Digital twin hydraulic engineering dynamic prediction and simulation method based on AI technology

Abstract

The invention belongs to the technical field of digital twinning and water conservancy management, and discloses a digital twinning water conservancy dynamic prediction and simulation method based on an AI technology, the dynamic prediction and simulation method is realized based on a digital twinning platform, and the specific steps are as follows: S1, constructing a digital twinning scene; s2, preparing model prediction training data; s3, training a prediction model; s4, realizing future prediction; and S5, performing future digital twinning simulation. By introducing the AI learning model and constructing the three-dimensional water conservancy digital simulation scene, a large amount of data is provided for the model, the model is further enabled to automatically learn and extract a complex nonlinear relationship from a large amount of historical data and real-time monitoring data, the prediction precision of hydrological data is remarkably improved, and the prediction efficiency of the hydrological data is improved. Compared with traditional static prediction, the method not only can adaptively adjust prediction parameters, but also can perform dynamic adjustment according to continuously updated environment data, and has higher real-time performance and reliability.

Description

Technical Field

[0001] The present invention belongs to the field of digital twin and water conservancy management technology, and specifically is a digital twin water conservancy dynamic prediction and simulation method based on AI technology. Background Art

[0002] Digital twin technology is to model entities, processes or systems in the real world in digital form, and realize all-round simulation and management of the physical world by integrating multiple cutting-edge technologies such as the Internet of Things, cloud computing, artificial intelligence, big data, etc. This technology uses sensors, data analysis, modeling and simulation, etc. to map the state and behavior of physical entities to the virtual space in real time, forming a corresponding digital model, thereby realizing the monitoring, prediction and optimization of the physical world. AI technology, namely artificial intelligence technology, is a new technical science used to simulate, extend and expand certain human thinking processes and intelligent behaviors.

[0003] At present, in the field of water conservancy management, traditional methods are difficult to cope with complex dynamic changes. They are usually based on physical models, statistical regression, empirical formulas and numerical weather forecasting techniques. Although these methods are effective in some cases, they have limitations. With climate change, accelerated urbanization and increasingly complex water conservancy projects, traditional static models cannot accurately predict hydrological data such as water level and flow. In addition, the rapidly changing environment and sudden extreme weather events have put higher demands on the scheduling and management of water resources. Therefore, a more accurate and flexible prediction and simulation method is needed. Summary of the invention

[0004] The purpose of the present invention is to provide a digital twin water conservancy dynamic prediction and simulation method based on AI technology to solve the problems raised in the above background technology.

[0005] In order to achieve the above-mentioned purpose, the present invention provides the following technical solution: a digital twin water conservancy dynamic prediction and simulation method based on AI technology, wherein the dynamic prediction and simulation method is implemented based on a digital twin platform, and the specific steps are as follows:

[0006] S1, building a digital twin scene: using the digital twin platform to build a three-dimensional water conservancy digital simulation scene, and the scene includes a historical restoration module, a current status monitoring module and a future prediction module;

[0007] S2, prepare model prediction training data: extract historical and current scene data in the twin scene, and form a training data set that meets the model requirements after processing;

[0008] S3, training prediction model: using a deep learning model, such as a convolutional neural network, and training based on the data prepared in step S2;

[0009] S4, achieve future prediction: use the trained model to predict hydrological data for future periods and perform scenario simulation based on real-time and future meteorological conditions, socio-economic changes and other parameters;

[0010] S5, future digital twin simulation: According to the prediction results in step S4, the future data is fed back to the digital twin scene, and the simulation effect in the twin scene is controlled to realize the simulation of future data.

[0011] Preferably, the historical and current scene data in S1 mainly include terrain, vegetation, crop types, buildings, land use, roads, rivers, lakes, ditches and water conservancy engineering facilities.

[0012] Preferably, after extracting the scene data in S2, the relevant data needs to be preprocessed, cleaned and formatted to ensure that the data meets the requirements, and the data sources include hydrological monitoring data, meteorological data, remote sensing images and real-time data of IoT devices.

[0013] Preferably, in addition to the water conservancy-related elements displayed in the scene, S1 also needs to collect auxiliary data related to population, meteorological and socio-economic factors, and associate the scene with relevant population, meteorological and socio-economic data through data integration technology.

[0014] Preferably, in the data integration stage, the historical restoration module will associate the historical restoration scenario with the historical data, the current status monitoring module will associate the current status monitoring scenario with the IoT monitoring device, and the current status monitoring scenario data will be converted into the historical scenario according to the simulation cycle.

[0015] Preferably, in S3, the results of the model training need to be debugged, and the model performance needs to be optimized through cross-validation and error analysis to ultimately obtain a high-accuracy model.

[0016] Preferably, the historical restoration module is used to reconstruct the past state of the water conservancy system through historical data, the current status monitoring module obtains current hydrological information based on real-time data, and the future prediction module deduces and warns future hydrological conditions through an AI model.

[0017] Preferably, the method can support multi-level and multi-scenario water conservancy system simulation, allow different water conservancy management schemes to be simulated through multiple simulation models, and provide decision support system functions to help water conservancy management departments select the optimal solution based on simulation results.

[0018] Preferably, in S3, when the model is being trained, dynamic monitoring data and historical data are integrated to dynamically train and dynamically predict the model, and the model is updated in real time so that the data can be fed back to the scene simulation in a timely manner, so that the model can further simulate and predict the real-time changing hydrological state, and has the functions of adaptive adjustment and optimization.

[0019] The beneficial effects of the present invention are as follows:

[0020] 1. The present invention introduces an AI learning model and constructs a three-dimensional water conservancy digital simulation scene to provide the model with a large amount of data, further enabling the model to automatically learn and extract complex nonlinear relationships from a large amount of historical data and real-time monitoring data, thereby significantly improving the prediction accuracy of hydrological data. Compared with traditional static prediction, this method can not only adaptively adjust prediction parameters and handle real-time changing meteorological, geographical and other factors, but also make dynamic adjustments based on constantly updated environmental data, and has stronger real-time performance and reliability.

[0021] 2. Through the introduction of digital twin simulation technology, the present invention enables staff to test the effectiveness of water management plans under different situations through simulation scenarios, thereby facilitating staff to optimize resource allocation. Compared with traditional methods, this dynamic prediction and simulation method can not only improve efficiency in routine management, but also provide decision-making support for water conservancy staff in emergency management through AI prediction models, ensuring the scientificity and rationality of decision-making, and playing an important role.

[0022] 3. The present invention uses digital twin technology to enable the method to simulate the performance of water conservancy facilities in different situations in real time in a virtual environment. In particular, in extreme weather and disaster events, the method can not only predict possible hydrological changes in advance and provide accurate early warning information, but also automatically adjust the simulation results according to real-time data, thereby improving the flexibility and accuracy of the prediction, providing a scientific basis for the scheduling of water conservancy projects and disaster prevention, thereby effectively reducing disaster losses and protecting people’s lives and property. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a dynamic prediction and simulation flow chart of the present invention. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] like Figure 1 As shown, the embodiment of the present invention provides a digital twin water conservancy dynamic prediction and simulation method based on AI technology. The dynamic prediction and simulation method is implemented based on a digital twin platform, and the specific steps are as follows:

[0026] S1, building a digital twin scene: using the digital twin platform to build a three-dimensional water conservancy digital simulation scene, and the scene includes a historical restoration module, a current status monitoring module and a future prediction module;

[0027] S2, prepare model prediction training data: extract historical and current scene data in the twin scene, and form a training data set that meets the model requirements after processing;

[0028] S3, training prediction model: using a deep learning model, such as a convolutional neural network (CNN), and training based on the data prepared in step S2;

[0029] S4, achieve future prediction: use the trained model to predict hydrological data (such as water level, flow, precipitation, etc.) in the future period, and perform scenario simulation based on real-time and future meteorological conditions, socio-economic changes and other parameters;

[0030] This step uses the trained model to predict future data for newly input conditions (time, etc.) and adjusts the simulated values ​​in the twin scenario based on the prediction results.

[0031] S5, future digital twin simulation: According to the prediction results in step S4, the future data is fed back to the digital twin scene, and the simulation effect in the twin scene is controlled to realize the simulation of future data.

[0032] In S1, the digital twin platform is used to build a three-dimensional water conservancy digital simulation scene. The main data contents in the scene are as follows:

[0033]

[0034]

[0035] As shown in the table above, before training the model, the above data needs to be preprocessed, cleaned and formatted to ensure that the format of the above data meets the requirements before training the model.

[0036] By introducing AI learning models and constructing three-dimensional water conservancy digital simulation scenes to provide the model with a large amount of data, the model can be further enabled to automatically learn and extract complex nonlinear relationships from a large amount of historical data and real-time monitoring data, significantly improving the prediction accuracy of hydrological data. Compared with traditional static predictions, this method can not only adaptively adjust prediction parameters and handle real-time changing meteorological, geographical and other factors, but also make dynamic adjustments based on constantly updated environmental data, with stronger real-time and reliability.

[0037] The historical and current scene data in S1 mainly include terrain, vegetation, crop types, buildings, land use, roads, rivers, lakes, ditches, and water conservancy engineering facilities.

[0038] Among them, after extracting the scene data in S2, the relevant data needs to be preprocessed, cleaned and formatted to ensure that the data meets the requirements, and the data sources include hydrological monitoring data, meteorological data, remote sensing images and real-time data of IoT devices.

[0039] In S2, taking water level data as an example, the historical data of a monitoring station, as well as the meteorological, elevation, upstream and downstream station data corresponding to the monitoring data at the time, the population size and economic output value of the area, etc. are extracted and integrated into CSV format files for training data sets and test data sets.

[0040] Among them, in addition to the water conservancy-related elements displayed in the scene, S1 also needs to collect auxiliary data related to population, meteorology and socio-economic factors, such as meteorological station data, river basin topography data, historical water level data, population and economic statistics, etc., and use data integration technology to associate the scene with relevant population, meteorological and socio-economic data (such as meteorological, population, economic indicators, etc.).

[0041] Among them, in the data integration stage, the historical restoration module will associate the historical restoration scene with the historical data, the current status monitoring module will associate the current status monitoring scene with the IoT monitoring device (or use the real-time reported data to associate), and the current status monitoring scene data is converted into historical scenes according to the simulation cycle.

[0042] Among the three groups of modules, future prediction scenarios simulate and feedback future time period data based on AI algorithms for further decision-making and emergency response.

[0043] Among them, in S3, the results of model training need to be debugged, and the model performance needs to be optimized through cross-validation and error analysis to finally obtain a high-accuracy model.

[0044] Among them, the training data set and the test data set are used to train the model, and the model is evaluated, including accuracy, precision, recall rate, etc., and the training is adjusted through data supplementation until the training results are stable.

[0045] Among them, the historical restoration module is used to reconstruct the past state of the water conservancy system through historical data, the current status monitoring module obtains current hydrological information based on real-time data, and the future prediction module uses AI models to deduce and warn of future hydrological conditions.

[0046] Through the introduction of digital twin simulation technology, staff can test the effectiveness of water management plans under different situations through simulation scenarios, thereby facilitating staff to optimize resource allocation. Compared with traditional methods, this dynamic prediction and simulation method can not only improve efficiency in routine management, but also provide decision-making support for water conservancy staff in emergency management through AI prediction models, ensuring the scientificity and rationality of decision-making, and playing an important role.

[0047] Among them, this method can support multi-level and multi-scenario water conservancy system simulation, allow different water conservancy management schemes to be simulated through multiple simulation models (such as climate change models, water resources allocation models, etc.), and provide decision support system (DSS) functions to help water conservancy management departments select the optimal plan based on simulation results.

[0048] Among them, in S3, when the model is trained, the dynamic monitoring data and historical data are integrated to dynamically train and dynamically predict the model, and the model is updated in real time so that the data can be fed back to the scene simulation in time, so that the model can further simulate and predict the real-time changing hydrological state, and has the functions of adaptive adjustment and optimization.

[0049] Through digital twin technology, this method can simulate the performance of water conservancy facilities in different scenarios in real time in a virtual environment. Especially in extreme weather and disaster events, this method can not only predict possible hydrological changes in advance and provide accurate early warning information, but also automatically adjust the simulation results according to real-time data, thereby improving the flexibility and accuracy of the prediction, and providing a scientific basis for the scheduling of water conservancy projects and disaster prevention, thereby effectively reducing disaster losses and protecting people’s lives and property.

[0050] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0051] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A digital twin water conservancy dynamic prediction and simulation method based on AI technology, characterized by: The dynamic prediction and simulation method is implemented based on the digital twin platform, and its specific steps are as follows: S1, building a digital twin scene: using the digital twin platform to build a three-dimensional water conservancy digital simulation scene, and the scene includes a historical restoration module, a current status monitoring module and a future prediction module; S2, prepare model prediction training data: extract historical and current scene data in the twin scene, and form a training data set that meets the model requirements after processing; S3, training prediction model: using a deep learning model, such as a convolutional neural network, and training based on the data prepared in step S2; S4, achieve future prediction: use the trained model to predict hydrological data for future periods and perform scenario simulation based on real-time and future meteorological conditions, socio-economic changes and other parameters; S5, future digital twin simulation: According to the prediction results in step S4, the future data is fed back to the digital twin scene, and the simulation effect in the twin scene is controlled to realize the simulation of future data.

2. According to claim 1, a digital twin water conservancy dynamic prediction and simulation method based on AI technology is characterized by: The historical and current scene data in S1 mainly include terrain, vegetation, crop types, buildings, land use, roads, rivers, lakes, ditches and water conservancy engineering facilities.

3. According to claim 1, a digital twin water conservancy dynamic prediction and simulation method based on AI technology is characterized by: After the scene data is extracted in S2, the relevant data needs to be preprocessed, cleaned and formatted to ensure that the data meets the requirements, and the data sources include hydrological monitoring data, meteorological data, remote sensing images and real-time data of IoT devices.

4. According to claim 1, a digital twin water conservancy dynamic prediction and simulation method based on AI technology is characterized by: In addition to the water conservancy-related elements displayed in the scene, S1 also needs to collect auxiliary data related to population, meteorological and socio-economic factors, and associate the scene with relevant population, meteorological and socio-economic data through data integration technology.

5. According to claim 4, a digital twin water conservancy dynamic prediction and simulation method based on AI technology is characterized by: During the data integration phase, the historical restoration module will associate the historical restoration scenario with the historical data, the current status monitoring module will associate the current status monitoring scenario with the IoT monitoring device, and the current status monitoring scenario data will be converted into the historical scenario according to the simulation cycle.

6. According to claim 1, a digital twin water conservancy dynamic prediction and simulation method based on AI technology is characterized by: In S3, the model training results need to be debugged, and the model performance needs to be optimized through cross-validation and error analysis to ultimately obtain a high-accuracy model.

7. According to claim 1, a digital twin water conservancy dynamic prediction and simulation method based on AI technology is characterized by: The historical restoration module is used to reconstruct the past state of the water conservancy system through historical data, the current status monitoring module obtains current hydrological information based on real-time data, and the future prediction module deduces and warns future hydrological conditions through AI models.

8. According to claim 1, a digital twin water conservancy dynamic prediction and simulation method based on AI technology is characterized by: This method can support multi-level and multi-scenario water system simulation, allow different water management schemes to be simulated through multiple simulation models, and provide decision support system functions to help water management departments select the optimal solution based on simulation results.

9. According to claim 1, a digital twin water conservancy dynamic prediction and simulation method based on AI technology is characterized by: In S3, when the model is being trained, dynamic monitoring data and historical data are integrated to dynamically train and dynamically predict the model, and the model is updated in real time so that the data can be fed back to the scene simulation in a timely manner, so that the model can further simulate and predict the real-time changing hydrological state, and has the functions of adaptive adjustment and optimization.

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