Water conservancy project flood control safety early warning method based on big data and intelligent analysis

Abstract

The invention discloses a water conservancy project flood control safety early warning method based on big data and intelligent analysis, and belongs to the technical field of water conservancy projects. The method comprises the following steps: collecting multi-source heterogeneous data including river water level, flow and rainfall in real time; a real-time database system is constructed by adopting a mode of combining a big data distributed storage architecture and multiple storage engines, and efficient storage and query of mass data are realized; developing an intelligent data processing module, automatically detecting and repairing abnormal data, carrying out association mining, format conversion and fusion processing on multi-source heterogeneous data, and generating a standardized hydro-meteorological data set; constructing a drainage basin-oriented distributed hydrology and hydraulics model, importing a standardized hydrology and meteorology data set into the model, optimizing model parameters through machine learning, and simulating the formation and evolution process of flood in the drainage basin with high precision; and accurate forecasting and timely early warning are carried out on the flood level and the influence range in a certain time period in the future.

Description

Technical Field

[0001] This application relates to the technical field of water conservancy projects. More specifically, it relates to a flood control safety warning method for water conservancy projects based on big data and intelligent analysis. Background Art

[0002] How to effectively carry out flood warning and prevention has become an important problem that urgently needs to be solved in the field of water conservancy projects.

[0003] Traditional flood control warning systems usually rely on limited hydrometeorological monitoring data and experience-based models. This method has many deficiencies. First, the collection of monitoring data is often not comprehensive enough, lacking coverage in space and time, and it is difficult to accurately reflect the actual hydrometeorological conditions within the basin. Second, traditional data processing and storage technologies are difficult to handle the efficient processing and analysis of massive, multi-source, and heterogeneous data. In addition, the parameter setting and optimization of traditional models rely on artificial experience, making it difficult to achieve high-precision warning effects, and lacking real-time and dynamic update capabilities.

[0004] With the rapid development of big data and artificial intelligence technologies, new opportunities have been brought to the flood control safety warning of water conservancy projects. By constructing a comprehensive hydrometeorological monitoring network, collecting multi-source heterogeneous data in real time, and using big data technologies for efficient storage and processing, it is possible to provide more comprehensive and accurate data support for flood warning. At the same time, intelligent analysis technologies can deeply mine and analyze data, automatically detect and repair abnormal data, improve data quality, and optimize the parameters of hydrological and hydraulic models in combination with machine learning methods to achieve high-precision flood forecasting.

[0005] Based on the above background, this application proposes a flood control safety warning method for water conservancy projects based on big data and intelligent analysis. By constructing a real-time monitoring network, adopting big data storage and processing technologies, developing intelligent data processing modules, and constructing high-precision hydrological and hydraulic models, it realizes the full-process optimization from data collection, storage, processing to model simulation and warning release. Summary of the Invention

[0006] In order to overcome a series of defects existing in the prior art, the purpose of this application is to provide a flood control safety warning method for water conservancy projects based on big data and intelligent analysis in view of the above problems, including the following steps:

[0007] Step 1, construct a hydrometeorological monitoring network covering the basin to be warned, and collect multi-source heterogeneous data including river water level, flow rate, and rainfall in real time;

[0008] Step 2, adopt a combination of big data distributed storage architecture and multiple storage engines to construct a real-time database system to achieve efficient storage and query of massive data;

[0009] Step 3: Develop an intelligent data processing module to automatically detect and repair abnormal data, perform correlation mining, format conversion, and fusion processing on multi-source heterogeneous data, and generate a standardized hydrometeorological dataset;

[0010] Step 4: Build a distributed hydrological and hydraulic model for the basin, import the standardized hydrometeorological dataset into the model, optimize the model parameters through machine learning, and accurately simulate the formation and evolution process of floods in the basin;

[0011] Step 5: Accurately forecast and promptly issue early warnings for flood levels and affected areas within a certain future time period.

[0012] Furthermore, Step 1 includes the following steps:

[0013] Define the geographical boundaries of the basin to be warned, and select key locations for monitoring points based on terrain, river flow direction, and historical flood data;

[0014] Optimize the selection and layout of key locations through GIS analysis and historical data research to ensure comprehensive monitoring of the basin to be warned;

[0015] Set up monitoring points at the optimized key locations and install monitoring equipment including water level gauges, flow meters, and rain gauges;

[0016] Establish a real-time data acquisition and transmission system to ensure that each monitoring point can upload monitoring data to the data center in a timely manner;

[0017] Formulate unified data standards and format specifications for different types of monitoring data to achieve seamless integration of multi-source heterogeneous data;

[0018] Regularly maintain and upgrade the monitoring equipment to ensure its normal operation and provide continuous and reliable data support for hydrometeorological early warnings.

[0019] Furthermore, Step 2 includes the following steps:

[0020] Introduce a distributed file system as the underlying storage of the data lake to store raw monitoring data and various derivative data;

[0021] Deploy a distributed database cluster as the core of data storage and management;

[0022] Use a streaming data processing framework to achieve real-time access, conversion, and persistence of data, and efficiently write the data into the distributed database cluster;

[0023] For different query scenarios, adopt multiple storage engines in parallel to provide efficient data query and analysis capabilities;

[0024] Decouple various upstream data sources and different downstream storage engines to achieve seamless data access and elastic expansion;

[0025] Build a metadata management system to record the meta-information, data architecture, and data lineage of data, providing support for data traceability and utilization;

[0026] Formulate data lifecycle management strategies, including data backup, compression, and archiving, to ensure the long-term preservation and efficient utilization of data.

[0027] Furthermore, step 3 includes the following steps:

[0028] Build a data quality detection module to detect the integrity, consistency, and rationality of raw data, identify abnormal or missing data, and generate a data quality report;

[0029] Develop a data repair module to automatically repair the identified abnormal and missing data based on domain knowledge and statistical learning methods, completing data cleaning and filling;

[0030] Establish a multi-source data association module to automatically mine and determine the association relationships between different data sources;

[0031] Design a unified data format specification, including naming conventions, data types, time formats, and null value representations, to convert the raw data from different data sources into a standard format;

[0032] Summarize and splice the associated multi-source data to generate a comprehensive standard dataset at the semantic level;

[0033] Load metadata information for the generated standard dataset to describe the meaning, source, and preprocessing process of the data, ensuring the interpretability and traceability of the data;

[0034] Build an online data quality monitoring system to analyze and display the quality status of the generated data in real time, ensuring the high quality of production data.

[0035] Furthermore, step 4 includes the following steps:

[0036] Establish a three-dimensional digital elevation dataset for the basin to accurately describe the topographic and geomorphic features of the basin, serving as the basic data source for the model;

[0037] Collect and import land use data and soil type data within the basin to build a basin feature dataset;

[0038] Based on the topographic and geomorphic features and river network characteristics of the basin, adopt a distributed unit division method to divide the entire basin into numerous mutually coupled computational units;

[0039] For each computational unit, an independent sub-module of the hydrological model is established using a distributed model method based on physical mechanisms to uniformly describe the processes of rainfall and evapotranspiration, surface runoff, and groundwater movement within the computational unit;

[0040] A distributed coupled river network hydraulics module is constructed, connecting the outlets of each computational unit to the river network nodes to simulate the process of water body migration and aggregation at the scale of the entire river network;

[0041] A distributed hydraulics module is constructed to simulate the movement and convergence of water bodies within the river network and establish a digital representation of river networks, reservoirs, and dam facilities;

[0042] Using a parallel computing framework, the hydrological simulations of each computational unit are synchronously executed on a high-performance computing cluster to achieve cross-unit information exchange and time-step coordination;

[0043] The standardized spatio-temporally continuous hydrometeorological dataset is used as the driving data boundary condition for each computational unit and the river network module;

[0044] Based on historical flood event data, the key parameters in each sub-module of the hydrological model are optimized to improve the prediction accuracy of the model;

[0045] A visualization module is constructed to intuitively display the spatio-temporal evolution process of the formation, diversion, and dispersion of floods within the basin;

[0046] The simulation results are compared with the measured data to automatically identify the deviation of the model and feedback to optimize the model parameters.

[0047] Furthermore, based on the topographical and geomorphic features of the basin and the characteristics of the river network, a distributed unit division method is adopted to divide the entire basin into numerous mutually coupled computational units, including the following steps:

[0048] The digital elevation model data is preprocessed through GIS software, including depression filling and flow direction analysis operations, to ensure the continuity and hydrological correctness of the data;

[0049] Based on the preprocessed digital elevation model data, the extraction of the basin boundary and the identification of the main river network are carried out. At the same time, using hydrological analysis tools, the watershed and the main river direction of the basin are determined to provide a basic framework for subsequent unit division;

[0050] According to the area size and topographical complexity of the basin, the grid method is used for the preliminary division of computational units;

[0051] Based on the topographical characteristics of the basin, the computational units are optimized and re-divided. Specifically: the clustering analysis method is used to identify and merge adjacent units with similar hydrological and geomorphic characteristics to ensure that each computational unit has relatively homogeneous hydrological response characteristics;

[0052] Construct the hydrological topological relationship between computational units, specifically: determine the upstream contributing area and downstream receiving unit of each computational unit; define the water flow transmission path and connection method between computational units; quantify the hydraulic connection between adjacent computational units, including surface runoff and groundwater interaction.

[0053] Furthermore, the preliminary division of computational units using the grid method includes the following steps:

[0054] Determine the target grid area A according to the area size and topographic complexity of the basin grid , and the specific formula is: A grid = k·A / n, where A is the total area of the basin; n is a predetermined number of grids; k is a coefficient related to topographic complexity, and the more complex the terrain, the smaller the value of k;

[0055] Calculate the side length d of each grid through the following formula:

[0056] Calculate the total number of grids N through the following formula: N = A / A grid .

[0057] Furthermore, use the cluster analysis method to identify and merge adjacent units with similar hydrological and geomorphic characteristics to ensure that each computational unit has relatively homogeneous hydrological response characteristics, including the following steps:

[0058] Define the topographic feature vector: The topographic feature vector of each computational unit i is x i =(S i ,α i ,R i ), where S i is the slope of computational unit i; α i is the aspect of computational unit i; R i is the flow concentration path of computational unit i;

[0059] Use the weighted Euclidean distance to measure the topographic feature similarity between computational unit i and adjacent computational unit j, and the specific calculation formula is: where w S , w α and w R are the weights of the corresponding features, used to adjust the influence of different features on the total distance;

[0060] Divide all computational units into k clusters to maximize the similarity between computational units within the cluster;

[0061] Set a similarity threshold ∈, which represents the minimum requirement for the similarity measurement between two computational units. If Distance ij ≤∈, then merge them.

[0062] Further, for unit i, its upstream contribution area is expressed as:

[0063] For unit i, its downstream receiving unit is expressed as:

[0064] Surface runoff is expressed as where h i and h k represent the hydraulic head heights of unit i and k respectively; P ik is the path matrix; C surface is the surface runoff coefficient; A i represents the area of unit i;

[0065] Groundwater interaction is expressed as: C subsurface is the groundwater interaction coefficient.

[0066] Further, step 5 includes the following steps:

[0067] Establish a forecasting and decision-making module, comprehensively incorporate multi-source information such as hydrometeorological data, model simulation results, and the status of engineering facilities, and construct a knowledge base and a rule base for forecasting and decision-making;

[0068] Design flood level classification criteria, and combine with the historical flood records of the basin to divide floods into different levels according to their severity;

[0069] For different flood levels, predict the possible impact ranges, including the areas that may be flooded, the estimated number of affected people, and the economic losses;

[0070] Develop a risk-based early warning release mechanism, and automatically generate targeted early warning information according to the forecast flood level and impact range;

[0071] Establish a multi-level early warning response system, and formulate work processes, responsibility divisions, and emergency measure lists corresponding to different early warning levels;

[0072] Deploy a real-time forecasting system, automatically run the hydrological and hydraulic models at regular time intervals, and combine with new monitoring data to update the flood forecast results in a rolling manner;

[0073] Construct a Web GIS visualization platform to intuitively display the current water situation and simulation forecast results;

[0074] Integrate multiple early warning release channels to ensure high coverage and rapid dissemination of early warning information;

[0075] Evaluate the historical forecast results and emergency response situations, continuously improve the hydrological and hydraulic models, optimize the decision-making rules, and adjust the flood level classification criteria; establish an early warning feedback mechanism, collect corresponding feedback opinions, understand the effectiveness and deficiencies of the early warning, and continuously improve.

[0076] Compared with the prior art, the present application has the following beneficial effects:

[0077] The present application forms an efficient and accurate flood control safety early warning system by constructing a comprehensive hydrometeorological monitoring network, adopting big data and intelligent analysis technologies, developing an intelligent data processing module, and constructing a distributed hydrological and hydraulic model. This system integrates real-time data collection, efficient data storage and processing, abnormal data repair, multi-source data fusion, model parameter optimization, and a risk-based early warning release mechanism, and can accurately predict the flood level and the affected range, thereby providing timely and reliable early warning information to ensure flood control safety within the basin. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 It is a schematic flowchart of a flood control safety early warning method for water conservancy projects based on big data and intelligent analysis disclosed in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0079] To make the objectives, technical solutions, and advantages of the implementation of the present invention clearer, the technical solutions in the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings in the embodiments of the present invention. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some, but not all, of the embodiments of the present invention.

[0080] All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts fall within the scope of protection of the present invention.

[0081] The embodiments described below with reference to the accompanying drawings and the directional terms are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0082] As Figure 1 shown, a flood control safety early warning method for water conservancy projects based on big data and intelligent analysis includes the following steps:

[0083] Step 1, construct a hydrometeorological monitoring network covering the basin to be warned, and collect multi-source heterogeneous data including river water level, flow rate, and rainfall in real time;

[0084] Step 2, adopt a combination of a big data distributed storage architecture and multiple storage engines to construct a real-time database system to achieve efficient storage and query of massive data;

[0085] Step 3: Develop an intelligent data processing module to automatically detect and repair abnormal data, conduct correlation mining, format conversion, and fusion processing on multi-source heterogeneous data, and generate a standardized hydro-meteorological dataset.

[0086] Step 4: Build a distributed hydro-hydraulic model for the basin, import the standardized hydro-meteorological dataset into the model, and optimize the model parameters through machine learning to accurately simulate the formation and evolution process of floods in the basin.

[0087] Step 5: Accurately forecast and promptly warn of flood levels and affected areas within a certain period in the future.

[0088] The hydro-meteorological monitoring network constructed in Step 1 is the basis for flood control safety warning. This network collects real-time data such as water levels, flows, and rainfall amounts through various sensors installed at key locations such as rivers, lakes, and rainfall areas. Since these data come from different devices and platforms, they often exhibit the characteristics of multi-source heterogeneity, containing information in different formats and precisions. By establishing such a monitoring network, it is possible to ensure comprehensive monitoring of the hydrographic and meteorological conditions within the basin, and achieve timely capture and response to sudden flood events. The advantage of this step is to improve the real-time performance and coverage of data acquisition, providing rich and timely basic data for subsequent data processing and model calculation.

[0089] Step 2 constructs a real-time database system through the combination of a big data distributed storage architecture and multiple storage engines. This system can process the massive data continuously generated from the monitoring network, ensuring the efficient storage and rapid query of data. The distributed storage architecture has good scalability and fault tolerance, and can dynamically adjust storage resources to cope with changes in data volume. The combination of multiple storage engines provides the flexibility to select the optimal storage strategy according to data types and usage scenarios. For example, use a NoSQL database to store time-series data and a relational database to manage structured data, etc. In this way, while ensuring data integrity, it can significantly improve the efficiency of data storage and retrieval, providing a solid technical guarantee for real-time data processing and analysis.

[0090] The focus of Step 3 lies in the development of the intelligent data processing module. This module can automatically detect and repair abnormal data collected, ensuring the accuracy and consistency of the data. There are differences in format, precision, and time synchronization among multi-source heterogeneous data, and through steps such as correlation mining, format conversion, and fusion processing, it is necessary to convert them into a unified standardized data set. In this process, machine learning and data mining technologies are utilized to automatically discover the correlations and potential patterns between data, improving the intelligent level of data processing. The finally generated standardized hydrometeorological data set not only solves the problem of data heterogeneity but also improves the usability and analysis efficiency of the data, providing a high-quality data foundation for subsequent modeling and forecasting.

[0091] Step 4 constructs a distributed hydrological and hydraulic model for the basin. This model utilizes the standardized hydrometeorological data set to predict possible flood behaviors by simulating the formation and evolution process of floods within the basin. The distributed model can parallel process a large number of computing tasks, enhancing the efficiency and accuracy of the simulation. The introduction of machine learning technology to optimize the model parameters further improves the prediction accuracy of the model. Through continuous updating and training, the model can adaptively improve its prediction ability and provide high-precision flood simulation results. The advantage of this step is to achieve precise modeling and dynamic prediction of complex hydrological phenomena, enhancing the scientific nature and reliability of the flood prevention warning system.

[0092] In Step 5, through the previous data collection, processing, and modeling, the system can accurately forecast and timely warn of the flood level and affected area within a certain period in the future. Based on the high-precision simulation results, detailed flood warning information can be provided, including the time, location, scope of the flood occurrence, and possible impacts. Timely warning information can help relevant departments and the public take preventive measures in advance, reducing the losses and impacts of flood disasters. Overall, this step transforms data analysis and model prediction into specific action guidelines, greatly improving the practicality and social benefits of the flood prevention safety warning system. Through the synergistic effect of these technical measures, the system finally achieves the goal of efficient and accurate flood warning.

[0093] Furthermore, Step 1 includes the following steps:

[0094] Step 1.1, clarify the geographical boundary of the basin to be warned, and select the key locations for monitoring points to be arranged according to the terrain, river flow direction, and historical flood data;

[0095] Step 1.2, through GIS analysis and research on historical data, optimize the selection and layout of key locations to ensure comprehensive monitoring of the basin to be warned;

[0096] Step 1.3, set up monitoring points at the optimized key locations and install monitoring equipment including water level gauges, flow meters, and rain gauges;

[0097] Step 1.4: Establish a real-time data collection and transmission system to ensure that each monitoring point can upload monitoring data to the data center in a timely manner.

[0098] Step 1.5: Develop unified data standards and format specifications for different types of monitoring data to achieve seamless integration of multi-source heterogeneous data.

[0099] Step 1.6: Regularly maintain and upgrade monitoring equipment to ensure its normal operation and provide continuous and reliable data support for hydrometeorological early warning.

[0100] In Step 1.1, clarifying the geographical boundary of the basin to be warned is the primary task of flood control safety early warning. Through detailed analysis of topographic maps, river courses, and historical flood data, areas prone to flooding within the basin and the paths of flood propagation can be identified. This process involves using topographic and hydrographic maps and comprehensively evaluating the elevation, slope, and land use types of the basin through Geographic Information System (GIS) technology. In addition, historical flood data provides crucial background information, including flood frequency, scale, and impact range, which helps determine high-risk areas and the layout locations of key monitoring points. The advantage of this step is to ensure a scientific and reasonable distribution of monitoring points that can cover key locations throughout the basin, providing an accurate spatial basis for real-time monitoring and early warning.

[0101] In Step 1.2, after initially determining the monitoring points, further optimize the selection and layout of monitoring points through GIS analysis and historical data research. GIS technology can conduct detailed spatial analysis of topographic data, simulate flood propagation paths, and evaluate the coverage and monitoring effects of different monitoring point layout schemes. Combining historical flood data can verify and calibrate these simulation results to ensure that the selected monitoring points can effectively capture hydrological changes within the basin. This optimization process also considers the accessibility of monitoring points, the feasibility of equipment installation, and the convenience of maintenance. Through this scientific optimization method, the monitoring network not only achieves comprehensive coverage but also improves the reliability and timeliness of monitoring data, thus providing a solid foundation for flood control early warning.

[0102] In Step 1.3, at the optimized key positions, monitoring devices such as water level gauges, flow meters, and rain gauges are installed. This step is directly related to the actual operation effect of the monitoring network. Each monitoring device is responsible for collecting different types of hydrometeorological data: the water level gauge measures the changes in river water levels, the flow meter records the flow velocity and discharge of river water, and the rain gauge monitors the rainfall. These devices need to fully consider environmental factors such as waterproofing, anti-corrosion, and anti-theft during design and installation to ensure their long-term stable operation. By setting these monitoring devices at the optimized key positions, the hydrological dynamics within the basin can be captured in real time, providing high-precision data support, thus providing the basic real-time data input for the flood warning system. This step ensures the comprehensiveness of the monitoring network and the accuracy of data collection, providing a reliable data source for subsequent data processing and warning analysis.

[0103] In Step 1.4, establishing an efficient and stable real-time data collection and transmission system is the key to ensuring that the data collected at the monitoring points can be transmitted to the data center in a timely manner. This system usually includes a data collection module, a wireless transmission module, and a data reception and processing module. The collection module is responsible for obtaining data from the monitoring devices, the wireless transmission module transmits the data to the data center in real time through a wireless network (such as GPRS, 4G, or satellite communication), and the reception and processing module conducts preliminary processing and storage of the received data. Through this real-time data transmission system, the instant monitoring of the hydrometeorological information in the basin can be realized, and sudden flood events can be detected and responded to in a timely manner. The technical effect of this step lies in ensuring the real-time nature and continuity of the monitoring data, improving the overall efficiency and reliability of the data collection system, and providing timely and accurate data support for subsequent warning analysis.

[0104] In Step 1.5, in the face of multi-source heterogeneous monitoring data from different devices and platforms, formulating unified data standards and format specifications is the key to realizing seamless data integration. The data standardization process includes defining data formats, units, precision, etc., to ensure the consistency of all data during transmission and storage. The format specifications also include additional information such as timestamps, geographical locations, and metadata of the data, enabling data from different sources to be seamlessly integrated on the same platform. Through this standardization process, the compatibility issues faced by multi-source heterogeneous data during integration can be solved, ensuring the consistency and integrity of the data. The technical effect of this step lies in improving the efficiency of data processing and analysis, ensuring the availability and interoperability of the data, and providing a unified data basis for subsequent flood simulation and warning.

[0105] In Step 1.6, to ensure the long-term stable operation of the monitoring equipment, regular maintenance and upgrades are essential. This includes daily inspections of the equipment, troubleshooting, software updates, and hardware replacements. Maintenance work can prevent the equipment from failing due to environmental impacts (such as water immersion, corrosion, mechanical damage, etc.) and regularly calibrate the equipment to ensure data accuracy. Equipment upgrades can introduce new technologies and functions, improving the performance and expandability of the monitoring system. Through regular maintenance and upgrades, it can be ensured that the monitoring equipment is always in the best working condition, providing continuous and reliable hydrometeorological data support. The technical effect of this step is to improve the stability and reliability of the monitoring system, ensuring that the early warning system can obtain accurate data in a timely manner under any circumstances, thereby enhancing the overall effectiveness and response ability of flood prevention early warning.

[0106] Furthermore, Step 2 includes the following steps:

[0107] Step 2.1, introduce a distributed file system as the underlying storage of the data lake for storing raw monitoring data and various derivative data;

[0108] Step 2.2, deploy a distributed database cluster as the core for data storage and management;

[0109] Step 2.3, use a streaming data processing framework to achieve real-time access, conversion, and persistence of data, and efficiently write the data into the distributed database cluster;

[0110] Step 2.4, for different query scenarios, deploy multiple storage engines in parallel to provide efficient data query and analysis capabilities;

[0111] Step 2.5, decouple various upstream data sources and different downstream storage engines to achieve seamless data access and elastic expansion;

[0112] Step 2.6, build a metadata management system to record the meta-information, data architecture, and data lineage of the data, providing support for data traceability and utilization;

[0113] Step 2.7, formulate a data life cycle management strategy, including data backup, compression, and archiving, to ensure the long-term preservation and efficient utilization of data.

[0114] In Step 2.1, introducing a distributed file system as the underlying storage of the data lake is one of the key steps in processing big data. As the main architecture for storing raw monitoring data and various types of derived data, the data lake can effectively receive, store, and manage massive amounts of data. The distributed file system has good scalability and fault tolerance, and can handle large data streams transmitted from different monitoring devices and data sources. Through the architecture of the data lake, raw monitoring data can be securely stored, while supporting multi-dimensional analysis and processing of the data, laying a solid foundation for subsequent data processing and mining.

[0115] In Step 2.2, deploying a distributed database cluster as the core of data storage and management can effectively process and store structured data and derived data from the data lake. Through horizontal scaling, the distributed database cluster distributes data storage and processing capabilities across multiple nodes, improving the overall performance and reliability of the system. This architecture not only supports high-concurrency data access requirements but also can seamlessly scale as the data volume grows. Through centralized management and efficient query interfaces, the distributed database cluster provides users with fast and stable data access services, supporting complex data analysis and real-time query requirements.

[0116] In Step 2.3, introducing a streaming data processing framework is a key technical means to ensure real-time data access, transformation, and persistence. Streaming data processing can receive real-time data streams from monitoring devices and sensors, perform immediate processing and transformation, and efficiently write the processed data into the distributed database cluster. This approach ensures low-latency data processing and high throughput, enabling the system to respond promptly to changing data requirements and rapidly growing data traffic. Through streaming processing, the system can achieve real-time data updates and persistent storage, providing a real-time input data source for subsequent data analysis and prediction models.

[0117] In Step 2.4, to address different query scenarios and data access requirements, parallel deployment of multiple storage engines is a key strategy to improve the system's data query and analysis capabilities. Different storage engines, such as relational databases, NoSQL databases, and columnar storage, are each good at handling different types and structures of data. Through parallel deployment, the optimal storage engine can be selected for data access according to data characteristics and query requirements. This flexible storage architecture not only improves the system's data processing efficiency and response speed but also optimizes resource utilization and system performance, providing users with efficient and accurate data query and analysis services.

[0118] In Step 2.5, decoupling the upstream data source and the downstream storage engine is a key measure to ensure seamless data access and elastic system expansion. Through standardized interfaces and a data transformation layer, various upstream data sources can enter the data lake and the streaming processing framework through a unified data access point without modifying and adjusting their respective data formats. At the same time, the decoupled data can be stored and processed flexibly according to requirements, supporting the system to expand as the data scale and user needs grow. This architecture design not only improves the flexibility and maintainability of the system but also effectively reduces the integration and maintenance costs, ensuring the stability and continuity of the data flow.

[0119] In Step 2.6, building a metadata management system is an important means to ensure data management and utilization. The metadata system records and manages the metadata of data (such as data source, format, update time), data architecture (such as data table structure, field definition), and data lineage (the generation and processing history of data), providing comprehensive support for data traceability and utilization. Through metadata management, the quality and consistency of data can be ensured, data redundancy and duplicate work can be reduced, and the availability and credibility of data resources can be improved. This is particularly important for complex big data environments, which can help users quickly understand and utilize data, supporting data-driven decision-making and application development.

[0120] In Step 2.7, formulating a data life cycle management strategy is a necessary measure to ensure the long-term preservation and efficient utilization of data. This includes regular data backup, data compression, and data archiving operations, storing data at different storage levels according to the importance and usage frequency of the data. The backup operation ensures the security and recoverability of data, the compression operation reduces the occupancy of storage space, and the archiving operation archives infrequently used data to low-cost storage media to reduce storage costs. Through effective data life cycle management, the system can effectively manage data storage costs and ensure the long-term preservation and efficient utilization of data, providing basic support for the continuous operation and development of the system.

[0121] In summary, the measures in Step 2 are combined to build an efficient and reliable big data processing and management system, supporting the real-time collection, storage, processing, and analysis of hydrometeorological data, providing a solid technical foundation and data support for the flood control safety warning system of water conservancy projects.

[0122] Furthermore, Step 3 includes the following steps:

[0123] Build a data quality detection module to detect the integrity, consistency, and reasonableness of the original data, identify abnormal or missing data, and generate a data quality report;

[0124] Develop a data repair module to automatically repair the identified abnormal data and missing data based on domain knowledge and statistical learning methods, and complete the cleaning and filling of the data;

[0125] Build a multi-source data association module to automatically mine and determine the association relationships between different data sources;

[0126] Design a unified data format specification, including naming conventions, data types, time formats, and null value representations, and convert the original data from different data sources into a standard format;

[0127] Summarize and splice the associated multi-source data to generate a comprehensive standard data set at the semantic level;

[0128] For the generated standard data set, load metadata information to describe the meaning, source, and preprocessing process of the data, ensuring the interpretability and traceability of the data;

[0129] Build an online data quality monitoring system to analyze and display the quality status of the generated data in real time, ensuring the high quality of production data.

[0130] Generally speaking, the above steps cover the whole process from data acquisition, cleaning, integration to monitoring. It can not only improve the quality and usability of data, but also provide more reliable and valuable data assets for the organization. Through automated and standardized processing, the efficiency and consistency of data processing can be greatly improved. At the same time, through the association and integration of multi-source data, as well as the management of metadata, deeper data value can be mined. Real-time quality monitoring ensures the sustainability of data quality and provides a solid foundation for data-driven decision-making.

[0131] Furthermore, step 4 includes the following steps:

[0132] Establish a three-dimensional digital elevation data set for the basin to accurately describe the topographic and geomorphic features of the basin as the basic data source for the model;

[0133] Collect and import land use data and soil type data within the basin to construct a basin feature data set;

[0134] Based on the topographic and geomorphic features and river network features of the basin, adopt a distributed unit division method to divide the entire basin into numerous mutually coupled calculation units;

[0135] For each calculation unit, establish an independent hydrological model sub-module using a distributed model method based on physical mechanisms to uniformly describe the rainfall evapotranspiration process, surface runoff, and groundwater movement within the calculation unit;

[0136] Construct a distributed coupled river network hydraulics module, connect the outlets of each computational unit to the river network nodes, and simulate the water body migration and aggregation process at the scale of the entire river network;

[0137] Construct a distributed hydraulics module to simulate the water body movement and convergence process within the river network, and establish digital representations of the river network, reservoirs, and dam facilities;

[0138] Utilize a parallel computing framework to synchronously execute the hydrological simulations of each computational unit on a high-performance computing cluster, and achieve cross-unit information exchange and time-step coordination;

[0139] Use a standardized spatio-temporally continuous hydrometeorological dataset as the driving data boundary condition for each computational unit and the river network module;

[0140] Based on historical flood event data, optimize the key parameters in each sub-module of the hydrological model to improve the prediction accuracy of the model;

[0141] Construct a visualization module to intuitively display the spatio-temporal evolution process of flood formation, diversion, and dispersion within the basin;

[0142] Compare the simulation results with the measured data, automatically identify the model deviations, and feedback to optimize the model parameters.

[0143] Generally speaking, the above steps integrate a number of advanced technologies, including high-precision terrain modeling, distributed hydrological simulation, parallel computing, data-driven parameter optimization, etc. It can not only provide high-precision flood prediction, but also help understand the complex basin hydrological process. The modular design and parallel computing framework endow it with good scalability and efficiency, and can meet the complex hydrological simulation requirements of large-scale basins.

[0144] Furthermore, based on the topographic and geomorphic features and river network characteristics of the basin, adopt a distributed unit division method to divide the entire basin into numerous mutually coupled computational units, including the following steps:

[0145] Preprocess the digital elevation model data through GIS software, including filling depressions and flow direction analysis operations, to ensure data continuity and hydrological correctness;

[0146] Based on the preprocessed digital elevation model data, extract the basin boundary and identify the main river network. At the same time, use hydrological analysis tools to determine the watershed and main river directions of the basin, providing a basic framework for subsequent unit division;

[0147] According to the area size and topographic complexity of the basin, adopt the grid method to conduct a preliminary division of the computational units;

[0148] Based on the topographic characteristics of the basin, the computational units are optimized and re-divided. Specifically: the clustering analysis method is used to identify and merge adjacent units with similar hydrogeomorphic characteristics to ensure relatively homogeneous hydroresponse characteristics within each computational unit;

[0149] Construct the hydrotopological relationship between computational units. Specifically: determine the upstream contributing area and downstream receiving unit of each computational unit; define the water flow transmission path and connection method between computational units; quantify the hydraulic connection between adjacent computational units, including surface runoff and groundwater interaction.

[0150] Furthermore, the preliminary division of computational units using the grid method includes the following steps:

[0151] Determine the target grid area A according to the area size and topographic complexity of the basin grid , and the specific formula is: A grid = k·A / n, where A is the total area of the basin; n is a predetermined number of grids; k is a coefficient related to topographic complexity, and the more complex the terrain, the smaller the value of k;

[0152] Calculate the side length d of each grid through the following formula:

[0153] Calculate the total number of grids N through the following formula: N = A / A grid .

[0154] Furthermore, the clustering analysis method is used to identify and merge adjacent units with similar hydrogeomorphic characteristics to ensure relatively homogeneous hydroresponse characteristics within each computational unit, including the following steps:

[0155] Define the topographic feature vector: the topographic feature vector of each computational unit i is x i =(S i ,α i ,R i ), where S i is the slope of computational unit i; α i is the aspect of computational unit i; R i is the flow accumulation path of computational unit i;

[0156] Use the weighted Euclidean distance to measure the topographic feature similarity between computational unit i and adjacent computational unit j. The specific calculation formula is: where, w S , w α and w R are the weights of the corresponding features, used to adjust the influence of different features on the total distance;

[0157] Divide all computational units into k clusters to maximize the similarity between computational units within the cluster;

[0158] Set a similarity threshold ∈, which represents the minimum requirement for the similarity measurement between two computing units. If Distance ij ≤∈, then merge them.

[0159] Furthermore, for unit i, its upstream contribution area is expressed as:

[0160] For unit i, its downstream receiving unit is expressed as:

[0161] Surface runoff is expressed as where h i and h k represent the head heights of unit i and k respectively; P ik is the path matrix; C surface is the surface runoff coefficient; A i represents the area of unit i;

[0162] Groundwater interaction is expressed as: C subsurface is the groundwater interaction coefficient.

[0163] Furthermore, step 5 includes the following steps:

[0164] Establish a forecast decision-making module, comprehensively incorporate multi-source information such as hydrometeorological data, model simulation results, and engineering facility status, and construct a knowledge base and a rule base for forecast decision-making;

[0165] Design flood level classification criteria, and classify floods into different levels according to their severity in combination with the historical flood records of the basin;

[0166] For different flood levels, predict the possible impact range, including the possible flooded areas, the number of affected people, and the estimated economic losses;

[0167] Develop a risk-based early warning release mechanism, and automatically generate targeted early warning information according to the forecast flood level and impact range;

[0168] Establish a multi-level early warning response system, and formulate work processes, responsibility assignments, and emergency measure lists corresponding to different early warning levels;

[0169] Deploy a real-time forecasting system, and automatically run the hydrological and hydraulic models at regular time intervals, and update the flood forecast results in real time in combination with new monitoring data;

[0170] Build a Web GIS visualization platform to intuitively display the current water situation and simulation and prediction results;

[0171] Integrate multiple early warning release channels to ensure high coverage and rapid dissemination of early warning information;

[0172] Evaluate historical prediction results and emergency response situations, continuously improve the hydrological and hydraulic models, optimize decision-making rules, and adjust flood level classification standards; establish an early warning feedback mechanism, collect corresponding feedback opinions, understand the effects and deficiencies of early warnings, and continuously improve.

[0173] Generally speaking, the above steps integrate a number of advanced technologies and management methods, including multi-source data fusion, risk grading, real-time prediction, visualization display, multi-channel dissemination, etc. It can not only provide accurate and timely flood early warnings, but also support more scientific and effective flood control decision-making.

[0174] Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A flood control safety early warning method for water conservancy projects based on big data and intelligent analysis, characterized in that: The following steps are involved: Step 1: Build a hydrological and meteorological monitoring network covering the watershed to be warned, and collect multi-source heterogeneous data including river water level, flow and rainfall in real time; Step 2: Use a big data distributed storage architecture and a combination of multiple storage engines to build a real-time database system to achieve efficient storage and query of massive data; Step 3: Develop an intelligent data processing module to automatically detect and repair abnormal data, perform association mining, format conversion and fusion processing on multi-source heterogeneous data, and generate standardized hydrological and meteorological data sets; Step 4: Build a distributed hydrological and hydraulic model for the watershed, import standardized hydrological and meteorological data sets into the model, optimize model parameters through machine learning, and simulate the formation and evolution of floods in the watershed with high accuracy; Step 5: Make accurate forecasts and timely warnings of flood levels and impact areas within a certain period of time in the future.

2. According to claim 1, a flood control safety early warning method for water conservancy projects based on big data and intelligent analysis is characterized in that: Step 1 includes the following steps: Identify the geographical boundaries of the basin to be warned, and select key locations for monitoring points based on topography, river direction, and historical flood data; Through GIS analysis and historical data research, optimize the selection and layout of key locations to ensure comprehensive monitoring of the watershed to be warned; Set up monitoring points at optimized key locations and install monitoring equipment including water level meters, flow meters and rain gauges; Establish a real-time data collection and transmission system to ensure that each monitoring point can upload monitoring data to the data center in a timely manner; Formulate unified data standards and format specifications for different types of monitoring data to achieve seamless integration of multi-source heterogeneous data; Regularly maintain and upgrade monitoring equipment to ensure its normal operation and provide continuous and reliable data support for hydrological and meteorological warnings.

3. According to claim 1, a flood control safety early warning method for water conservancy projects based on big data and intelligent analysis is characterized in that: Step 2 includes the following steps: Introduce a distributed file system as the underlying storage of the data lake to store original monitoring data and various derivative data; Deploy a distributed database cluster as the core of data storage and management; Use the streaming data processing framework to achieve real-time data access, conversion, and persistence, and efficiently write data into the distributed database cluster; For different query scenarios, multiple storage engines are deployed in parallel to provide efficient data query and analysis capabilities; Decouple various upstream data sources and downstream storage engines to achieve seamless data access and elastic expansion; Build a metadata management system to record data metadata, data architecture, and data lineage, and provide support for data tracing and utilization; Develop data lifecycle management strategies, including data backup, compression and archiving, to ensure long-term preservation and efficient use of data.

4. The method for early warning of flood control safety of water conservancy projects based on big data and intelligent analysis according to claim 1 is characterized in that: Step 3 includes the following steps: Build a data quality detection module to detect the integrity, consistency and rationality of the original data, identify abnormal data or missing data, and generate a data quality report; Develop a data repair module to automatically repair identified abnormal data and missing data based on domain knowledge and statistical learning methods, and complete data cleaning and filling; Establish a multi-source data association module to automatically mine and determine the associations between different data sources; Design unified data format specifications, including naming conventions, data types, time formats, and null value representation, to convert raw data from different data sources into a standard format; Aggregate and stitch related multi-source data to generate comprehensive, semantically standardized data sets; For the generated standard data set, metadata information is loaded to describe the meaning, source, and preprocessing process of the data to ensure the interpretability and traceability of the data; Build an online data quality monitoring system to analyze and display the quality status of generated data in real time to ensure the high quality of production data.

5. The method for early warning of flood control safety of water conservancy projects based on big data and intelligent analysis according to claim 1 is characterized in that: Step 4 includes the following steps: Establish a three-dimensional digital elevation data set of the watershed to accurately describe the topographic features of the watershed as the basic data source of the model; Collect and import land use data and soil type data in the watershed to construct a watershed characteristic dataset; Based on the topography and river network characteristics of the basin, the distributed unit division method is used to divide the entire basin into a number of mutually coupled computing units; For each calculation unit, a distributed model method based on physical mechanism is used to establish an independent hydrological model submodule to uniformly describe the rainfall evapotranspiration process, surface runoff and groundwater movement in the calculation unit. Construct a distributed coupled river network hydraulics module, connect the outlets of each computing unit with the river network nodes, and simulate the water transport and aggregation process at the scale of the entire river network; Construct a distributed hydraulics module to simulate the movement and convergence of water in the river network and establish a digital representation of the river network, reservoirs and dam facilities; Using a parallel computing framework, the hydrological simulation of each computing unit is synchronously executed on a high-performance computing cluster to achieve cross-unit information exchange and time step coordination; The standardized spatiotemporally continuous hydrological and meteorological datasets are used as driving data boundary conditions for each calculation unit and river network module; Based on historical flood event data, key parameters in each hydrological model submodule are optimized to improve the prediction accuracy of the model; Construct a visualization module to intuitively display the formation, diversion, and spatiotemporal evolution of floods in the basin; Compare the simulation results with the measured data, automatically identify model deviations, and provide feedback to optimize model parameters.

6. The method for early warning of flood control safety of water conservancy projects based on big data and intelligent analysis according to claim 5 is characterized in that: Based on the topography and river network characteristics of the basin, the distributed unit division method is used to divide the entire basin into a number of mutually coupled computing units, including the following steps: Preprocess the digital elevation model data using GIS software, including depression filling and flow direction analysis operations, to ensure data continuity and hydrological correctness; Based on the pre-processed digital elevation model data, the watershed boundaries are extracted and the main river network is identified. At the same time, the watershed watershed and main river direction are determined using hydrological analysis tools, providing a basic framework for subsequent unit division. According to the size of the basin and the complexity of the terrain, the grid method is used to make a preliminary division of the calculation units; Based on the topographic characteristics of the watershed, the calculation units are optimized and re-divided. Specifically, the cluster analysis method is used to identify and merge adjacent units with similar hydrological and geomorphological characteristics to ensure that each calculation unit has relatively homogeneous hydrological response characteristics; Construct the hydrological topological relationship between the calculation units, specifically: determine the upstream contributing area and downstream receiving unit of each calculation unit; define the water flow transmission path and connection method between the calculation units; quantify the hydraulic connection between adjacent calculation units, including surface runoff and groundwater interaction.

7. The method for early warning of flood control safety of water conservancy projects based on big data and intelligent analysis according to claim 6 is characterized in that: The preliminary division of the calculation unit using the grid method includes the following steps: Determine the target grid area A based on the size of the watershed and the complexity of the terrain grid , the specific formula is: A grid = k·A / n, where A is the total area of ​​the basin; n is a predetermined number of grids; k is a coefficient related to the complexity of the terrain. The more complex the terrain, the smaller the k value. The side length d of each grid is calculated by the following formula: The total number of grids N is calculated by the following formula: N = A / A grid .

8. The method for early warning of flood control safety of water conservancy projects based on big data and intelligent analysis according to claim 6 is characterized in that: Cluster analysis is used to identify and merge adjacent units with similar hydrological and geomorphological characteristics to ensure that each calculation unit has relatively homogeneous hydrological response characteristics, including the following steps: Define the terrain feature vector: The terrain feature vector of each computing unit i is x i =(S i ,α i ,R i ), where S i is the slope of computation unit i; α i is the slope direction of calculation unit i; R i is the sink path of computing unit i; The weighted Euclidean distance is used to measure the similarity of terrain features between calculation unit i and adjacent calculation unit j. The specific calculation formula is: Among them, w S , w α and w R is the weight of the corresponding feature, which is used to adjust the impact of different features on the total distance; Divide all computing units into k clusters so that the similarity between computing units in the cluster is maximized; Set a similarity threshold ∈, which represents the minimum requirement for the similarity measurement between two computing units. If Distance ij ≤∈, then merge them.

9. The method for early warning of flood control safety of water conservancy projects based on big data and intelligent analysis according to claim 6 is characterized in that: For unit i, its upstream contribution area It is expressed as: For unit i, its downstream receiving unit It is expressed as: Surface runoff Expressed as Among them, h i and h k represent the water head height of unit i and k respectively; P ik is the path matrix; C surface is the surface runoff coefficient; A i represents the area of ​​unit i; Groundwater interaction It is expressed as: C subsurface is the groundwater interaction coefficient.

10. The method for early warning of flood control safety of water conservancy projects based on big data and intelligent analysis according to claim 1, characterized in that: Step 5 includes the following steps: Establish a forecast decision module, integrate hydrological and meteorological data, model simulation results and multi-source information on engineering facility status, and build a knowledge base and rule base for forecast decision-making; Design flood classification standards, combining historical flood records of the basin to classify floods into different levels according to their severity; For different flood levels, predict the possible impact range, including the area that may be flooded, the number of people affected and the estimated economic losses; Develop a risk-based warning release mechanism to automatically generate targeted warning information based on the predicted flood level and impact area; Establish a multi-level early warning response system and formulate work processes, division of responsibilities and emergency measures lists corresponding to different warning levels; Deploy a real-time forecasting system to automatically run the hydrological and hydraulic models at regular intervals, and update flood forecast results in a rolling manner based on new monitoring data; Build a Web GIS visualization platform to intuitively display the current water conditions and simulation forecast results; Integrate multiple warning release channels to ensure high coverage and rapid dissemination of warning information; Evaluate historical forecast results and emergency response situations, continuously improve hydrological and hydraulic models, optimize decision-making rules, and adjust flood classification standards; Establish an early warning feedback mechanism, collect relevant feedback, understand the effects and shortcomings of early warning, and make continuous improvements.

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