Comprehensive management method and device for water conservancy

Abstract

The invention relates to the technical field of artificial intelligence, and particularly provides a comprehensive management method and device for water conservancy, and the method comprises the following steps: S1, a multi-source heterogeneous data integration platform; s2, a knowledge extraction and understanding module based on a large model; s3, the image recognition and analysis module is used for automatically recognizing and classifying features in the high-resolution remote sensing image; s4, realizing combined calling of the water conservancy professional model by combining a digital twinning technology and a physical simulation engine; s5, interactive document office and knowledge management are carried out; and S6, developing a standardized interface protocol, docking with various wading terminal devices and water conservancy facility control systems, and carrying out remote monitoring and control. Compared with the prior art, the water resource distribution and scheduling strategy can be optimized, the health condition of the river and lake ecosystem is improved, and the safe operation management of the water conservancy project is enhanced.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and specifically provides a comprehensive management method and device for water conservancy. Background Art

[0002] As global climate change intensifies and extreme weather events occur more frequently, water security in countries around the world has been challenged unprecedentedly. How to effectively use modern information technology to improve the accuracy of hydrological monitoring, speed up flood forecasting, and achieve precision irrigation has become one of the issues that need to be addressed urgently.

[0003] In recent years, although many studies have attempted to apply AI technology to the water conservancy field, the actual results are often unsatisfactory due to the lack of customized solutions for specific application scenarios. In addition, the existing water conservancy information system usually has serious information island phenomenon and slow response, which makes it difficult to meet the needs of modern water conservancy management. Summary of the invention

[0004] The present invention aims at solving the above-mentioned deficiencies of the prior art and provides a highly practical comprehensive management method for water conservancy.

[0005] A further technical task of the present invention is to provide a comprehensive water conservancy management device that is rationally designed, safe and applicable.

[0006] The technical solution adopted by the present invention to solve the technical problem is:

[0007] A comprehensive management method for water resources has the following steps:

[0008] S1, Multi-source heterogeneous data integration platform;

[0009] S2, knowledge extraction and understanding module based on large models;

[0010] S3, image recognition and analysis module, used to automatically identify and classify features in high-resolution remote sensing images;

[0011] S4. Combine digital twin technology and physical simulation engine to realize the combined call of water conservancy professional models;

[0012] S5. Conduct interactive document office and knowledge management;

[0013] S6. Develop standardized interface protocols to connect with various water-related terminal equipment and water conservancy facility control systems for remote monitoring and control.

[0014] Furthermore, in step S1, a unified portal is established to receive relevant information from different sensor networks, remote sensing satellite images and other public databases, and cleans and converts the information into a format through a preprocessing algorithm;

[0015] Advanced data fusion algorithms are used to merge and standardize data from different sources. Stream processing technology is introduced to enable rapid processing and analysis of real-time collected data, thereby promptly reflecting current water conditions. An anomaly detection mechanism is established to automatically identify abnormal data points through machine learning methods.

[0016] Furthermore, in step S2, a language model trained with large-scale text is used to perform customized training in combination with professional terminology, business rules and expert experience in the field of water conservancy. Professional terminology trained with a small model is used as input for the large model. The model's understanding of water conservancy expertise is enhanced through knowledge graph technology to decompose complex water conservancy tasks.

[0017] Furthermore, in step S3, an image processing algorithm is used to extract high-quality information from high-resolution remote sensing images, and the latest target detection algorithm is used to achieve accurate positioning and tracking of specific targets, and a report containing analysis results is automatically generated in a templated manner;

[0018] After the user sets the theme, thematic charts of key hydrological and spatial information are dynamically generated, providing users with intuitive and accurate information display.

[0019] Furthermore, in step S4, computing resources are used to simulate and predict in real time for management response; multiple professional models are seamlessly integrated into one platform through API interfaces; and decision strategies are continuously optimized by combining reinforcement learning algorithms;

[0020] With the help of multi-scale similarity algorithm matching technology driven by large models, the similarity matching of watershed scenarios based on historical scheduling processes and preset deduction processes can be achieved, thereby assisting in the formulation of more effective flood control plans.

[0021] Furthermore, in step S5, a document with complete structure and rich content is automatically generated based on a small amount of input information provided by the user; the user is allowed to have multiple rounds of dialogue with the system through natural language to gradually improve the document content; and the recall retrieval enhancement technology is used to strengthen the reference to the external knowledge base in the document generation process;

[0022] Provides an intelligent document preparation assistant that automatically generates meeting plans, writes drafts of data chart documents, and summarizes complex materials based on user requirements and specified templates.

[0023] Furthermore, in step S6, a set of common communication standards is defined, and a web interface or mobile application is provided, so that managers can view the equipment status and send control instructions anytime and anywhere, and a fault diagnosis algorithm is integrated to automatically detect potential problems and send alerts to relevant personnel.

[0024] A comprehensive management device for water conservancy, comprising: at least one memory and at least one processor;

[0025] The at least one memory is used to store a machine-readable program;

[0026] The at least one processor is used to call the machine-readable program to execute an integrated management method for water conservancy.

[0027] Compared with the prior art, the integrated water conservancy management method and device of the present invention has the following outstanding beneficial effects:

[0028] The present invention not only solves the problems of information islands and slow response in traditional water management, but also significantly improves the predictability and accuracy of flood control and drought relief work, optimizes water resource allocation and scheduling strategies, improves the health of river and lake ecosystems, and strengthens the safe operation and management of water conservancy projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0030] Attached Figure 1 It is a flow chart of an integrated management approach for water resources. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the solution of the present invention, the present invention is further described in detail below in conjunction with specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than 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.

[0032] A best embodiment is given below:

[0033] like Figure 1 As shown, a comprehensive management method for water conservancy in this embodiment has the following steps:

[0034] S1, Multi-source heterogeneous data integration platform;

[0035] Establish a unified portal that can receive relevant information from different sensor networks (such as rain gauges, water level gauges, etc.), remote sensing satellite images, and other public databases, and clean and convert them through preprocessing algorithms to ensure the quality of data used in subsequent analysis. The platform should be able to dynamically update the information in the national water conservancy "one map", including data on key objects such as reservoirs, river embankments, and flood storage areas, while integrating high-resolution remote sensing satellite image resources and video surveillance streams.

[0036] In order to ensure data consistency and integrity, the following key technologies are used:

[0037] Data fusion technology: Using advanced data fusion algorithms, data from different sources are merged and standardized to eliminate inconsistencies between data.

[0038] Real-time data processing: Introduce stream processing technology to realize rapid processing and analysis of real-time collected data, so as to timely reflect the current water conditions.

[0039] Anomaly detection mechanism: Automatically identify abnormal data points through machine learning methods to prevent erroneous information from interfering with the decision-making process.

[0040] S2, knowledge extraction and understanding module based on large models;

[0041] Using language models trained on large-scale texts, customized training is performed in combination with professional terminology, business rules and expert experience in the field of water conservancy, and professional terminology trained on small models is used as input for large models, so that it has the ability to "understand water conservancy language". The model's understanding of water conservancy professional knowledge is enhanced through knowledge graph technology, enabling it to decompose complex water conservancy tasks, such as predicting reservoir scheduling plans based on rainfall conditions. In addition, efficient parameter fine-tuning methods such as full update or LoRA are used to further enhance the model's understanding and task execution capabilities for specific water conservancy business scenarios.

[0042] The key technologies of this module include:

[0043] Understanding and interpretation of professional terms: Through the study of a large number of water conservancy documents and technical reports, the large model can accurately understand and interpret professional terms in the field of water conservancy.

[0044] Business logic reasoning: Combined with knowledge graphs, it can realize the reasoning of complex business logic, such as the linkage logic of "rainfall-runoff-confluence-evolution".

[0045] Personalized Question Answering System: Develop a question answering system based on natural language processing that allows users to ask questions in natural language and get accurate and relevant answers.

[0046] S3, image recognition and analysis module, used to automatically identify and classify features in high-resolution remote sensing images;

[0047] It integrates advanced computer vision technology to automatically identify and classify features in high-resolution remote sensing images, such as identifying buildings in river channels, monitoring new building lists, and automatically generating analysis reports on suspected illegal construction that hinders flooding. This module also supports users to set themes and dynamically generate thematic charts of key hydrological and spatial information such as precipitation distribution, reservoir water storage, and river water levels, providing users with intuitive and accurate information display.

[0048] The key technologies of the image recognition and analysis module are:

[0049] High-resolution image processing: Use efficient image processing algorithms to ensure that high-quality information can be extracted from high-resolution remote sensing images.

[0050] Target detection and tracking: Use the latest target detection algorithms, such as YOLOv5, Mask R-CNN, etc., to accurately locate and track specific targets (such as buildings).

[0051] Automated report generation: Automatically generate reports containing analysis results through templates to reduce the workload of manual operations.

[0052] S4. Combine digital twin technology and physical simulation engine to realize the combined call of water conservancy professional models;

[0053] Combining digital twin technology and physical simulation engine, it realizes the combined call of water conservancy professional models, such as hydrological forecasting model, hydrodynamic model, water conservancy project scheduling model, etc. Through these models, the system can predict the path and intensity of meteorological typhoons at the minute level, simulate rainfall in the basin, predict the increase of river water level, quickly predict flood inundation, and recommend scheduling schemes. On this basis, with the help of multi-scale similarity algorithm matching technology driven by large models, the basin scene similarity matching based on the historical scheduling process and the preset deduction process is realized, so as to assist in the formulation of more accurate and effective flood control plans.

[0054] The specific functions and technical highlights of the system are as follows:

[0055] Real-time simulation and prediction: Utilize high-performance computing resources to achieve real-time simulation and prediction, helping managers to prepare for responses in advance.

[0056] Multi-model collaboration: Through the API interface, multiple professional models are seamlessly integrated into one platform to ensure smooth collaboration between them.

[0057] Intelligent decision support: Combined with reinforcement learning algorithms, it continuously optimizes decision-making strategies and improves the scientificity and accuracy of decisions.

[0058] S5. Conduct interactive document office and knowledge management;

[0059] It provides an intelligent document preparation assistant that can automatically generate meeting plans, write drafts of data charts and documents, and summarize complex materials based on user requirements and specified templates. This function not only supports online reading or downloading of generated documents, but also allows collaborative modification and improvement of materials through dialogue. At the same time, the system has built-in rich public knowledge base and private knowledge base retrieval mechanisms to ensure that the information source is reliable and comprehensive.

[0060] Key features of document office and knowledge management tools include:

[0061] Automated document generation: Automatically generate well-structured and rich documents based on a small amount of input information provided by the user.

[0062] Multi-round dialogue interaction: allows users to have multiple rounds of dialogue with the system through natural language to gradually improve the document content.

[0063] Knowledge base retrieval enhancement: Use techniques such as recall retrieval enhancement (RAG) to strengthen the reference to external knowledge bases during the document generation process.

[0064] S6. Develop standardized interface protocols to connect with various water-related terminal equipment and water conservancy facility control systems for remote monitoring and control;

[0065] Develop standardized interface protocols to enable large water conservancy models to directly connect with various water-related terminal equipment (such as rainfall radars, rain gauges) and water conservancy facility control systems to achieve remote monitoring and control. This will help improve all-day, all-weather monitoring and intelligent service capabilities, and also facilitate rapid measures to respond to emergencies in emergency situations.

[0066] The core elements of the terminal equipment and facility linkage control interface include:

[0067] Standardized communication protocols: Define a set of universal communication standards to ensure that devices produced by different manufacturers can access the system without obstacles.

[0068] Remote monitoring and control: Provides a web interface or mobile application to facilitate managers to check device status and send control instructions anytime and anywhere.

[0069] Fault diagnosis and early warning: Integrated fault diagnosis algorithms automatically detect potential problems and issue alerts to relevant personnel.

[0070] Based on the above method, a comprehensive management device for water conservancy in this embodiment includes: at least one memory and at least one processor;

[0071] The at least one memory is used to store a machine-readable program;

[0072] The at least one processor is used to call the machine-readable program to execute an integrated management method for water conservancy.

[0073] The above-mentioned specific implementations are only specific cases of the present invention. The patent protection scope of the present invention includes but is not limited to the above-mentioned specific implementations. Any technical solutions that conform to the above-mentioned specific implementations of the present invention and any appropriate changes or substitutions made by ordinary technicians in the relevant technical field shall fall within the patent protection scope of the present invention.

[0074] 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 comprehensive management method for water conservancy, characterized in that: The steps are as follows: S1, Multi-source heterogeneous data integration platform; S2, knowledge extraction and understanding module based on large models; S3, image recognition and analysis module, used to automatically identify and classify features in high-resolution remote sensing images; S4. Combine digital twin technology and physical simulation engine to realize the combined call of water conservancy professional models; S5. Conduct interactive document office and knowledge management; S6. Develop standardized interface protocols to connect with various water-related terminal equipment and water conservancy facility control systems for remote monitoring and control.

2. The integrated water management method according to claim 1, characterized in that: In step S1, a unified portal is established to receive relevant information from different sensor networks, remote sensing satellite images and other public databases, and cleans and converts the information into a format through a preprocessing algorithm; Advanced data fusion algorithms are used to merge and standardize data from different sources. Stream processing technology is introduced to enable rapid processing and analysis of real-time collected data, thereby promptly reflecting current water conditions. An anomaly detection mechanism is established to automatically identify abnormal data points through machine learning methods.

3. The integrated management method for water resources according to claim 2, characterized in that: In step S2, a language model trained with large-scale text is used to conduct customized training in combination with professional terminology, business rules and expert experience in the field of water conservancy. The professional terminology trained with a small model is used as the input of the large model. The model's understanding of water conservancy expertise is strengthened through knowledge graph technology, allowing it to decompose complex water conservancy tasks.

4. The integrated water conservancy management method according to claim 3 is characterized in that: In step S3, image processing algorithms are used to extract high-quality information from high-resolution remote sensing images, and the latest target detection algorithms are used to achieve precise positioning and tracking of specific targets, and a report containing analysis results is automatically generated in a templated manner; After the user sets the theme, thematic charts of key hydrological and spatial information are dynamically generated, providing users with intuitive and accurate information display.

5. The integrated water conservancy management method according to claim 4 is characterized in that: In step S4, computing resources are used to simulate and predict in real time to conduct management responses; Through the API interface, multiple professional models are seamlessly integrated into one platform; Combined with reinforcement learning algorithms, the decision-making strategy is continuously optimized; With the help of multi-scale similarity algorithm matching technology driven by large models, the similarity matching of watershed scenarios based on historical scheduling processes and preset deduction processes can be achieved, thereby assisting in the formulation of more effective flood control plans.

6. A comprehensive management method for water resources according to claim 5, characterized in that: In step S5, a document with complete structure and substantial content is automatically generated based on a small amount of input information provided by the user; Allow users to have multiple rounds of dialogue with the system through natural language to gradually improve the document content; Use recall retrieval enhancement technology to strengthen the reference to external knowledge bases during document generation; Provides an intelligent document preparation assistant that automatically generates meeting plans, writes drafts of data chart documents, and summarizes complex materials based on user requirements and specified templates.

7. A comprehensive management method for water resources according to claim 6, characterized in that: In step S6, a set of common communication standards is defined, and a web interface or mobile application is provided, so that managers can view the equipment status and send control instructions anytime and anywhere, and a fault diagnosis algorithm is integrated to automatically detect potential problems and send alerts to relevant personnel.

8. A comprehensive management device for water conservancy, characterized in that: include: at least one memory and at least one processor; The at least one memory is used to store a machine-readable program; The at least one processor is configured to call the machine-readable program to execute the method according to any one of claims 1 to 7.

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