite element model, a three-dimensional virtual scene module, a dam structure and state analysis module, a real-time monitoring early warning module and an engineering decision support module. According to the invention, a digital twin body synchronous with the entity dam is established by utilizing a digital twin technology, and through the synchronization of a physical entity and a virtual model, the historical state of the dam can be tracked, the current state can be evaluated, the future state can be predicted, the functions of safety state prediction, safety risk early warning, safety state rehearsal, safety disposal plan and the like can be established, the intelligent analysis and early warning of engineering safety can be comprehensively realized, and the digitization and intelligent level of engineering management can be effectively improved.
Description
Technical Field
The invention belongs to the field of intelligent water conservancy construction, and particularly relates to a dam safety analysis early warning system and method based on a digital twin.
Background
The digital transformation is a national basic requirement, and the digital construction and management of engineering are inevitable trends. On the basis of various information construction of engineering, the current latest technologies and development trends of domestic and foreign simulation, real-time control, virtual reality and the like are combined, a theoretical method system of a digital twin dam is constructed on the basis of unified data platform service, an immersive dam engineering decision support environment is created through synchronous operation of a physical entity and a virtual model, data and parameter interaction, construction of a key technology knowledge map, simulation monitoring and regulation, a dam digital twin organism consisting of multi-source data, a virtual model, a support software system, a key technology knowledge map and a visual decision platform is constructed, digital management and control of the whole life of the engineering are achieved, the intrinsic safety of engineering operation management is improved, and water conservancy is boosted for high-quality development.
A dam safety analysis early warning system is an important guarantee for long-term safe operation of hydraulic engineering. In recent years, water conservancy information construction has achieved positive results, the digitization, networking and intelligentization levels of dam safety analysis are continuously improved, but compared with the requirement of water conservancy high-quality development, the intelligentization degree of business is still insufficient, and the requirements of dam safety supervision in a new stage cannot be met in the aspects of engineering monitoring perception, structural behavior analysis algorithm, monitoring and early warning capacity and the like. Therefore, the supporting driving function of a new generation of information technology needs to be fully exerted, a dam safety analysis early warning system and method based on the digital twin are established, and the comprehensive supervision capability of the dam safety is improved and the engineering safety operation and sustainable development are promoted through synchronous simulation operation and virtual-real interaction with the entity dam and real-time monitoring of various software and hardware devices.
Disclosure of Invention
Aiming at the defect of supervision capacity of the existing engineering, the invention provides a dam safety analysis early warning system and method based on digital twins, wherein digital twins synchronous with an entity dam are established, and the historical state of the dam can be tracked, the current state can be evaluated, the future state can be predicted through the synchronization of a physical entity and a virtual model, so that the digital and intelligent management of engineering safety prediction, early warning, preview and plan can be realized.
In order to achieve the purpose, the invention adopts the following technical scheme:
a dam safety analysis early warning system based on digital twins comprises a data acquisition management module, a dam finite element model and three-dimensional virtual scene module, a dam structure and state analysis module, a real-time monitoring early warning module and an engineering decision support module.
The data acquisition management module is used for acquiring dam site area environmental quantity monitoring data and dam safety monitoring data and providing corresponding data retrieval service according to the query instruction;
the dam finite element model is used for supporting dam structure performance finite element calculation, and after calculation results are extracted, visual display and interactive query are carried out in a three-dimensional virtual scene;
the dam structure performance analysis module comprises a three-dimensional finite element model based on a dam and bedrock, an environment quantity prediction module and an automatic pre-and-post processing module; performing finite element calculation analysis on the current or future working state of the dam according to the monitoring data of the data acquisition management module and the dam finite element model; the analysis result is fed back to the three-dimensional virtual scene module and the real-time monitoring and early warning module;
the real-time monitoring and early warning module dynamically calculates dam safety monitoring indexes according to monitoring data of the data acquisition and management module and analysis results of the dam structure performance analysis module, and performs safety assessment and graded early warning according to the monitoring indexes and the monitoring data; the analysis result is input to the engineering decision support module;
the engineering decision support module retrieves engineering knowledge, design data and rule specifications, and performs attribution analysis and engineering measure suggestion according to the early warning result of the real-time monitoring early warning module.
The early warning method of the dam safety analysis early warning system based on the digital twin comprises the following steps:
the method comprises the following steps: in the actual operation process of the dam, the data acquisition management module automatically acquires the environment quantity of the dam site area, the deformation of the dam, the seepage pressure, the stress strain and the temperature monitoring data at regular time, performs gross error judgment and calibration on the acquired data of different monitoring items, and performs classified storage on the filtered data according to the monitoring items;
step two: the dam structure performance analysis module acquires a real-time monitoring value of the environmental quantity from the data acquisition management module according to a user instruction, or calls the environmental quantity prediction module to generate an environmental quantity prediction value, or introduces a preview scheduling scheme of the environmental quantity from the outside of the system; setting boundary conditions of finite element calculation according to the measured value, the predicted value or the imported data of the environmental quantity, and generating a calculation input file;
step three: the dam structure performance analysis module calls a dam finite element model to perform dam structure performance analysis, and extracts temperature, deformation, stress and seepage time-course curves of corresponding positions from a finite element result according to the arrangement position of a monitoring instrument after the analysis is finished; respectively drawing cloud pictures of a temperature field, a deformation field, a stress field and a seepage field of the dam and the bedrock according to the finite element calculation result, and calling a dam finite element model and a three-dimensional virtual scene module to load and display the result; writing the analysis time, the analysis type and the execution condition of the historical analysis into a calculation log, and storing a time course curve to a corresponding form of a database and a cloud picture to a specified path;
step four: the real-time monitoring and early-warning module adopts a confidence interval method and a limit state method to draw up a graded monitoring index of dam deformation, adopts a standard method to draw up a graded monitoring index of seepage and seepage pressure, and adopts a small probability method to draw up a graded monitoring index of stress strain.
Step five: the real-time monitoring and early-warning module acquires current dam safety monitoring data from the data acquisition and management module, performs state recognition on the measuring points of each monitoring project by using the grading monitoring indexes, and performs classification statistics on the number of the measuring points with measured values exceeding the monitoring indexes: when the measured value is within the first-level monitoring index range, judging the state of the measuring point to be normal; when the measured value exceeds the first-level monitoring index but is within the range of the second-level monitoring index, judging that the state of the measuring point is slightly abnormal; when the measured value exceeds the second-level monitoring index but is within the range of the third-level monitoring index, judging that the state of the measuring point is abnormal; when the measured value exceeds the three-level monitoring index, judging the state of the measuring point to be dangerous;
step six: the real-time monitoring and early warning module comprehensively evaluates the overall safety state of the dam according to the monitoring state of each monitoring project measuring point and gives an early warning level: if the monitoring states of all the measuring points are normal, judging that the overall safety state of the dam is safe; if the number of slightly abnormal or abnormal measuring points in the same dam section is less than or equal to l, or the number of slightly abnormal or abnormal measuring points in two adjacent dam sections is less than or equal to 2, judging that the overall safety state of the dam is safe as a first-level early warning; if the same dam section has 2 or more than 2 slight abnormal or abnormal points, or the adjacent dam sections have more than 2 slight abnormal or abnormal points, but not more than two adjacent dam sections; judging the whole safety state of the dam as a secondary early warning; if more than 2 abnormal measuring points respectively appear on two adjacent dam sections or the sum of the abnormal points of the two adjacent dam sections is more than 2, judging that the overall safety state of the dam is three-stage early warning;
step seven: inputting the abnormal measuring points judged in the real-time monitoring and early-warning module into an engineering decision support module according to a user instruction, analyzing the monitoring project, the current monitoring state and the layout position of the abnormal measuring points, performing attribution analysis in a dam safety knowledge base, and giving related engineering cases and handling measures; and inputting the judgment result of the overall safety state of the dam in the real-time monitoring and early warning module into an engineering decision support module according to a user instruction, and automatically matching a corresponding engineering plan according to the early warning level.
As a further scheme of the invention, the data acquisition management module specifically comprises the following construction steps:
the method comprises the following steps: in the dam construction period and the operation period, internal and external monitoring instruments are arranged at the dam body and the bedrock part, the environment quantity, the dam deformation, the seepage pressure, the stress strain and the temperature of the dam site area are monitored in real time, and an automatic acquisition system of monitoring data is established.
In the data acquisition management module, lay the inside and outside monitoring devices of dam body and basement rock and include: the radar water level gauge is used for measuring upstream and downstream water levels; the differential resistance type thermometer is used for measuring the water temperature, the air temperature, the dam body temperature and the bedrock temperature of the reservoir; vacuum laser, tension wire, precision level and multipoint displacement meter for measuring horizontal deformation, vertical deformation and bedrock displacement of dam body; the osmometer, the measuring weir and the like are used for measuring the dam body osmotic pressure, the dam foundation uplift pressure and the dam foundation leakage amount; and the strain gauge group, the steel bar gauge and the steel plate gauge are used for measuring the stress strain of the dam body.
Step two: and constructing a monitoring data filter, and carrying out gross error judgment and calibration on the acquired data of different monitoring projects. And constructing a multi-source information database, classifying and storing the filtered data according to monitoring items, and establishing a multi-level index list of 'measuring point number-measured value item-measured value'.
Step three: and constructing a data query interface function, providing a real-time or historical data retrieval function according to the name of the measuring point, the name of the measuring item and the retrieval range and solving the functions of statistical analysis such as extreme values, average values and the like of the data series according to the user query instruction.
As a further scheme of the invention, the dam finite element model and the three-dimensional scene module are specifically constructed by the following steps:
the method comprises the following steps: based on engineering completion drawings, reconstruction and danger removal reinforcement data, oblique photography and laser point cloud measurement data, finite element modeling software is applied to construct a three-dimensional finite element model of the dam and the bedrock, and the model needs to reflect topographic and geological conditions of the dam area, fault fracture structures, and detailed structures of orifices, gate piers, parting joints and the like of the dam.
Step two: defining a heat dissipation surface, a water pressure surface and displacement constraints in a finite element model according to real boundary conditions in the dam operation process; and according to engineering design data, performing material division on the concrete and the bedrock in the finite element model, and respectively setting thermodynamic material parameters.
Step three: and (3) constructing a dam region high-simulation three-dimensional scene by applying unreal engine software, wherein the scene reflects the accurate component sizes, vivid material textures and detailed attribute information of buildings, structures and equipment facilities. The three-dimensional scene can load dam performance analysis results and perform dynamic visual demonstration.
As a further scheme of the invention, the dam structure behavior analysis module is specifically constructed by the following steps:
the method comprises the following steps: based on the three-dimensional finite element model of the dam and the bedrock, thermodynamic parameters of the concrete and the bedrock are obtained through inversion by finite element calculation according to environmental quantity monitoring data such as reservoir water level, reservoir water temperature, air temperature and the like and monitoring data of the dam temperature, deformation and stress strain.
In the dam structure performance analysis module, the thermal parameters of the concrete and the bedrock to be inverted comprise: heat conductivity coefficient, thermal conductivity coefficient, specific heat, surface heat dissipation coefficient; the mechanical parameters of the concrete and bedrock to be inverted include: elastic modulus, poisson's ratio, volume weight, coefficient of linear expansion, permeability coefficient, creep, and rheological parameters.
Step two: and constructing an environment quantity prediction module which comprises a reservoir water temperature prediction module and a temperature prediction module.
In the dam structure behavior analysis module, the construction method of the reservoir water temperature prediction module comprises the following steps: the data acquisition management module is used for inquiring the reservoir water temperatures of different months and different water depths in the last decade, calculating the average water temperature of the months of many years, drawing a reservoir water temperature distribution curve, and obtaining the reservoir water temperature predicted value of any water depth at any moment through linear interpolation.
In the dam structure performance analysis module, the construction method of the air temperature prediction module is as follows: the average air temperature of the local area for years and months is obtained through the weather station in the dam site, an air temperature annual variation curve is drawn, and an air temperature predicted value at any time in one year can be obtained through linear interpolation.
Step three: and (3) constructing an automatic pre-and post-processing module, automatically generating an input file required by finite element calculation according to a user instruction, calling finite element calculation software to analyze the structural behavior of the dam, and automatically extracting a result cloud picture and a time-course curve after the analysis is finished.
In the dam structure performance analysis, the automatic pretreatment comprises the following specific steps: and according to a user instruction, acquiring a real-time monitoring value of the environmental quantity from the data acquisition management module, or calling an environmental quantity prediction module to generate an environmental quantity prediction value, or importing a preview scheduling scheme of the environmental quantity from the outside of the system. And setting boundary conditions of finite element calculation according to the environment measured value, the predicted value or the imported data, and generating a calculation input file.
In the structural behavior analysis of the dam, the automatic post-processing comprises the following specific steps: extracting temperature, deformation, stress and seepage time-course curves of corresponding positions from finite element results according to the arrangement position of the monitoring instrument; respectively drawing cloud pictures of a temperature field, a deformation field, a stress field and a seepage field of the dam and the bedrock according to a finite element calculation result, and calling a three-dimensional scene to load and display a result; and writing the analysis time, the analysis type and the execution condition of the historical analysis into a calculation log, and storing the time course curve into a corresponding form of the database and the cloud picture to a specified path.
As a further scheme of the invention, the real-time monitoring and early warning module is specifically constructed by the following steps:
the method comprises the following steps: and constructing a monitoring index calculation module, and calculating the monitoring index of each monitoring project by adopting a confidence interval method, a limit state method, a small probability method, a standard method and the like according to the result of the structural state analysis of the dam and the safety monitoring data of the dam.
In the real-time monitoring early warning module, each monitoring project all adopts hierarchical control index, specifically has: the method comprises the steps of drawing up a graded monitoring index of dam deformation by adopting a confidence interval method and an extreme state method, drawing up a graded monitoring index of seepage and seepage pressure by adopting a standard method, and drawing up a graded monitoring index of stress strain by adopting a small probability method.
Step two: and acquiring current dam safety monitoring data from the data acquisition management module, performing state recognition on the measuring points of each monitoring project by using the grading monitoring indexes, and performing classification statistics on the number of the measuring points with measured values exceeding the monitoring indexes.
In the real-time monitoring and early-warning module, the abnormal grade of a measured value is divided: when the measured value is within the first-level monitoring index range, judging the state of the measuring point to be normal; when the measured value exceeds the first-level monitoring index but is within the range of the second-level monitoring index, judging that the state of the measuring point is slightly abnormal; when the measured value exceeds the second-level monitoring index but is within the range of the third-level monitoring index, judging that the state of the measuring point is abnormal; and when the measured value exceeds the three-level monitoring index, judging that the state of the measuring point is dangerous.
Step three: and comprehensively evaluating the overall safety state of the dam according to the monitoring state of each monitoring project measuring point, and giving an early warning level.
In the real-time monitoring early warning module, the early warning grades are divided: if the monitoring states of all the measuring points are normal, judging that the overall safety state of the dam is safe; if the number of slightly abnormal or abnormal measuring points in the same dam section is less than or equal to l, or the number of slightly abnormal or abnormal measuring points in two adjacent dam sections is less than or equal to 2, judging that the overall safety state of the dam is safe as a first-level early warning; if the same dam section has 2 or more than 2 slight abnormal or abnormal points, or the adjacent dam sections have more than 2 slight abnormal or abnormal points, but not more than two adjacent dam sections; judging the whole safety state of the dam as a secondary early warning; and if more than 2 abnormal measuring points respectively appear on two adjacent dam sections or the sum of the abnormal points of the two adjacent dam sections is more than 2, judging that the overall safety state of the dam is three-stage early warning.
As a further scheme of the present invention, the engineering decision support module is specifically constructed as follows:
the method comprises the following steps: the dam safety related knowledge is electronically transcribed by utilizing an informatization technology, the digitization and the structurization of the dam safety knowledge are realized, and a dam safety knowledge base is constructed. And analyzing the causal relationship of different knowledge contents, realizing logical and digital expression of the causal relationship, and forming a knowledge map. And constructing a knowledge retrieval interface, performing semantic analysis on the key words input by the user, and giving out an associated knowledge graph.
In the engineering decision support module, the dam safety related knowledge comprises: engineering design, construction, acceptance and regular inspection data, law and regulation regulations and regulation specifications related to dam transportation and management, handling experiences of engineering hidden danger or accident handling cases, engineering plans, engineering safety conventions, special safety inspection and the like.
Step two: and inputting the abnormal measuring points judged in the real-time monitoring and early warning module into an engineering decision support module, analyzing the monitoring projects, the current monitoring state and the layout positions to which the abnormal measuring points belong, and performing attribution analysis in a dam safety knowledge base to give out related engineering cases and handling measures.
Step three: and inputting the judgment result of the overall safety state of the dam in the real-time monitoring and early warning module into the engineering decision support module, and automatically matching the corresponding engineering plan by the module according to the early warning level.
The invention has the beneficial effects that:
1. the data acquisition and management module is arranged, and automatic data acquisition can be realized by means of an automatic monitoring system, so that the operation is simplified, the labor is saved, and the acquisition efficiency is improved; a data filter is constructed in the module, and gross error judgment and calibration are carried out on the acquired data, so that the data accuracy is ensured; a data query interface function is constructed in the module, a real-time or historical data retrieval function is provided, and statistical analysis functions such as extreme values and average values of a data series are calculated, so that a user can conveniently retrieve data.
2. The dam structure performance analysis system is provided with the dam structure performance analysis module, has the automatic pre-and-post processing function, can automatically acquire data, start analysis and generate results, can avoid the influence of manual operation, and reduces the use requirement of the system. The module synchronously carries out finite element calculation according to the working environment of the real dam, and displays the real-time temperature field, the deformation field, the stress field and the seepage field of the dam in a cloud picture mode, so that a user can conveniently and visually master the working state of the dam.
3. The system is provided with a real-time monitoring and early warning module, and can calculate the monitoring indexes of each monitoring project based on a confidence interval method, a limit state method, a small probability method and a standard method, judge the monitoring state of each measuring point and the overall safety state of the dam, find out potential safety hazards of the dam in time and send out early warning.
4. The system is provided with an engineering decision support module, and can perform attribution analysis and treatment measure suggestion on abnormal measuring points based on a knowledge base and a knowledge map, automatically match an engineering plan with an early warning result, and effectively support engineering operation and management personnel to perform engineering decision.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
Fig. 1 is a system block diagram of a dam safety analysis system based on digital twins according to the present invention;
FIG. 2 is an example of querying the environment monitoring data of the dam site area in the data acquisition management module;
FIG. 3 is a dam finite element model and a dam finite element model in a three-dimensional virtual scene module;
FIG. 4A is a three-dimensional virtual scene (main scene) of the dam in the dam finite element model and the three-dimensional virtual scene module;
FIG. 4B is a three-dimensional virtual scene (perspective scene) of the dam in the dam finite element model and the three-dimensional virtual scene module;
FIG. 5 is a classification statistical result of the number of the measuring points of which the measured values exceed the monitoring index in the real-time monitoring and early warning module;
FIG. 6 is a diagram showing the early warning level given by the real-time monitoring early warning module after the dam is subjected to overall safety assessment;
Detailed Description
The invention is further illustrated in detail below with reference to specific examples:
referring to fig. 1, the dam safety analysis early warning system based on the digital twin comprises a data acquisition management module, a dam finite element model, a three-dimensional virtual scene module, a dam structure performance analysis module, a real-time monitoring early warning module and an engineering decision support module.
The data acquisition management module is used for acquiring the environment quantity monitoring data of the dam site area and the dam safety monitoring data and providing corresponding data retrieval service according to the query instruction.
Specifically, in the construction period and the operation period of the dam, internal and external monitoring instruments are arranged at the dam body and the bedrock part, the environment quantity, the deformation, the seepage pressure, the stress strain and the temperature of the dam site area are monitored in real time, and an automatic acquisition system of monitoring data is established. The data acquisition management module constructs a monitoring data filter, and performs gross error judgment and calibration on the acquired data of different monitoring projects; constructing a multi-source information database, classifying and storing the filtered data according to monitoring items, and establishing a multi-level index list of 'measuring point number-measured value item-measured value'; a data query interface function is constructed, a real-time or historical data retrieval function is provided according to a user query instruction, a measuring point name, a measuring item name and a retrieval range, and statistical analysis results such as extreme values, average values and the like of a data series are obtained, as shown in fig. 2.
In the data acquisition management module, lay the inside and outside monitoring instrument who locates dam body and basement rock and include: the radar water level gauge is used for measuring upstream and downstream water levels; the differential resistance type thermometer is used for measuring the water temperature, the air temperature, the dam body temperature and the bedrock temperature of the reservoir; vacuum laser, tension wire, precision level and multipoint displacement meter for measuring horizontal deformation, vertical deformation and bedrock displacement of dam body; the osmometer, the measuring weir and the like are used for measuring the dam body osmotic pressure, the dam foundation uplift pressure and the dam foundation leakage amount; and the strain gauge group, the steel bar gauge and the steel plate gauge are used for measuring the stress strain of the dam body.
The dam finite element model and the three-dimensional virtual scene module are used for supporting dam structure performance finite element calculation, and after calculation results are extracted, visual display and interactive query are carried out in the three-dimensional virtual scene.
Based on engineering completion drawings, oblique photography and laser point cloud measurement data, a three-dimensional finite element model of the dam and the bedrock is constructed by applying finite element modeling software, and as shown in fig. 3, the model needs to reflect topographic and geological conditions of the dam area, fault fracture structures, and detailed structures of orifices, gate piers, parting joints and the like of the dam. Defining a heat dissipation surface, a water pressure surface and displacement constraints in a finite element model according to real boundary conditions in the dam operation process; and according to engineering design data, performing material division on the concrete and the bedrock in the finite element model, and respectively setting thermodynamic material parameters. And (3) constructing a dam region high-simulation three-dimensional scene by applying unreal engine software, wherein the scene reflects accurate component sizes, vivid material textures and detailed attribute information of buildings, structures and equipment facilities as shown in fig. 4A and 4B. The three-dimensional scene can load dam performance analysis results and perform dynamic visual demonstration.
The dam structure performance analysis module is used for carrying out finite element calculation analysis on the current or future working performance of the dam.
Specifically, the dam structure behavior analysis module is based on a three-dimensional finite element model of the dam and the bedrock, and is used for carrying out inversion to obtain thermodynamic parameters of concrete and bedrock through finite element calculation according to environment quantity monitoring data such as reservoir water level, reservoir water temperature, air temperature and the like and dam temperature, deformation and stress strain monitoring data; constructing an environment quantity prediction module which comprises a reservoir water temperature prediction module and a temperature prediction module; and constructing an automatic pre-and-post processing module, automatically generating an input file required by finite element calculation according to a user instruction, calling finite element calculation software to analyze the structural state of the dam, and automatically extracting a result cloud picture and a time-course curve after analysis.
In the dam structure performance analysis module, the inverted thermal parameters of the concrete and the bedrock comprise: heat conductivity coefficient, thermal conductivity coefficient, specific heat, surface heat dissipation coefficient; the mechanical parameters of the concrete and bedrock to be inverted include: elastic modulus, poisson's ratio, volume weight, coefficient of linear expansion, permeability coefficient, creep, and rheological parameters.
In the dam structure performance analysis module, the data acquisition management module inquires the reservoir water temperatures of different months and different water depths in the last decade, calculates the average water temperature of the months of the years, draws a reservoir water temperature distribution curve, and can obtain the predicted reservoir water temperature value of any water depth at any time through linear interpolation.
In the dam structure performance analysis module, the average air temperature of the local area for years and months is obtained through the weather station in the dam site area, an air temperature annual variation curve is drawn, and an air temperature predicted value at any time in one year can be obtained through linear interpolation.
In the dam structure performance analysis, automatic pretreatment is carried out to obtain a real-time monitoring value of the environmental quantity from a data acquisition management module according to a user instruction, or an environmental quantity prediction module is called to generate an environmental quantity prediction value, or a preview scheduling scheme of the environmental quantity is introduced from the outside of the system. And setting boundary conditions of finite element calculation according to the environment measured value, the predicted value or the imported data, and generating a calculation input file.
In the structural behavior analysis of the dam, automatic post-processing extracts temperature, deformation, stress and seepage time-course curves of corresponding positions from finite element results according to the arrangement positions of monitoring instruments; respectively drawing cloud charts of a temperature field, a deformation field, a stress field and a seepage field of the dam and the bedrock according to a finite element calculation result, and calling a three-dimensional scene to load and display a result; and writing the analysis time, the analysis type and the execution condition of the historical analysis into a calculation log, and storing the time-course curve into a corresponding form and a cloud picture of the database to a specified path.
The real-time monitoring and early warning module is used for dynamically calculating dam safety monitoring indexes, and carrying out safety assessment and grading early warning according to the monitoring indexes and monitoring data, as shown in fig. 6;
specifically, the real-time monitoring and early-warning module constructs a monitoring index calculation module, and calculates the monitoring indexes of each monitoring project by adopting a confidence interval method, a limit state method, a small probability method, a standard method and the like according to the structural state analysis result of the dam and the safety monitoring data of the dam. The real-time monitoring early warning module acquires current dam safety monitoring data from the data acquisition management module, performs state recognition on the measuring points of each monitoring project by using the grading monitoring indexes, and performs classified statistics on the number of the measuring points with measured values exceeding the monitoring indexes. And the real-time monitoring and early warning module comprehensively evaluates the overall safety state of the dam according to the monitoring state of each monitoring project measuring point and gives an early warning grade.
In the real-time monitoring and early warning module, each monitoring item adopts a grading monitoring index: the method is characterized in that a confidence interval method and a limit state method are adopted to draw up a grading monitoring index of dam deformation, a standard method is adopted to draw up a grading monitoring index of seepage pressure, and a small probability method is adopted to draw up a grading monitoring index of stress strain.
In the real-time monitoring and early warning module, the abnormal grade of the measured value is divided: when the measured value is within the first-level monitoring index range, judging the state of the measuring point to be normal; when the measured value exceeds the first-level monitoring index but is within the range of the second-level monitoring index, judging that the state of the measuring point is slightly abnormal; when the measured value exceeds the second-level monitoring index but is within the range of the third-level monitoring index, judging that the state of the measuring point is abnormal; and when the measured value exceeds the three-level monitoring index, judging that the state of the measuring point is dangerous.
In the real-time monitoring early warning module, the early warning grades are divided: if the monitoring states of all the measuring points are normal, judging that the overall safety state of the dam is safe; if the number of slightly abnormal or abnormal measuring points in the same dam section is less than or equal to l, or the number of slightly abnormal or abnormal measuring points in two adjacent dam sections is less than or equal to 2, judging that the overall safety state of the dam is safe as a first-level early warning; if the same dam section has more than 2 or 2 slight abnormal or abnormal points, or the adjacent dam sections have more than 2 slight abnormal or abnormal points, but not more than two adjacent dam sections; judging the overall safety state of the dam as a secondary early warning; and if more than 2 abnormal measuring points respectively appear on two adjacent dam sections or the sum of the abnormal points of the two adjacent dam sections is more than 2, judging that the overall safety state of the dam is three-stage early warning.
The engineering decision support module is used for searching engineering knowledge, design data and rule specifications, and performing attribution analysis and engineering measure suggestion according to the early warning result;
specifically, the engineering decision support module carries out electronic transcription on dam safety related knowledge by utilizing an informatization technology, realizes digitization and structuralization of dam safety knowledge, and constructs a dam safety knowledge base. And analyzing the causal relationship of different knowledge contents to realize the logical and digital expression of the knowledge contents so as to form a knowledge map. And constructing a knowledge retrieval interface, performing semantic analysis on the keywords input by the user, and giving out an associated knowledge map. And inputting the abnormal measuring points judged in the real-time monitoring and early warning module into an engineering decision support module, analyzing the monitoring projects, the current monitoring state and the layout positions to which the abnormal measuring points belong, and performing attribution analysis in a dam safety knowledge base to provide related engineering cases and handling measures. And inputting the judgment result of the overall safety state of the dam in the real-time monitoring and early warning module into the engineering decision support module, and automatically matching the corresponding engineering plan by the module according to the early warning level.
In the engineering decision support module, the dam safety related knowledge includes engineering design, construction, acceptance and regular inspection data, law and regulation specifications related to dam transportation and management, engineering hidden danger or accident handling cases, engineering plans, engineering safety meetings, special safety inspection and other handling experiences.
Claims (7)
1. A dam safety analysis early warning system based on digital twins is characterized by comprising a data acquisition management module, a dam finite element model, a three-dimensional virtual scene module, a dam structure performance analysis module, a real-time monitoring early warning module and an engineering decision support module;
the data acquisition management module is used for acquiring dam site area environment quantity monitoring data and dam safety monitoring data and providing corresponding data retrieval service according to the query instruction;
the dam finite element model is used for supporting dam structure performance finite element calculation, and after calculation results are extracted, visual display and interactive query are carried out in a three-dimensional virtual scene;
the dam structure performance analysis module comprises a three-dimensional finite element model based on a dam and bedrock, an environment quantity prediction module and an automatic pre-and-post processing module; performing finite element calculation analysis on the current or future working state of the dam according to the monitoring data of the data acquisition management module and the dam finite element model; the analysis result is fed back to the three-dimensional virtual scene module and the real-time monitoring and early warning module;
the real-time monitoring and early warning module dynamically calculates dam safety monitoring indexes according to monitoring data of the data acquisition and management module and analysis results of the dam structure performance analysis module, and performs safety assessment and graded early warning according to the monitoring indexes and the monitoring data; the analysis result is input to the engineering decision support module;
the engineering decision support module retrieves engineering knowledge, design data and rule specifications, and performs attribution analysis and engineering measure suggestion according to the early warning result of the real-time monitoring early warning module.
2. A dam safety analysis early warning method based on digital twins, which adopts the dam safety analysis early warning system based on digital twins as claimed in claim 1, comprises the following steps:
the method comprises the following steps: in the actual operation process of the dam, the data acquisition management module automatically acquires the environment quantity of the dam site area, the deformation of the dam, the seepage pressure, the stress strain and the temperature monitoring data at regular time, performs gross error judgment and calibration on the acquired data of different monitoring items, and performs classified storage on the filtered data according to the monitoring items;
step two: the dam structure performance analysis module acquires a real-time monitoring value of the environmental quantity from the data acquisition management module according to a user instruction, or calls the environmental quantity prediction module to generate an environmental quantity prediction value, or introduces a preview scheduling scheme of the environmental quantity from the outside of the system; setting boundary conditions of finite element calculation according to the measured value, the predicted value or the imported data of the environmental quantity, and generating a calculation input file;
step three: the dam structure performance analysis module calls a dam finite element model to perform dam structure performance analysis, and extracts temperature, deformation, stress and seepage time-course curves of corresponding positions from a finite element result according to the arrangement position of a monitoring instrument after the analysis is finished; respectively drawing cloud charts of a temperature field, a deformation field, a stress field and a seepage field of the dam and the bedrock according to a finite element calculation result, and calling a dam finite element model and a three-dimensional virtual scene module to load and display a result; writing the analysis time, the analysis type and the execution condition of the historical analysis into a calculation log, and storing a time course curve to a corresponding form of a database and a cloud picture to a specified path;
step four: the real-time monitoring and early-warning module adopts a confidence interval method and a limit state method to draw up a graded monitoring index of dam deformation, adopts a standard method to draw up a graded monitoring index of seepage and seepage pressure, and adopts a small probability method to draw up a graded monitoring index of stress strain.
Step five: the real-time monitoring early warning module acquires current dam safety monitoring data from the data acquisition management module, performs state recognition on the measuring points of each monitoring project by using the grading monitoring indexes, and performs classification statistics on the number of the measuring points with measured values exceeding the monitoring indexes: when the measured value is within the first-level monitoring index range, judging the state of the measuring point to be normal; when the measured value exceeds the first-level monitoring index but is within the range of the second-level monitoring index, judging that the state of the measuring point is slightly abnormal; when the measured value exceeds the second-level monitoring index but is within the range of the third-level monitoring index, judging that the state of the measuring point is abnormal; when the measured value exceeds the three-level monitoring index, judging the state of the measuring point to be dangerous;
step six: the real-time monitoring early warning module carries out comprehensive assessment on the overall safety state of the dam according to the monitoring state of each monitoring project measuring point, and gives an early warning grade: if the monitoring states of all the measuring points are normal, judging that the overall safety state of the dam is safe; if the number of slightly abnormal or abnormal measuring points in the same dam section is less than or equal to l, or the number of slightly abnormal or abnormal measuring points in two adjacent dam sections is less than or equal to 2, judging that the overall safety state of the dam is safe as a first-level early warning; if the same dam section has 2 or more than 2 slight abnormal or abnormal points, or the adjacent dam sections have more than 2 slight abnormal or abnormal points, but not more than two adjacent dam sections; judging the whole safety state of the dam as a secondary early warning; if more than 2 abnormal measuring points respectively appear on two adjacent dam sections or the sum of the abnormal points of the two adjacent dam sections is more than 2, judging that the overall safety state of the dam is three-stage early warning;
step seven: inputting the abnormal measuring points judged in the real-time monitoring and early-warning module into an engineering decision support module according to a user instruction, analyzing the monitoring project, the current monitoring state and the layout position of the abnormal measuring points, performing attribution analysis in a dam safety knowledge base, and giving related engineering cases and handling measures; and inputting the judgment result of the overall safety state of the dam in the real-time monitoring and early warning module into an engineering decision support module according to a user instruction, and automatically matching a corresponding engineering plan according to the early warning level.
3. The dam safety analysis early warning method based on the digital twin as claimed in claim 2, wherein the data acquisition management module is specifically constructed by the following steps:
the method comprises the following steps: in the dam construction period and the operation period, arranging internal and external monitoring instruments at the dam body and bedrock part, monitoring the environment quantity, the dam deformation, the seepage pressure, the stress strain and the temperature of the dam site area in real time, and establishing an automatic acquisition system of monitoring data;
in the data acquisition management module, lay the inside and outside monitoring instrument who locates dam body and basement rock and include: the radar water level gauge is used for measuring upstream and downstream water levels; the differential resistance type thermometer is used for measuring the water temperature, the air temperature, the dam body temperature and the bedrock temperature of the reservoir; vacuum laser, tension wire, precise level and multipoint displacement meter for measuring horizontal deformation, vertical deformation and bedrock displacement of dam body; a osmometer and a measuring weir for measuring the seepage pressure of the dam body, the uplift pressure of the dam foundation and the seepage quantity of the dam foundation; a strain gauge group, a steel bar gauge and a steel plate gauge for measuring stress strain of the dam body;
step two: constructing a monitoring data filter, and carrying out gross error judgment and calibration on the collected data of different monitoring projects; constructing a multi-source information database, classifying and storing the filtered data according to monitoring items, and establishing a multi-level index list of 'measuring point number-measured value item-measured value';
step three: and constructing a data query interface function, providing a real-time or historical data retrieval function according to the name of the measuring point, the name of the measuring item and the retrieval range and calculating an extreme value and an average value statistical analysis function of the data series according to the user query instruction.
4. The dam safety analysis and early warning method based on the digital twinning as claimed in claim 2, wherein the dam finite element model and the three-dimensional scene module are specifically constructed by the following steps:
the method comprises the following steps: based on engineering completion drawings, oblique photography and laser point cloud measurement data, applying finite element modeling software to construct a three-dimensional finite element model of the dam and the bedrock, wherein the model needs to reflect topographic and geological conditions of the dam area, fault fracture structure and detailed structures of orifices, gate piers and parting of the dam;
step two: defining a heat dissipation surface, a water pressure surface and displacement constraints in a finite element model according to real boundary conditions in the dam operation process; according to engineering design data, performing material division on concrete and bedrock in the finite element model, and respectively setting thermodynamic material parameters;
step three: constructing a dam region high-simulation three-dimensional scene by applying unreal engine software, wherein the scene reflects accurate component sizes, vivid material textures and detailed attribute information of buildings, structures and equipment facilities; the three-dimensional scene can load dam performance analysis results and perform dynamic visual demonstration.
5. The dam safety analysis early warning method based on the digital twin as claimed in claim 2, wherein the dam structure behavior analysis module comprises the following specific construction steps:
the method comprises the following steps: based on a three-dimensional finite element model of the dam and the bedrock, acquiring thermodynamic parameters of concrete and bedrock through finite element calculation and inversion according to reservoir water level, reservoir water temperature and air temperature environmental quantity monitoring data and dam temperature, deformation and stress strain monitoring data;
in the dam structure performance analysis module, the thermal parameters of the concrete and bedrock to be inverted comprise: heat conductivity coefficient, thermal conductivity coefficient, specific heat, surface heat dissipation coefficient; the mechanical parameters of the concrete and bedrock to be inverted include: elastic modulus, poisson's ratio, volume weight, coefficient of linear expansion, permeability coefficient, creep, and rheological parameters;
step two: constructing an environment quantity prediction module which comprises a reservoir water temperature prediction module and an air temperature prediction module;
in the dam structure behavior analysis module, the construction method of the reservoir water temperature prediction module comprises the following steps: inquiring the reservoir water temperatures of different months and different water depths in the last decade from a data acquisition management module, calculating the average water temperatures of the months of the years, drawing a reservoir water temperature distribution curve, and obtaining a reservoir water temperature predicted value of any water depth at any moment through linear interpolation;
in the dam structure performance analysis module, the construction method of the air temperature prediction module comprises the following steps: acquiring the average temperature of the local area for multiple years and months through a weather station in the dam site, drawing a temperature annual variation curve, and acquiring a predicted value of the temperature at any time in one year through linear interpolation;
step three: an automatic pre-processing and post-processing module is constructed, an input file required by finite element calculation is automatically generated according to a user instruction, finite element calculation software is called to analyze the structural behavior of the dam, and after the analysis is finished, a result cloud picture and a time-course curve are automatically extracted;
in the dam structure performance analysis, the automatic pretreatment comprises the following specific steps: according to a user instruction, acquiring a real-time monitoring value of the environmental quantity from a data acquisition management module, or calling an environmental quantity prediction module to generate an environmental quantity prediction value, or importing a preview scheduling scheme of the environmental quantity from the outside of the system; setting boundary conditions of finite element calculation according to the measured value, the predicted value or the imported data of the environmental quantity, and generating a calculation input file;
in the dam structure performance analysis, the automatic post-processing specific steps comprise: extracting temperature, deformation, stress and seepage time-course curves of corresponding positions from finite element results according to the arrangement position of the monitoring instrument; respectively drawing cloud charts of a temperature field, a deformation field, a stress field and a seepage field of the dam and the bedrock according to a finite element calculation result, and calling a three-dimensional scene to load and display a result; and writing the analysis time, the analysis type and the execution condition of the historical analysis into a calculation log, and storing the time-course curve into a corresponding form and a cloud picture of the database to a specified path.
6. The dam safety analysis and early warning method based on the digital twin as claimed in claim 2, wherein the real-time monitoring and early warning module is specifically constructed by the following steps:
the method comprises the following steps: a monitoring index calculation module is constructed, and monitoring indexes of all monitoring projects are calculated by adopting a confidence interval method, a limit state method, a small probability method and a standard method according to the result of dam structure state analysis and dam safety monitoring data;
in the real-time monitoring early warning module, each monitoring project all adopts hierarchical control index, specifically has: a confidence interval method and a limit state method are adopted to draw up a graded monitoring index of dam deformation, a standard method is adopted to draw up a graded monitoring index of seepage pressure, and a small probability method is adopted to draw up a graded monitoring index of stress strain;
step two: acquiring current dam safety monitoring data from a data acquisition management module, performing state recognition on measuring points of each monitoring project by using a grading monitoring index, and performing classification statistics on the number of the measuring points with measured values exceeding the monitoring index;
in the real-time monitoring and early warning module, the abnormal grade of the measured value is divided: when the measured value is within the first-level monitoring index range, judging the state of the measuring point to be normal; when the measured value exceeds the first-level monitoring index but is within the range of the second-level monitoring index, judging that the state of the measuring point is slightly abnormal; when the measured value exceeds the second-level monitoring index but is within the range of the third-level monitoring index, judging that the state of the measuring point is abnormal; when the measured value exceeds the three-level monitoring index, judging the state of the measuring point to be dangerous;
step three: comprehensively evaluating the overall safety state of the dam according to the monitoring state of each monitoring project measuring point, and giving an early warning level;
in the real-time monitoring early warning module, early warning grades are divided: if the monitoring states of all the measuring points are normal, judging that the overall safety state of the dam is safe; if the number of slightly abnormal or abnormal measuring points in the same dam section is less than or equal to l, or the number of slightly abnormal or abnormal measuring points in two adjacent dam sections is less than or equal to 2, judging that the overall safety state of the dam is safe as a first-level early warning; if the same dam section has 2 or more than 2 slight abnormal or abnormal points, or the adjacent dam sections have more than 2 slight abnormal or abnormal points, but not more than two adjacent dam sections; judging the overall safety state of the dam as a secondary early warning; and if more than 2 abnormal measuring points respectively appear on two adjacent dam sections or the sum of the abnormal points of the two adjacent dam sections is more than 2, judging that the overall safety state of the dam is three-stage early warning.
7. The dam safety analysis early warning method based on the digital twin as claimed in claim 2, wherein the engineering decision support module is specifically constructed by the following steps:
the method comprises the following steps: the method comprises the steps that the information technology is utilized to electronically transcribe dam safety related knowledge, digitization and structuralization of the dam safety knowledge are achieved, and a dam safety knowledge base is constructed; analyzing the causal relationship of different knowledge contents to realize the logical and digital expression of the knowledge contents and form a knowledge map; constructing a knowledge retrieval interface, performing semantic analysis on key words input by a user, and giving out an associated knowledge graph;
in the engineering decision support module, the dam safety related knowledge comprises: engineering design, construction, acceptance and regular inspection data, laws and regulations relevant to dam transportation and management, engineering hidden danger or accident handling cases, engineering plans, engineering safety conventions and special safety inspection handling experiences;
step two: inputting the abnormal measuring points judged in the real-time monitoring and early warning module into an engineering decision support module, analyzing the monitoring projects, the current monitoring state and the layout positions to which the abnormal measuring points belong, performing attribution analysis in a dam safety knowledge base, and giving out related engineering cases and handling measures;
step three: and inputting the judgment result of the overall safety state of the dam in the real-time monitoring and early warning module into an engineering decision support module, and automatically matching a corresponding engineering plan by the module according to the early warning level.
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