CN115952681A - Digital twin analysis method for flood disaster prevention in extreme rainstorm weather - Google Patents
Digital twin analysis method for flood disaster prevention in extreme rainstorm weather Download PDFInfo
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Abstract
The invention discloses a digital twin analysis method for flood disaster prevention in extreme rainstorm weather, which comprises the following steps of; step one, building a data base plate, namely building the data base plate based on digital twin construction according to water conservancy basic data, high-precision geographic space data, dynamic monitoring data, business management data and cross-industry shared data; step two, rainfall forecast, which is to provide various rainfall forecast modes; according to the method, flood simulation and rehearsal under a digital scene are realized by four-forecast digital twin analysis of 'data bottom plate construction, rainfall forecast, flood forecast, evaluation and early warning, simulated rehearsal and management plan', flood prevention consultation decisions are effectively supported, weak links of the traditional flood prevention consultation process are further optimized, and the capacity and level of unified scheduling and unified management of flood disaster flood prevention are improved.
Description
Technical Field
The invention belongs to the technical field of intelligent water conservancy flood and drought disaster prevention, and particularly relates to a digital twin analysis method for flood and drought disaster prevention in extreme rainstorm weather.
Background
In recent years, with the continuous promotion of global change and urbanization progress, flood disasters become a still more stubborn disease after the problems of population, traffic, environment and the like, 2/3 of the territory of China is threatened by flood, 2/3 of the cities are subjected to rainstorm flood with different degrees, extreme weather becomes a high-frequency word and is influenced by the extreme rainstorm weather, more than 300 areas of China are subjected to flood disasters with different degrees, the frequency threatens the life and property safety of people, and the urgency and the importance of responding to the flood disasters in the extreme rainstorm weather are highlighted.
On one hand, the prior art is difficult to realize the accurate forecast of rainstorm in extreme weather in a timing, fixed point and quantitative mode, and the drainage basin and the urban underlying surface have complex conditions, so that serious casualties and property loss are easily caused; on the other hand, flood control coping relates to a plurality of departments, and the traditional flood control coping mainly uses a paper emergency plan as a main means, lacks sufficient excavation and efficient utilization of information, is difficult to support quick response and emergency linkage under extreme rainstorm weather, and cannot practically guide actual flood disaster flood control emergency work.
In recent years, china has achieved abundant achievements in flood control informatization, and business application systems such as hydrologic monitoring, flood forecasting, flood simulation, water engineering scheduling and flood assessment are constructed, so that the important roles are played for flood control and disaster reduction in China, but the basin thorough perception calculation data is still insufficient, and a model algorithm is far from a high-fidelity target, so that the deep fusion of new-generation information technologies such as digital twin, big data and artificial intelligence and water conservancy business is enhanced, the supporting and driving effects of the information technologies are fully played, a digital twin platform is used as a base of basic facilities, an intelligent flood control with 'four forecasts' (forecasting, early warning, forecasting and planning and forecasting) is constructed, the high-quality development of water conservancy at a new stage is realized, a response method which can be used for practical operation and has good timeliness is provided for flood control personnel, the flood disaster risk is reduced to the maximum extent, and quick response and science are realized, and thus the response and loss of flood disaster personnel are reduced.
Disclosure of Invention
The invention aims to overcome the existing defects and provide a digital twin analysis method for flood disaster prevention in extreme rainstorm weather, so that intelligent water conservancy is enabled digitally, and digital support of 'advance forecasting, early warning, early scheduling and advanced pre-planning' is provided for flood prevention management work.
In order to achieve the purpose, the invention provides the following technical scheme: a digital twin analysis method for flood disaster prevention in extreme rainstorm weather comprises the following steps;
step one, building a data base plate, namely building the data base plate based on digital twin construction according to water conservancy basic data, high-precision geographic space data, dynamic monitoring data, service management data and cross-industry shared data;
step two, rainfall forecast, which is to provide various rainfall forecast modes;
step three, flood forecasting, namely adopting a numerical model to forecast the flood according to the rainfall forecast data obtained in the step two, and further forecasting whether the area is subjected to flood disasters;
evaluating and early warning, namely automatically dividing early warning grades according to the prediction result of the step three, and simultaneously feeding back the early warning result to relevant flood prevention departments for the first time to ensure the safety flood control of the region;
step five, simulating rehearsal, namely calling a model base flood evolution model based on a flood forecast result by taking a digital base as a carrier and combining a hydraulic engineering scheduling process of the area to carry out flood disaster rehearsal, and further supporting flood control scheduling consultation decision of the area by utilizing the situation of forecasting and scheduling integrated dynamic rehearsal of the area;
and step six, managing the plan, and on the basis that the system automatically and comprehensively analyzes the basic conditions of the area, according to the feedback result of the step five, comprehensively comparing, analyzing and evaluating personnel, economic losses and the like which are possibly influenced after the implementation of the plan by combining the historical scheduling plan and the effect, comprehensively evaluating and optimizing the scheduling plan, and starting the corresponding plan grade to form the coping plan of the flood disaster scene through multi-plan comparison.
Preferably, the plurality of rainfall forecasting methods in the second step include the following three methods;
the method comprises the following steps of firstly, forecasting rainfall by adopting a machine learning model according to long-sequence actual measurement rainfall data of an area where the rainfall occurs;
the method II comprises the steps of utilizing a rainfall influence knowledge graph to carry out similarity analysis, and automatically recommending historical similar rainfall fields and rainfall processes by a system to carry out rainfall forecast;
in a third mode, by combining with the change trend of a weather system, a user self-defines the rainfall area and intensity according to personal expert experience, or performs rainfall forecast by amplifying and reducing the rainfall area and intensity in a same ratio on similar rainfall occasions; and (4) forecasting rainfall through various rainfall forecasting methods, judging whether extreme rainfall occurs in the area in the future days, if so, performing flood forecasting in the third step, and if not, continuing to perform rainfall forecasting in the area.
Preferably, in the third step, if flood disasters occur, the fourth step is carried out for early warning, and if not, the first step is returned to continue rainfall forecast.
Preferably, the rainfall forecast comprises a machine learning model, a rainfall influence knowledge graph and a custom rainfall intensity.
Preferably, the machine learning model comprises contents of neural network, machine learning and the like, analysis and research are carried out based on data of a single station, the single station is a manual sampling point or an automatic monitoring station, finally, after the forecast characteristic factors are selected, factor values of the previous day are added into modeling data every day, modeling is carried out again, and dynamic updating of the forecasting system is achieved.
Preferably, the numerical model in step three is: the flood forecasting numerical model mainly comprises an interval basin rainfall runoff forecasting scheme and a main flow river flood forecasting scheme, wherein the rainfall runoff forecasting comprises the following steps: rainfall runoff forecast is carried out according to the interval rainfall falling, the content of the rainfall runoff forecast is mainly used for forecasting the production convergence process of the interval rainfall falling, a certain forecast period is provided, and forecast accuracy is relatively high; forecasting river flood: river flood forecasting is carried out according to upstream incoming water, the content is used under the condition of no interval rainfall, the forecasting precision is high, the forecasting period is limited by the propagation time of river flood, and the forecasting period is relatively short.
Preferably, flood disaster flood evolution simulation: coupling the one-dimensional hydrodynamic model and the two-dimensional earth surface submerging model; and (3) inputting rainfall forecast data serving as driving data and parameter sets into the model, carrying out large-range, quick and high-precision simulation on flooding, and analyzing the flood evolution process, the flood submergence condition and the arrival time.
Preferably, flood risk analysis: the method is used for evaluating the flood disaster risk of the area, is dynamically connected with flood disaster flood evolution simulation, a social and economic database and the like, quickly evaluates the flood situations and losses in real time and distributes the flood situations and the losses, carries out risk evaluation and zoning on the flood disasters, comprises loss evaluation, risk zoning and disaster situation risk levels, and strives for precious time for making and implementing response schemes for transferring people, property and the like in advance.
Compared with the prior art, the invention provides a digital twin analysis method for flood disaster defense in extreme rainstorm weather, which has the following beneficial effects:
1. according to the method, the flood simulation and rehearsal under a digital scene are realized by four-forecast digital twin analysis of 'data base plate construction-rainfall forecast-flood forecast-assessment early warning-simulation rehearsal-management plan', the decision of flood prevention consultation is effectively supported, the weak link of the traditional flood prevention consultation process is further optimized, and the capacity and level of flood disaster flood prevention unified scheduling and unified management are improved;
2. the invention can quickly respond and scientifically prevent flood, provides technical support for protecting the life and property safety of people, and provides powerful support and powerful drive for high-quality development of water conservancy.
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 without limiting the invention in which:
FIG. 1 is a flow chart of a digital twin analysis method according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention provides a technical solution: a digital twin analysis method for flood disaster prevention in extreme rainstorm weather comprises the following steps;
step one, building a data base plate, namely building the data base plate based on digital twin construction according to water conservancy basic data, high-precision geographic space data, dynamic monitoring data, business management data and cross-industry shared data;
step two, rainfall forecast, which provides various rainfall forecast modes;
step three, flood forecasting, namely adopting a numerical model to forecast the flood according to the rainfall forecast data obtained in the step two, and further forecasting whether the area is subjected to flood disasters;
evaluating and early warning, automatically dividing early warning grades according to the prediction result of the third step, and simultaneously feeding the early warning result back to relevant flood prevention departments for the first time to ensure the safe flood control of the area;
step five, simulating rehearsal, namely calling a model base flood evolution model based on a flood forecast result by taking a digital base as a carrier and combining a hydraulic engineering scheduling process of the area to carry out flood disaster rehearsal, and further supporting flood control scheduling consultation decision of the area by utilizing the situation of forecasting and scheduling integrated dynamic rehearsal of the area;
and step six, managing the plan, and on the basis that the system automatically and comprehensively analyzes the basic conditions of the area, according to the feedback result of the step five, comprehensively comparing, analyzing and evaluating personnel, economic losses and the like which are possibly influenced after the implementation of the plan by combining the historical scheduling plan and the effect, comprehensively evaluating and optimizing the scheduling plan, and starting the corresponding plan grade to form the coping plan of the flood disaster scene through multi-plan comparison.
Preferably, the plurality of rainfall forecasting methods in the second step include the following three methods;
the method comprises the following steps of firstly, forecasting rainfall by adopting a machine learning model according to long-sequence actual measurement rainfall data of an area where the rainfall occurs;
the method II comprises the steps of carrying out similarity analysis by utilizing a rainfall influence knowledge graph, and automatically recommending historical similar rainfall fields and rainfall processes by a system to forecast rainfall;
in a third mode, combining the change trend of a weather system, a user self-defines the rainfall area and intensity according to personal expert experience, or performs rainfall forecast by amplifying and reducing the rainfall area and intensity in a same ratio on similar rainfall fields; and (4) forecasting rainfall through various rainfall forecasting methods, judging whether extreme rainfall occurs in the area in the future days, if so, performing flood forecasting in the third step, and if not, continuing to perform rainfall forecasting in the area.
In the invention, preferably, in the third step, if flood disasters occur, the early warning is carried out in the fourth step, and if not, the rainfall forecast is continued in the first step.
In the invention, preferably, the rainfall forecast comprises a machine learning model, a rainfall influence knowledge graph and a user-defined rainfall intensity.
In the invention, preferably, the machine learning model comprises contents of neural network, machine learning and the like, analysis and research are carried out based on data of a single station, the single station is a manual sampling point or an automatic monitoring station, and finally after forecasting characteristic factors are selected, factor values of the previous day are added into the modeling data every day, modeling is carried out again, and dynamic updating of a forecasting system is realized.
In the present invention, preferably, the numerical model in step three is: the flood forecast numerical model mainly comprises an interval basin rainfall runoff forecast scheme and a main flow river flood forecast scheme, wherein the rainfall runoff forecast comprises the following steps: rainfall runoff forecast is carried out according to the interval rainfall falling, the content of the rainfall runoff forecast is mainly used for forecasting the production convergence process of the interval rainfall falling, a certain forecast period is provided, and forecast accuracy is relatively high; forecasting river flood: river flood forecasting is carried out according to upstream incoming water, the content is used under the condition of no interval rainfall, the forecasting precision is high, the forecasting period is limited by the propagation time of river flood, and the forecasting period is relatively short.
In the invention, preferably, flood evolution simulation of flood disasters: coupling the one-dimensional hydrodynamic model and the two-dimensional earth surface submerging model; and (3) inputting rainfall forecast data serving as driving data and parameter sets into the model, carrying out large-range, quick and high-precision simulation on flooding, and analyzing the flood evolution process, the flood submergence condition and the arrival time.
In the present invention, preferably, the flood risk analysis: the method is used for evaluating the flood disaster risk of the area, is dynamically connected with flood disaster flood evolution simulation, a social and economic database and the like, quickly evaluates the flood situations and losses in real time and distributes the flood situations and the losses, carries out risk evaluation and zoning on the flood disasters, comprises loss evaluation, risk zoning and disaster situation risk levels, and strives for precious time for making and implementing response schemes for transferring people, property and the like in advance.
Example one
A digital twin analysis method for flood disaster prevention in extreme rainstorm weather comprises the following steps;
step one, building a data base plate, namely building the data base plate based on digital twin construction according to water conservancy basic data, high-precision geographic space data, dynamic monitoring data, business management data and cross-industry shared data;
step two, rainfall forecast, which provides various rainfall forecast modes; the method comprises the following three steps;
the method comprises the following steps of firstly, forecasting rainfall by adopting a machine learning model according to long-sequence actual measurement rainfall data of an area where the rainfall occurs;
the method II comprises the steps of carrying out similarity analysis by utilizing a rainfall influence knowledge graph, and automatically recommending historical similar rainfall fields and rainfall processes by a system to forecast rainfall;
in a third mode, by combining with the change trend of a weather system, a user self-defines the rainfall area and intensity according to personal expert experience, or performs rainfall forecast by amplifying and reducing the rainfall area and intensity in a same ratio on similar rainfall occasions; and (4) forecasting rainfall through various rainfall forecasting methods, judging whether extreme rainfall occurs in the area in the future days, if so, performing flood forecasting in the third step, and if not, continuing to perform rainfall forecasting in the area.
Step three, flood forecasting, namely, according to the rainfall forecasting data obtained in the step two, adopting a numerical model to forecast the flood, further forecasting whether flood disasters happen to the area where the rainfall forecasting data is located, if the flood disasters happen, turning to the step four to carry out early warning, and if not, returning to the step one to continue the rainfall forecasting;
evaluating and early warning, namely automatically dividing early warning grades according to the prediction result of the step three, and simultaneously feeding back the early warning result to relevant flood prevention departments for the first time to ensure the safety flood control of the region;
step five, simulating rehearsal, namely calling a model library flood evolution model based on a flood forecast result by taking a digital base as a carrier, combining a hydraulic engineering scheduling process of the area to perform flood disaster rehearsal, and further supporting flood control scheduling consultation decision of the area by utilizing the condition of integrated dynamic rehearsal of the area forecast scheduling;
and step six, managing the plan, and on the basis that the system automatically and comprehensively analyzes the basic conditions of the area, according to the feedback result of the step five, comprehensively comparing, analyzing and evaluating personnel, economic losses and the like which are possibly influenced after the implementation of the plan by combining the historical scheduling plan and the effect, comprehensively evaluating and optimizing the scheduling plan, and starting the corresponding plan grade to form the coping plan of the flood disaster scene through multi-plan comparison.
Example two
A digital twin analysis method for flood disaster prevention in extreme rainstorm weather comprises the following steps;
the method comprises the steps of a machine learning model, a rainfall influence knowledge graph and user-defined rainfall intensity.
The machine learning model comprises contents such as neural network and machine learning, analysis and research are carried out based on data of a single station, the single station is a manual sampling point or an automatic monitoring station, finally, after forecasting characteristic factors are selected, factor values of the previous day are added into modeling data every day, modeling is carried out again, and dynamic updating of a forecasting system is achieved.
Numerical model: the flood forecast numerical model mainly comprises an interval basin rainfall runoff forecast scheme and a main flow river flood forecast scheme, wherein the rainfall runoff forecast comprises the following steps: rainfall runoff forecasting is carried out according to the area rainfall falling, the rainfall runoff forecasting is mainly carried out according to the content of the area rainfall falling, the rainfall falling forecasting is carried out in the area rainfall falling process forecasting process, a certain forecasting period is achieved, and the forecasting precision is relatively high; forecasting river flood: river flood forecasting is carried out according to upstream incoming water, the content is used under the condition of no interval rainfall, the forecasting precision is high, the forecasting period is limited by the propagation time of river flood, and the forecasting period is relatively short.
Flood disaster flood evolution simulation: coupling the one-dimensional hydrodynamic model and the two-dimensional earth surface submerging model; and (3) inputting rainfall forecast data serving as driving data and parameter sets into the model, carrying out large-range, quick and high-precision simulation on flooding, and analyzing the flood evolution process, the flood submergence condition and the arrival time.
Flood risk analysis: the method is used for carrying out flood disaster risk assessment on the area, is dynamically connected with flood disaster flood evolution simulation, a social and economic database and the like, rapidly and real-timely assesses the flood situations and loss size and distribution, carries out risk assessment and zoning on the flood disasters, comprises loss assessment, risk zoning and disaster risk levels, and strives for precious time for making and implementing response schemes for transferring people, property and the like in advance.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. A digital twin analysis method for flood disaster prevention in extreme rainstorm weather is characterized by comprising the following steps;
step one, building a data base plate, namely building the data base plate based on digital twin construction according to water conservancy basic data, high-precision geographic space data, dynamic monitoring data, business management data and cross-industry shared data;
step two, rainfall forecast, which provides various rainfall forecast modes;
step three, flood forecasting, namely adopting a numerical model to forecast the flood according to the rainfall forecast data obtained in the step two, and further forecasting whether the area is subjected to flood disasters;
evaluating and early warning, namely automatically dividing early warning grades according to the prediction result of the step three, and simultaneously feeding back the early warning result to relevant flood prevention departments for the first time to ensure the safety flood control of the region;
step five, simulating rehearsal, namely calling a model library flood evolution model based on a flood forecast result by taking a digital base as a carrier, combining a hydraulic engineering scheduling process of the area to perform flood disaster rehearsal, and further supporting flood control scheduling consultation decision of the area by utilizing the condition of integrated dynamic rehearsal of the area forecast scheduling;
and step six, managing the plan, and on the basis that the system automatically and comprehensively analyzes the basic conditions of the area, according to the feedback result of the step five, comprehensively comparing, analyzing and evaluating personnel, economic losses and the like which are possibly influenced after the implementation of the plan by combining the historical scheduling plan and the effect, comprehensively evaluating and optimizing the scheduling plan, and starting the corresponding plan grade to form the coping plan of the flood disaster scene through multi-plan comparison.
2. The digital twin analysis method for flood disaster prevention in extreme stormy weather according to claim 1, wherein: the second step includes the following three rainfall forecasting modes;
the method comprises the following steps of firstly, forecasting rainfall by adopting a machine learning model according to long-sequence actual measurement rainfall data of an area where the rainfall occurs;
the method II comprises the steps of utilizing a rainfall influence knowledge graph to carry out similarity analysis, and automatically recommending historical similar rainfall fields and rainfall processes by a system to carry out rainfall forecast;
in a third mode, combining the change trend of a weather system, a user self-defines the rainfall area and intensity according to personal expert experience, or performs rainfall forecast by amplifying and reducing the rainfall area and intensity in a same ratio on similar rainfall fields; and (4) forecasting rainfall through various rainfall forecasting methods, judging whether extreme rainfall occurs in the area in the future days, if so, performing flood forecasting in the third step, and if not, continuing to perform rainfall forecasting in the area.
3. The digital twin analysis method for flood disaster prevention in extreme stormy weather according to claim 1, wherein: and in the third step, if flood disasters occur, the fourth step is carried out for early warning, and if not, the first step is returned to continue rainfall forecast.
4. The digital twin analysis method for flood disaster prevention in extreme stormy weather according to claim 1, wherein: the rainfall forecast comprises a machine learning model, a rainfall influence knowledge graph and a user-defined rainfall intensity.
5. The method for digital twin analysis of flood disaster protection in extreme stormy weather according to claim 4, wherein: the machine learning model comprises contents such as neural network and machine learning, analysis and research are carried out based on data of a single station, the single station is a manual sampling point or an automatic monitoring station, finally, after forecasting characteristic factors are selected, factor values of the previous day are added into modeling data every day, modeling is carried out again, and dynamic updating of a forecasting system is achieved.
6. The digital twin analysis method for flood disaster prevention in extreme stormy weather according to claim 1, wherein: the numerical model in the third step is as follows: the flood forecast numerical model mainly comprises an interval basin rainfall runoff forecast scheme and a main flow river flood forecast scheme, wherein the rainfall runoff forecast comprises the following steps: rainfall runoff forecast is carried out according to the interval rainfall falling, the content of the rainfall runoff forecast is mainly used for forecasting the production convergence process of the interval rainfall falling, a certain forecast period is provided, and forecast accuracy is relatively high; forecasting river flood: river flood forecasting is carried out according to upstream incoming water, the content is used under the condition of no interval rainfall, the forecasting precision is high, the forecasting period is limited by the propagation time of river flood, and the forecasting period is relatively short.
7. The digital twin analysis method for flood disaster prevention in extreme stormy weather according to claim 1, wherein: flood disaster flood evolution simulation: coupling the one-dimensional hydrodynamic model and the two-dimensional earth surface submerging model; and (3) inputting rainfall forecast data serving as driving data and parameter sets into the model, carrying out large-range, quick and high-precision simulation on flooding, and analyzing the flood evolution process, the flood submergence condition and the arrival time.
8. The digital twin analysis method for flood disaster protection in extreme stormy weather according to claim 1, wherein: flood risk analysis: the method is used for evaluating the flood disaster risk of the area, is dynamically connected with flood disaster flood evolution simulation, a social and economic database and the like, quickly evaluates the flood situations and losses in real time and distributes the flood situations and the losses, carries out risk evaluation and zoning on the flood disasters, comprises loss evaluation, risk zoning and disaster situation risk levels, and strives for precious time for making and implementing response schemes for transferring people, property and the like in advance.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113222283A (en) * | 2021-05-31 | 2021-08-06 | 中国水利水电科学研究院 | Mountain torrent forecasting and early warning method and system based on digital twin |
| CN116738766A (en) * | 2023-08-11 | 2023-09-12 | 安徽金海迪尔信息技术有限责任公司 | Intelligent agriculture online industrialization service system based on digital twinning |
| CN116933217A (en) * | 2023-09-19 | 2023-10-24 | 广州华水生态科技有限公司 | Method for forecasting flood of medium and small river based on digital twin and AI technology |
| CN117057616A (en) * | 2023-10-11 | 2023-11-14 | 安徽金海迪尔信息技术有限责任公司 | Water conservancy monitoring method and system based on digital twin |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113222283A (en) * | 2021-05-31 | 2021-08-06 | 中国水利水电科学研究院 | Mountain torrent forecasting and early warning method and system based on digital twin |
| CN113222283B (en) * | 2021-05-31 | 2023-12-26 | 中国水利水电科学研究院 | Mountain torrent forecasting and early warning method and system based on digital twinning |
| CN116738766A (en) * | 2023-08-11 | 2023-09-12 | 安徽金海迪尔信息技术有限责任公司 | Intelligent agriculture online industrialization service system based on digital twinning |
| CN116738766B (en) * | 2023-08-11 | 2023-10-13 | 安徽金海迪尔信息技术有限责任公司 | Intelligent agriculture online industrialization service system based on digital twinning |
| CN116933217A (en) * | 2023-09-19 | 2023-10-24 | 广州华水生态科技有限公司 | Method for forecasting flood of medium and small river based on digital twin and AI technology |
| CN116933217B (en) * | 2023-09-19 | 2024-01-30 | 广州华水生态科技有限公司 | Method for forecasting flood of medium and small river based on digital twin and AI technology |
| CN117057616A (en) * | 2023-10-11 | 2023-11-14 | 安徽金海迪尔信息技术有限责任公司 | Water conservancy monitoring method and system based on digital twin |
| CN117057616B (en) * | 2023-10-11 | 2023-12-26 | 安徽金海迪尔信息技术有限责任公司 | Water conservancy monitoring method and system based on digital twin |
| CN117494905A (en) * | 2023-12-27 | 2024-02-02 | 贵州路桥集团有限公司 | Construction method and system for digital counterweight balance arch bridge |
| CN117494905B (en) * | 2023-12-27 | 2024-03-08 | 贵州路桥集团有限公司 | Construction method and system for digital counterweight balance arch bridge |
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