Water conservancy intelligent management system based on digital twinning

Abstract

The invention relates to the technical field of water conservancy management, and discloses a water conservancy intelligent management system based on digital twinning, and the system comprises an intelligent knowledge base module which is used for obtaining water conservancy data, carrying out the classification and integration of the water conservancy data, and generating a multi-layer knowledge graph, the water conservancy data comprises water conservancy literature data, water conservancy historical data and water conservancy real-time monitoring data; the algorithm optimization module is used for training and verifying the digital twinborn model and optimizing the digital twinborn model based on a deep learning model; the intelligent monitoring and early warning module is used for monitoring and analyzing the running state data of the water conservancy facilities and the water conservancy real-time monitoring data in real time, and carrying out risk early warning when a risk is detected; and the intelligent decision module is used for analyzing the water conservancy data and generating a water conservancy decision. According to the invention, artificial intelligence is utilized to enable the digital twinning technology, and popularization and application of the digital twinning technology in the water conservancy industry can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy management, and in particular to a water conservancy intelligent management system based on digital twins. Background Art

[0002] Entering the 21st century, with the rapid development of artificial intelligence technology, especially the breakthroughs in machine learning, deep learning and other technologies, unprecedented opportunities have been brought to the innovation of digital twin technology. Artificial intelligence can not only efficiently integrate massive data and build a more accurate knowledge base, but also significantly improve the computing efficiency and prediction accuracy of digital twin models through algorithm optimization technology. In addition, the application of artificial intelligence in operation and maintenance monitoring and intelligent decision support has endowed the digital twin system with the ability of autonomous learning and self-optimization, greatly enhancing the flexibility and adaptability of the system.

[0003] In the water conservancy industry, digital twin technology, as a cutting-edge and highly potential technical means, can provide important technical support for water resources management, flood prevention and disaster reduction, water conservancy project operation and maintenance, etc. However, the implementation cost of traditional digital twin technology is high and the development cycle is long, so it cannot be widely used in the water conservancy industry. Summary of the invention

[0004] In view of this, the present invention provides a water conservancy intelligent management system based on digital twins, which uses artificial intelligence to empower digital twin technology, and can realize the popularization and application of digital twin technology in the water conservancy industry.

[0005] In the first aspect, the present invention provides a water conservancy intelligent management system based on digital twins, which includes: an intelligent knowledge base module, which is used to acquire water conservancy data, classify and integrate the water conservancy data, and generate a multi-layer knowledge graph, wherein the water conservancy data includes water conservancy literature data, water conservancy historical data, and water conservancy real-time monitoring data; an algorithm optimization module, which is used to train and verify the digital twin model, and optimize the digital twin model based on a deep learning model; an intelligent monitoring and early warning module, which is used to monitor and analyze the operating status data of water conservancy facilities and the real-time monitoring data of water conservancy, and issue a risk warning when a risk is detected; an intelligent decision-making module, which is used to analyze water conservancy data and generate water conservancy decisions.

[0006] In this implementation, the application realizes the deep integration of artificial intelligence and digital twin technology, and can perform knowledge fusion, model optimization, fault diagnosis, early warning response and auxiliary decision-making according to real-time monitoring data and dynamic external data sources, which greatly simplifies the development of algorithms in water conservancy management systems. Among them, the intelligent knowledge base module builds a comprehensive and dynamic intelligent knowledge base, forms a multi-level knowledge graph, has adaptive expansion capabilities, can dynamically increase knowledge graph nodes and knowledge associations as business grows, effectively respond to complex and changing water conservancy management environments, and improve data utilization efficiency and query speed. Among them, the algorithm optimization module introduces machine learning algorithms and deep learning models to deeply optimize the key calculation processes in the digital twin model to ensure the high accuracy and high stability of machine learning algorithms in complex environments. Among them, the intelligent monitoring and early warning module quickly identifies and classifies abnormal data. When potential risks or faults are detected, the system will automatically trigger the early warning mechanism to improve the operational safety of the water conservancy system. Among them, the intelligent decision-making module helps managers to have an in-depth understanding of the operating status and potential problems of the water conservancy system through multi-dimensional data analysis and comparison, and improves the decision-making efficiency and quality of water conservancy managers. The water conservancy intelligent management system has a highly intelligent process response capability, is suitable for the complex environment in the field of water conservancy management, and provides strong technical support for the intelligent development of the industry, realizing the popularization and application of digital twin technology in the water conservancy industry.

[0007] In an optional implementation, the water conservancy data is classified and integrated to generate a multi-layer knowledge graph, including: using a vector space model and embedding technology to vectorize and segment the water conservancy data to obtain water conservancy vector data; using a natural language processing model to extract keywords that represent the water conservancy data from the water conservancy vector data; and classifying and archiving the water conservancy data based on the keywords to generate a multi-layer knowledge graph.

[0008] In this implementation, the intelligent knowledge base module of the present application builds a comprehensive and dynamic intelligent knowledge base, adopts natural language processing models and knowledge graph technology, automatically extracts and integrates key knowledge and knowledge rules from information sources such as water conservancy professional literature, historical data, real-time monitoring data, etc., to form a multi-level knowledge graph with adaptive expansion capabilities. It can dynamically increase knowledge graph nodes and knowledge associations as business grows, effectively respond to the complex and changeable water conservancy management environment, and improve data utilization efficiency and query speed.

[0009] In an optional implementation, optimizing the digital twin model based on the deep learning model includes: acquiring water conservancy business data; and fine-tuning the water conservancy corpus using a water conservancy fine-tuning model based on the water conservancy business data.

[0010] In an optional implementation, optimizing the digital twin model based on a deep learning model includes: fusing multiple layers of knowledge graphs using retrieval enhancement generation technology, and performing label classification and matching on data in the multiple layers of knowledge graphs.

[0011] In an optional implementation, the data twin model includes a hydrological simulation model, a water quality prediction model, and a flood evolution model.

[0012] In this implementation, the algorithm optimization module of this application introduces machine learning algorithms and deep learning models to target the complex and changeable characteristics of the water conservancy system, and deeply optimizes key computing processes such as hydrological simulation, water quality prediction, and flood evolution in the digital twin model. Through the training and verification of large-scale data sets, the high accuracy and high stability of machine learning algorithms in complex environments are ensured. At the same time, the algorithm optimization module built into the water conservancy system can automatically adjust the algorithm parameters and structure according to real-time data feedback and model performance evaluation results, so as to achieve continuous optimization and self-improvement of the model.

[0013] In an optional embodiment, the intelligent monitoring and early warning module includes a monitoring unit and an early warning unit, wherein the monitoring unit is used to use the Internet of Things technology to monitor the operating status data of the water conservancy system and the water conservancy real-time monitoring data in real time; identify the operating status data and the water conservancy real-time monitoring data, and when abnormal data is detected, classify and identify the current abnormal data to obtain the current data abnormality type; the early warning unit is used to issue an early warning according to the data abnormality type when the current abnormal data is detected.

[0014] In an optional embodiment, when a data anomaly is detected, issuing an early warning based on the data anomaly type includes: constructing an early warning mechanism, the early warning mechanism including the priority of the data anomaly type and the early warning strategy corresponding to the data anomaly type; when current abnormal data is detected, identifying the current data anomaly type, and issuing an early warning based on the priority and the early warning strategy.

[0015] In an optional embodiment, the intelligent monitoring and early warning module also includes a fault diagnosis unit, wherein the fault diagnosis unit is used to obtain historical abnormal data and corresponding historical data abnormal causes from water conservancy historical data; compare and analyze the current abnormal data with the historical abnormal data to determine the current data abnormal cause of the current abnormal data.

[0016] In this implementation, the intelligent monitoring and early warning module of the water conservancy intelligent management system of this application uses the Internet of Things technology to monitor the operating status and environmental parameters of water conservancy facilities in real time, and combines big data analysis technology to quickly identify and classify abnormal data. When potential risks or failures are detected, the system will automatically trigger the early warning mechanism, notify management personnel through various means such as emails and text messages, and provide detailed fault analysis reports and emergency handling suggestions. In addition, the system also supports remote control and fault diagnosis functions to help managers quickly respond to and solve problems, and improve the operational safety of water conservancy systems.

[0017] In an optional implementation, the intelligent decision-making module includes an intelligent recommendation unit, wherein the intelligent recommendation unit is used to generate water conservancy decisions based on water conservancy literature data and water conservancy historical data; and adjust water conservancy decisions using water conservancy real-time monitoring data.

[0018] In an optional embodiment, the intelligent decision-making module includes a visualization unit and a data mining unit, wherein the visualization unit is used to visualize the water conservancy data; the data mining unit is used to analyze and compare multi-dimensional water conservancy data to detect the operating status information and potential problem information of the water conservancy system.

[0019] In this implementation, the intelligent monitoring and early warning module of the water conservancy intelligent management system of this application uses the Internet of Things technology to monitor the operating status and environmental parameters of water conservancy facilities in real time, and combines big data analysis technology to quickly identify and classify abnormal data. When potential risks or failures are detected, the system will automatically trigger the early warning mechanism, notify management personnel through various means such as emails and text messages, and provide detailed fault analysis reports and emergency handling suggestions. In addition, the system also supports remote control and fault diagnosis functions to help managers quickly respond to and solve problems, and improve the operational safety of water conservancy systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 is a schematic diagram of water conservancy management based on digital twin according to an embodiment of the present invention;

[0022] Figure 2 It is a schematic diagram of another water conservancy management based on digital twin according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0024] The water conservancy industry needs to continuously promote the improvement of the business governance system and governance capabilities of the water conservancy industry, and around application scenarios such as water safety, water resources, water environment, water ecology, water engineering, water supervision, water culture, and water administration, it needs to use big data, artificial intelligence, Internet of Things, 5G, digital twin technology, remote sensing, edge computing, mobile Internet, drones, unmanned ships and other new generation information technologies. Among them, digital twin technology can provide important technical support for many fields such as water resources management, flood prevention and disaster reduction, and water conservancy project operation and maintenance. However, the widespread application of traditional digital twin technology faces significant technical challenges: its construction and operation are highly dependent on complex algorithm systems and in-depth data analysis capabilities, which not only requires extremely high technical barriers, but also leads to high implementation costs and long development cycles, thereby limiting the popularization and application of this technology in a wider range of scenarios. Therefore, this application provides a water conservancy intelligent management system based on digital twins, combining artificial intelligence with digital twin technology, and artificial intelligence empowers digital twin technology, which can realize the popularization and application of digital twin technology in the water conservancy industry.

[0025] According to an embodiment of the present invention, a water conservancy intelligent management system based on digital twin is provided. Figure 1 , Figure 1 It is a schematic diagram of a water conservancy intelligent management system based on digital twin according to an embodiment of the present invention. The water conservancy intelligent management system based on digital twin includes an intelligent knowledge base module, an algorithm optimization module, an intelligent monitoring and early warning module and an intelligent decision-making module, forming a closed-loop management system from data collection, analysis, early warning to decision support.

[0026] The intelligent knowledge base module is used to acquire water conservancy data, classify and integrate the water conservancy data, and generate a multi-layer knowledge graph.

[0027] Among them, water conservancy data include water conservancy literature data, water conservancy historical data and water conservancy real-time monitoring data.

[0028] Among them, water conservancy historical data includes historical monitoring data, historical abnormal data, historical abnormal cause data, etc.

[0029] Among them, water conservancy real-time monitoring data includes water conservancy real-time internal monitoring data and water conservancy real-time external monitoring data. Water conservancy real-time internal monitoring data refers to the internal data of the water conservancy system, and water conservancy real-time external monitoring data refers to external environmental data, such as meteorological data, ocean current data, etc.

[0030] In one implementation, the water conservancy data also includes expert experience data.

[0031] Specifically, the intelligent knowledge base module adopts a method that combines the NLP (Natural Language Processing) model with knowledge graph technology to automatically extract and integrate knowledge information from information sources such as water conservancy literature data, water conservancy historical data, and water conservancy real-time monitoring data to form a multi-level knowledge graph.

[0032] The algorithm optimization module is used to train and verify the digital twin model and optimize the digital twin model based on the deep learning model.

[0033] Among them, the digital twin model includes key models in the calculation process of water conservancy system operation. The data twin model includes hydrological simulation model, water quality prediction model, flood evolution model, etc.

[0034] Specifically, an adaptive multi-model collaborative optimization mechanism is introduced into the algorithm optimization module, and a balance between accuracy and stability is achieved in complex environments through integrated training of multiple machine learning algorithms and deep learning models.

[0035] The intelligent monitoring and early warning module is used to monitor and analyze the operating status data of the water conservancy system and the real-time monitoring data of water conservancy in real time, and issue risk warnings when risks are detected.

[0036] Specifically, the intelligent monitoring and early warning module integrates Internet of Things technology to monitor the operating status and water environmental parameters of each facility in the water conservancy system in real time, and quickly identifies and classifies abnormal data through a variety of machine learning algorithms to ensure the stable operation of the digital twin model.

[0037] Among them, the Internet of Things (IoT) connects any object to the network through information sensing devices according to agreed protocols. Objects exchange and communicate information through information dissemination media to achieve intelligent identification, positioning, tracking, supervision and other functions.

[0038] Among them, various machine learning algorithms include decision trees, random forests, etc.

[0039] The intelligent decision-making module is used to analyze water conservancy data and generate water conservancy decisions.

[0040] See also Figure 2 , Figure 2 It is a schematic diagram of another water conservancy intelligent management system based on digital twin according to an embodiment of the present invention.

[0041] In one implementation, the intelligent knowledge base module includes a vector processing unit.

[0042] The vector processing unit is used to construct a vector knowledge base based on water conservancy data, based on the vector space model and embedding technology. The intelligent knowledge base module uses the vector space model and embedding technology to vectorize and segment the water conservancy data to obtain water conservancy vector data.

[0043] Specifically, the water conservancy data text is converted into vectors in high-dimensional space based on the vector space model, and the high-dimensional sparse feature vector is converted into a low-dimensional dense word vector using the embedding technology. The water conservancy data is similarity calculated by combining the vector space model and the embedding technology, and the water conservancy data is automatically vectorized and segmented to obtain water conservancy vector data. The water conservancy vector data is then stored.

[0044] In one implementation, the intelligent knowledge base module includes a knowledge graph unit.

[0045] The knowledge graph unit is also used to extract keywords representing water conservancy data from water conservancy vector data using a natural language processing model, classify and archive the water conservancy data based on the keywords, and generate a multi-layer knowledge graph.

[0046] Specifically, the keyword detection algorithm of the natural language processing model can be one or more of the TF-IDF (term frequency–inverse document frequency) algorithm, the LDA (Latent Dirichlet Allocation) algorithm, the TextRank algorithm, the KeyBERT algorithm, etc.

[0047] In one implementation, the TF-IDF algorithm is used to extract keywords from the water conservancy data. TF-IDF is a weighted technology used for information retrieval and text mining. The TF-IDF algorithm is used to evaluate the importance of a word to a water conservancy data and extract the most important keywords.

[0048] Furthermore, water conservancy data are intelligently classified and archived based on keywords to obtain a multi-level knowledge graph about water conservancy data.

[0049] In one implementation, water conservancy data is intelligently classified and archived using the business fields of keywords; in another implementation, water conservancy data is intelligently classified and archived using the corresponding business processes of keywords; in another implementation, water conservancy data is intelligently classified and archived using the importance of keywords.

[0050] In this implementation, knowledge graph technology is used to organize fragmented information into a structured knowledge system to provide rich and accurate semantic information support for the digital twin model.

[0051] The intelligent knowledge base module is also used to detect and update water conservancy data in real time, and use the updated water conservancy data to update the data knowledge base and adaptively adjust the knowledge graph.

[0052] Specifically, knowledge graph nodes are added according to the updated water conservancy data, or knowledge associations between nodes are added.

[0053] In this implementation, with the continuous accumulation of water conservancy data and continuous optimization of algorithms, the knowledge base of this application can achieve self-learning and evolution, and can ensure the prediction and analysis capabilities of subsequent digital twin models.

[0054] The intelligent knowledge base module of this application builds a comprehensive and dynamic intelligent knowledge base, adopts natural language processing models and knowledge graph technology, automatically extracts and integrates key knowledge and knowledge rules from information sources such as water conservancy professional literature, historical data, real-time monitoring data, etc., to form a multi-level knowledge graph with adaptive expansion capabilities. It can dynamically increase knowledge graph nodes and knowledge associations as business grows, effectively respond to the complex and changeable water conservancy management environment, and improve data utilization efficiency and query speed.

[0055] In one implementation, the algorithm optimization module includes a fine-tuning unit.

[0056] The fine-tuning unit is used to obtain water conservancy business data, and based on the water conservancy business data, fine-tune the water conservancy corpus using the water conservancy fine-tuning model.

[0057] Specifically, based on the fine-tuning model of water conservancy specialty of Qwen2.5:7b, combined with the water conservancy corpus, the digital twin model is fine-tuned according to the corresponding water conservancy business. In actual use, when fine-tuning the digital twin model, the feedback data of the water conservancy business is obtained in real time, and the data twin model is further fine-tuned according to the feedback data, so that the digital twin model can adapt to the dynamic changes of the water conservancy business.

[0058] In one implementation, the algorithm optimization module includes a retrieval enhancement generation unit.

[0059] The retrieval enhancement generation unit is used to fuse multi-layer knowledge graphs using retrieval enhancement generation technology to perform label classification and matching on the data in the multi-layer knowledge graphs.

[0060] Among them, Retrieval-Augmented Generation (RAG) technology is an artificial intelligence technology that combines information retrieval technology with language generation models. It retrieves relevant information from external knowledge bases and inputs it into large language models (LLMs) as prompts to enhance the model's ability to handle knowledge-intensive tasks such as question answering, text summarization, and content generation.

[0061] Specifically, the retrieval enhancement generation technology is combined with the multi-layer knowledge graph to retrieve water conservancy expertise from the multi-layer knowledge graph, and the water conservancy expertise is labeled and automatically matched. When generating an answer, the model will first analyze the question and match the relative knowledge base.

[0062] In this implementation, integrating the knowledge graph structure into the retrieval enhancement generation technology can improve the retrieval efficiency and response speed of the digital twin model, and can reduce the burden of large-scale data redundancy and retrieval query delay.

[0063] The algorithm optimization module of this application introduces machine learning algorithms and deep learning models to target the complex and changeable characteristics of the water conservancy system, and deeply optimizes key computing processes such as hydrological simulation, water quality prediction, and flood evolution in the digital twin model. Through the training and verification of large-scale data sets, the high accuracy and high stability of machine learning algorithms in complex environments are ensured. At the same time, the algorithm optimization module built into the water conservancy system can automatically adjust the algorithm parameters and structure according to real-time data feedback and model performance evaluation results, so as to achieve continuous optimization and self-improvement of the model.

[0064] In one implementation, the intelligent monitoring and early warning module includes a monitoring unit and an early warning unit.

[0065] Among them, the monitoring unit is used to use the Internet of Things technology to monitor the operating status data of the water conservancy system and the water conservancy real-time monitoring data in real time, identify the operating status data and the water conservancy real-time monitoring data, and when abnormal data is detected, classify and identify the current abnormal data to obtain the current data abnormal type.

[0066] Specifically, historical parameters are used to train machine learning algorithms, and the Internet of Things technology is used to monitor and collect various parameters of the water conservancy system in real time, such as operating status data, water conservancy environmental parameters, etc. The trained machine learning algorithm is used to identify the parameters in real time to detect whether there is abnormal data. When abnormal data exists, it is classified according to the data characteristics of the abnormal data to determine the specific type of the abnormal data, so as to facilitate subsequent processing of the abnormal data.

[0067] Among them, the early warning unit is used to issue an early warning according to the data abnormality type when current abnormal data is detected.

[0068] Among them, the warning methods include sound warnings, sending emails to managers, text messages and other warning methods.

[0069] In one implementation, an early warning mechanism is constructed, which includes the priority of the data anomaly type and the early warning strategy corresponding to the data anomaly type; when current abnormal data is detected, the current data anomaly type is identified and an early warning is issued according to the priority and the early warning strategy.

[0070] Specifically, a multi-channel early warning mechanism is built. Through multi-dimensional analysis of abnormal data, a multi-channel risk early warning framework is built, and different priorities and notification channels are set for different types of abnormal data to ensure that managers can quickly understand the severity of the risk.

[0071] In one implementation, the intelligent monitoring and early warning module also includes a fault diagnosis unit.

[0072] Among them, the fault diagnosis unit is used to obtain historical abnormal data and corresponding historical data abnormal causes from water conservancy historical data; compare and analyze the current abnormal data with the historical abnormal data to determine the current data abnormal cause of the current abnormal data.

[0073] Specifically, real-time diagnosis and distributed early warning functions. After abnormality detection, the fault diagnosis unit will analyze the historical fault information of the equipment through the local intelligent knowledge base, and provide fault root cause analysis and quick solutions by comparing the current situation, effectively improving the accuracy and response speed of early warning.

[0074] Furthermore, a fault analysis report and emergency handling suggestions are sent to management personnel at the same time as the early warning.

[0075] In this implementation, the intelligent monitoring and early warning module of the water conservancy intelligent management system of this application uses the Internet of Things technology to monitor the operating status and environmental parameters of water conservancy facilities in real time, and combines big data analysis technology to quickly identify and classify abnormal data. When potential risks or failures are detected, the system will automatically trigger the early warning mechanism, notify management personnel through various means such as emails and text messages, and provide detailed fault analysis reports and emergency handling suggestions. In addition, the system also supports remote control and fault diagnosis functions to help managers quickly respond to and solve problems, and improve the operational safety of water conservancy systems.

[0076] In one implementation, the intelligent decision-making module includes an intelligent recommendation unit.

[0077] Among them, the intelligent recommendation unit is used to generate water conservancy decisions based on water conservancy literature data and water conservancy historical data; and to adjust water conservancy decisions using real-time water conservancy monitoring data.

[0078] Specifically, based on historical data, real-time data and expert experience data, the intelligent recommendation unit is used to dynamically generate personalized intelligent decision-making suggestions. When the real-time water conservancy monitoring data changes, the decision-making suggestion content is adaptively adjusted in combination with the latest monitoring data.

[0079] In one implementation, the intelligent decision module includes a visualization unit and a data mining unit.

[0080] Among them, the visualization unit is used to visualize the water conservancy data.

[0081] Specifically, the visualization unit has a collaborative analysis function. By conducting correlation analysis and comparison on multi-source water conservancy data, the water conservancy data can be visualized in the form of charts, reports, etc. The intuitive and easy-to-understand visualization method can help managers comprehensively consider various influencing factors and improve the scientificity and practicality of decision-making.

[0082] Among them, the data mining unit is used to analyze and compare multi-dimensional water conservancy data and detect the operating status information and potential problem information of the water conservancy system.

[0083] Specifically, the data mining unit further mines data information that is not currently faulty but has potential risks. It compares multi-dimensional water conservancy data, analyzes the changing patterns and trends of water conservancy data, predicts water conservancy data in the future, prevents potential problems in advance, generates data reports based on the detected status information and potential problem information, and uses machine learning algorithms or manual work to adjust the water conservancy system in a timely manner.

[0084] In this implementation, the intelligent decision-making module of the water conservancy intelligent management system of this application has multiple functions such as visualization analysis, data mining, and intelligent recommendation, which can transform complex water conservancy data into intuitive and easy-to-understand charts and reports. Through multi-dimensional data analysis and comparison, it helps managers to have an in-depth understanding of the operating status and potential problems of the water conservancy system. At the same time, the intelligent recommendation system can provide managers with targeted decision-making suggestions and program optimization suggestions based on historical data and expert experience, helping them to make more scientific and reasonable decisions, and can improve the decision-making efficiency and quality of water conservancy managers.

[0085] This application realizes the deep integration of artificial intelligence and digital twin technology, and can perform knowledge fusion, model optimization, fault diagnosis, early warning response and auxiliary decision-making based on real-time monitoring data and dynamic external data sources, which greatly simplifies the development of algorithms in water conservancy management systems. Among them, the intelligent knowledge base module builds a comprehensive and dynamic intelligent knowledge base, forms a multi-level knowledge graph, has adaptive expansion capabilities, can dynamically increase knowledge graph nodes and knowledge associations as business grows, effectively respond to complex and changing water conservancy management environments, and improve data utilization efficiency and query speed. Among them, the algorithm optimization module introduces machine learning algorithms and deep learning models to deeply optimize the key calculation processes in the digital twin model to ensure the high accuracy and high stability of machine learning algorithms in complex environments. Among them, the intelligent monitoring and early warning module quickly identifies and classifies abnormal data. When potential risks or faults are detected, the system will automatically trigger the early warning mechanism to improve the operational safety of the water conservancy system. Among them, the intelligent decision-making module helps managers to have an in-depth understanding of the operating status and potential problems of the water conservancy system through multi-dimensional data analysis and comparison, and improves the decision-making efficiency and quality of water conservancy managers. The water conservancy intelligent management system has a highly intelligent process response capability, is suitable for the complex environment in the field of water conservancy management, and provides strong technical support for the intelligent development of the industry, realizing the popularization and application of digital twin technology in the water conservancy industry.

[0086] This application uses an artificial intelligence low-code development platform, so non-professional programmers can quickly build and optimize complex algorithm models, significantly reducing technical barriers and labor costs, and accelerating the process of water conservancy intelligence. According to preliminary estimates, compared with traditional development methods, the low-code development model of the present invention can improve algorithm development efficiency by more than 50%.

[0087] This application has made a fundamental innovation to the traditional knowledge base and introduced a new type of vector knowledge base technology. It not only breaks the limitations of traditional relational databases and realizes the seamless integration of all files of the same project in the same data warehouse, but also greatly simplifies the establishment and maintenance process of the data warehouse. Without a deep knowledge base of programming data warehouses such as C++, Hadoop, and Storm, you can easily build an efficient and flexible data storage and retrieval system. This method not only reduces the technical difficulty, but also promotes the sharing and utilization of data resources, providing more comprehensive and accurate data support for water conservancy management.

[0088] This application achieves the optimal allocation and efficient use of water resources by improving the intelligence level of the water management system, effectively alleviating social problems such as water shortage and flood disasters. At the same time, the application of intelligent operation and maintenance monitoring and early warning systems reduces the operating risks and maintenance costs of water conservancy facilities, improves overall economic benefits, and significantly improves the efficiency and effectiveness of water resource management.

[0089] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A water conservancy intelligent management system based on digital twins, characterized in that: The system comprises: An intelligent knowledge base module is used to obtain water conservancy data and generate a multi-layer knowledge graph by classifying and integrating the water conservancy data, wherein the water conservancy data includes water conservancy literature data, water conservancy historical data and water conservancy real-time monitoring data; An algorithm optimization module, used to train and verify the digital twin model based on the deep learning model, and optimize the digital twin model; An intelligent monitoring and early warning module is used to monitor and analyze the operating status data of the water conservancy system and the water conservancy real-time monitoring data in real time, and issue a risk early warning when a risk is detected; The intelligent decision-making module is used to analyze the water conservancy data and generate water conservancy decisions.

2. The water conservancy intelligent management system based on digital twin according to claim 1 is characterized in that: The classifying and integrating the water conservancy data to generate a multi-layer knowledge graph includes: Using a vector space model and embedding technology to perform vector segmentation processing on the water conservancy data to obtain water conservancy vector data; Extracting keywords representing the water conservancy data from the water conservancy vector data using a natural language processing model; The water conservancy data is classified and archived based on the keywords to generate the multi-layer knowledge graph.

3. The water conservancy intelligent management system based on digital twin according to claim 1 is characterized in that: The optimization of the digital twin model based on the deep learning model includes: Obtain water conservancy business data; Based on the water conservancy business data, the water conservancy fine-tuning model is used to fine-tune the water conservancy corpus.

4. The water conservancy intelligent management system based on digital twin according to claim 1 is characterized in that: The optimization of the digital twin model based on the deep learning model includes: The multi-layer knowledge graph is fused using retrieval enhancement generation technology to perform label classification and matching on the data in the multi-layer knowledge graph.

5. The water conservancy intelligent management system based on digital twin according to claim 1 is characterized in that: The data twin model includes a hydrological simulation model, a water quality prediction model, and a flood evolution model.

6. The water conservancy intelligent management system based on digital twin according to claim 1 is characterized in that: The intelligent monitoring and early warning module includes a monitoring unit and an early warning unit, wherein: The monitoring unit is used to monitor the operating status data of the water conservancy system and the water conservancy real-time monitoring data in real time using the Internet of Things technology; Identify the operation status data and the water conservancy real-time monitoring data, and when abnormal data is detected, classify and identify the current abnormal data to obtain the current data abnormal type; The early warning unit is used to issue an early warning according to the data abnormality type when the current abnormal data is detected.

7. The water conservancy intelligent management system based on digital twin according to claim 6 is characterized in that: When data anomaly is detected, issuing an early warning according to the data anomaly type includes: Constructing an early warning mechanism, the early warning mechanism including the priority of the data anomaly type and the early warning strategy corresponding to the data anomaly type; When the current abnormal data is detected, the abnormal type of the current data is identified, and an early warning is issued according to the priority and the early warning strategy.

8. The water conservancy intelligent management system based on digital twin according to claim 7 is characterized in that: The intelligent monitoring and early warning module also includes a fault diagnosis unit, wherein: A fault diagnosis unit, used for obtaining historical abnormal data and corresponding abnormal reasons of the historical data from the water conservancy historical data; The current abnormal data and the historical abnormal data are compared and analyzed to determine the current data abnormality cause of the current abnormal data.

9. The water conservancy intelligent management system based on digital twin according to claim 1 is characterized in that: The intelligent decision-making module includes an intelligent recommendation unit, wherein: An intelligent recommendation unit, used for generating water conservancy decisions based on the water conservancy literature data and the water conservancy historical data; The water conservancy real-time monitoring data is used to adjust the water conservancy decision.

10. The water conservancy intelligent management system based on digital twin according to claim 1 is characterized in that: The intelligent decision-making module includes a visualization unit and a data mining unit, wherein: The visualization unit is used to perform visualization processing on the water conservancy data; The data mining unit is used to analyze and compare the multi-dimensional water conservancy data to detect the operating status information and potential problem information of the water conservancy system.

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