A Prediction and Early Warning Method and System for Drought Disasters in Rural Water Supply Projects

Abstract

The present invention relates to the technical field of disaster prediction and early warning, and particularly to a method and system for predicting and early warning drought disasters in rural water supply projects. The method includes the following steps: obtaining a hydrological data set; performing data preprocessing on the hydrological data set to generate a core hydrological processing data set; extracting data features from the core hydrological processing data set to generate a hydrological feature data set; quantifying the hydrological feature data set to generate a calculation algorithm for multi-source drought early warning thresholds; using the calculation algorithm for multi-source drought early warning thresholds and combining with the hydrological feature data set to construct a multi-dimensional interaction model to generate a multi-source hydrological drought early warning and prediction model; therefore, the present invention solves the problem of inaccurate prediction caused by inconsistent multi-source data in the traditional early warning system by optimizing hydrological data processing and early warning threshold calculation, and greatly improves the accuracy and reliability of drought disaster early warning.

Description

Technical Field

[0001] The present invention relates to the technical field of disaster prediction and early warning, and particularly to a method and system for predicting and early warning drought disasters in rural water supply projects. Background Art

[0002] Rural water supply projects face many technical deficiencies in the prediction and early warning of drought disasters. Existing methods usually rely on data monitoring of a single water source, which results in a low response ability of the system to drought events. The interaction among reservoir water, groundwater, and river water resources has not been fully considered. However, reservoir water, groundwater, and river water are all water sources for rural water supply projects. Considering them separately will lead to a lack of comprehensiveness in drought prediction. Traditional drought early warning models mostly adopt static thresholds, which are out of touch with the dynamic changes of real-time hydrological data, easily causing a lag in early warning information and failing to respond to potential drought risks in a timely and effective manner. In the process of data feature extraction and analysis, existing technologies often ignore the multidimensionality and time series characteristics of hydrological data. Many models fail to achieve deep integration of multi-source data, resulting in inaccurate comprehensive assessment of drought situations. The lack of efficient data processing algorithms limits the analysis and prediction capabilities of real-time data and makes it difficult to adapt to rapidly changing environmental conditions. Finally, the information transmission and response mechanism of existing early warning systems is relatively lagging, and they fail to effectively integrate various types of data to form a unified decision support platform. This information island effect makes relevant departments lack necessary coordination and interaction in dealing with drought disasters, reducing the efficiency and accuracy of decision-making. Therefore, there is an urgent need for a method and system for predicting and early warning drought disasters that can comprehensively consider the characteristics of multiple water sources, respond to hydrological changes in real time, and have an efficient information transmission mechanism. Summary of the Invention

[0003] Based on this, it is necessary to provide a method and system for predicting and early warning drought disasters in rural water supply projects to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for predicting and early warning drought disasters in rural water supply projects includes the following steps:

[0005] Step S1: Obtain a hydrological data set; perform data preprocessing on the hydrological data set to generate a core hydrological processing data set;

[0006] Step S2: Extract data features from the core hydrological processing data set to generate a hydrological feature data set; quantify the hydrological feature data set to generate a calculation algorithm for multi-source drought early warning thresholds;

[0007] Step S3: Use the calculation algorithm for multi-source drought early warning thresholds and combine it with the hydrological feature data set to construct a multi-dimensional interaction model to generate a multi-source hydrological drought early warning and prediction model;

[0008] Step S4: Use the multi-source hydrological drought early warning and prediction model to conduct multi-dimensional drought prediction, generate drought disaster prediction data, and thus complete the drought disaster prediction and early warning operation.

[0009] The beneficial effects of the present invention are as follows: By obtaining a multi-source hydrological data set (including water storage-precipitation data, water level change data, surface infiltration data, and underground aquifer recharge data), and performing data preprocessing on it, a hydrological core processing data set is generated. The key to this step is to ensure the effective integration of hydrological data of different water source types on the same platform, avoiding the processing complexity caused by differences in data sources. By extracting data features from the core processing data set, accurate extraction of various water source characteristics such as reservoirs, groundwater, and river water is ensured. Further, a drought early warning threshold calculation algorithm is generated through quantization processing. This algorithm innovatively introduces multi-layer threshold setting and combines historical drought data for dynamic analysis to ensure the flexibility and accuracy of the early warning system. By quantifying the feature data set, the organic combination of different water source data under the same algorithm framework is ensured, providing a solid data foundation for subsequent model construction. By interacting the drought early warning threshold calculation algorithm with the hydrological feature data set, a multi-dimensional drought early warning and prediction model is constructed, further enhancing the accuracy and robustness of the system in predicting drought disasters. The multi-dimensional interaction model realizes accurate prediction under various hydrological conditions by integrating the hydrological characteristics and early warning thresholds of different water sources, effectively improving the adaptability of the prediction model under different regions and different water source conditions. Using the multi-dimensional interaction model to conduct multi-dimensional prediction of drought disasters, specific disaster prediction data is generated, thus providing comprehensive data support in the drought early warning system and realizing the full-process automation and high efficiency of the prediction operation. Therefore, the present invention solves the problem of inaccurate prediction caused by inconsistent multi-source data in the traditional early warning system by optimizing hydrological data processing and early warning threshold calculation, greatly improving the accuracy and reliability of drought disaster early warning.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Obtain a hydrological data set, where the hydrological data set includes water storage-precipitation data, water level change data, surface infiltration data, and underground aquifer recharge data;

[0012] Step S12: Perform data set cleaning processing on the hydrological data set to generate hydrological cleaning data;

[0013] Step S13: Perform data set format standardization on the hydrological cleaning data to generate a hydrological core processing data set.

[0014] The present invention realizes the comprehensive collection of different hydrological information by obtaining a hydrological data set including water storage - precipitation data, water level change data, surface infiltration data, and groundwater aquifer recharge data. These data cover the hydrological characteristics of various water source types such as reservoirs, rivers, and groundwater, laying a solid foundation for subsequent hydrological analysis and drought prediction. This step ensures the diversity of data sources and the integrity of their physical processes, enabling the core information under various hydrological conditions to be integrated into the data set without omission. The collected hydrological data is subjected to data set cleaning processing, which significantly improves the quality and consistency of the data. Through cleaning, redundant, incomplete, or noisy data is eliminated, ensuring the accuracy, effectiveness, and stability during subsequent processing. The cleaning of hydrological data is particularly important for ensuring the accuracy of analysis results because the original hydrological data often contains many irregular factors such as sensor errors, data missing, and data anomalies, and cleaning can significantly reduce the impact of such adverse factors on the analysis. Further, the cleaned hydrological data is subjected to standardized processing of the data set format, enabling the standardized generation of the core hydrological processing data set. Through this formatting and annotation processing, data of different water source types are structurally integrated, ensuring that consistent feature extraction and threshold calculation can be performed in subsequent steps. At the same time, this step of standardized processing also greatly simplifies the complexity of subsequent data processing, providing a standardized data basis for further data mining and model construction.

[0015] Preferably, step S2 includes the following steps:

[0016] Step S21: Extract data features from the core hydrological processing data set to generate a hydrological feature data set;

[0017] Step S22: Confirm the warning threshold according to the hydrological feature data set and quantify the warning through the warning threshold, thereby generating a multi - water - source drought warning threshold calculation algorithm.

[0018] Through data feature extraction, the present invention converts the hydrological core processing data sets of different water sources such as reservoirs, groundwater, and river channels into operable hydrological feature data sets. This process not only covers the extraction of key features such as water storage-precipitation changes, water level fluctuation amplitude, and surface permeability, but also constructs a targeted hydrological feature index system based on the characteristics of different water sources. In this way, the hydrological feature data set effectively captures the hydrological change laws of various water source areas before the occurrence of drought, making the subsequent calculation of warning thresholds reliable with data support. The confirmation of warning thresholds based on the hydrological feature data set further improves the scientificity and accuracy of drought warning. First, this step clarifies the drought critical thresholds of different water source areas by comprehensively analyzing multi-source hydrological feature data, and designs a multi-level warning threshold structure in combination with historical drought data to ensure the comprehensiveness of the warning mechanism. The confirmation and quantification of warning thresholds transform drought warning from qualitative description to quantitative analysis. Especially through the dynamic analysis of reservoir storage capacity changes, groundwater infiltration paths, and water level fluctuation characteristics, a multi-source drought warning threshold calculation algorithm is generated. This algorithm dynamically adjusts the warning threshold by combining the change trends of various hydrological feature data, thus providing quantitative support for drought disaster prediction under different hydrological conditions. This warning mechanism based on the data level can quickly respond to changes in the hydrological environment, significantly improving the accuracy and timeliness of drought prediction.

[0019] Preferably, step S21 includes the following steps:

[0020] Step S211: Calculate and evaluate the storage capacity change rate of water storage-precipitation data to generate reservoir storage capacity change rate replenishment data; calculate the contribution rate of reservoir water replenishment by combining the reservoir storage capacity change rate replenishment data and water level changes to generate reservoir hydrological feature data;

[0021] Step S212: Draw the groundwater recharge path for the surface infiltration data to generate the groundwater recharge path; conduct an analysis of the balance between aquifer recharge and consumption by combining the surface infiltration data and underground aquifer recharge data through the groundwater recharge path to generate groundwater hydrological feature data;

[0022] Step S213: Obtain historical seasonal water level change data of the river channel; analyze the water level fluctuation amplitude based on the water level change data, and combine the historical seasonal water level change data of the river channel to analyze the water level fluctuation characteristics to generate river channel water level fluctuation data; evaluate the flow characteristics of the river channel water level fluctuation data to generate river channel hydrological feature data;

[0023] Step S214: Integrate the reservoir hydrological feature data, groundwater hydrological feature data, and river channel hydrological feature data to generate a hydrological feature data set.

[0024] Through the calculation of the storage capacity change rate of water storage - precipitation data, the present invention accurately evaluates the water storage dynamics of the reservoir, and combines with the water level change data to calculate the recharge contribution rate, enabling the reservoir hydrological characteristic data to finely reflect the recharge contribution of the reservoir water volume. This process not only reflects the water volume change trend of the reservoir, but also captures the response pattern of the water storage system before and after drought, ensuring the scientific basis for subsequent drought early warning. Through the collaborative analysis of surface infiltration data and underground aquifer recharge data, the groundwater recharge path is generated, and further in - depth analysis of the balance between aquifer recharge and consumption is carried out. This process not only reveals the dynamic changes of groundwater resources, but also makes the recharge and loss pathways of groundwater clearer through the mapping of infiltration paths. Through this data - driven balance analysis, the generated groundwater hydrological characteristic data can make a more forward - looking judgment on the response of the groundwater system in drought situations, thus improving the accuracy of the early warning mechanism. The introduction of historical river channel seasonal water level change data enables the accurate quantification of the water level fluctuation characteristics of the river channel. By combining the analysis of the water level fluctuation amplitude with historical data, detailed river channel water level fluctuation data are formed, providing a key reference for the subsequent evaluation of flow characteristics. The evaluation of flow characteristics not only captures the dynamic changes of river channel water resources in different seasons, but also reveals the relationship between flow changes and the occurrence of drought through data analysis, enabling the river channel hydrological characteristic data to provide a reliable quantitative basis for drought early warning. By integrating the hydrological characteristic data of the reservoir, groundwater, and river channel, a unified hydrological characteristic data set is generated. This data set integrates the hydrological characteristics of different water sources, ensuring the comprehensiveness and accuracy of the early warning threshold algorithm. Through the construction of this data set, the present invention can real - time monitor the hydrological change trends of different water sources and provide accurate drought early warning references.

[0025] Preferably, step S22 includes the following steps:

[0026] Step S221: Obtain the historical drought data of the reservoir; conduct a comparative analysis of the reservoir storage capacity change rate recharge data and the historical drought data of the reservoir to generate the reservoir water level drought critical point data; conduct a comparison of the critical storage capacity change threshold for the reservoir storage capacity change rate recharge data to generate the first - layer threshold for reservoir drought early warning; calculate the minimum recharge rate of the internal reservoir water storage for the reservoir hydrological characteristic data to generate the second - layer threshold for reservoir drought early warning; generate a dynamic curve of the reservoir warning threshold by combining the reservoir hydrological characteristic data, the first - layer threshold for drought early warning, and the second - layer threshold for reservoir drought early warning; mark the warning critical value for the dynamic curve of the reservoir warning threshold to generate the reservoir drought disaster data;

[0027] Step S222: Obtain historical surface infiltration data and historical groundwater drought data; draw historical recharge paths for the historical surface infiltration data to generate historical groundwater recharge paths; conduct infiltration fracture detection on the groundwater recharge paths and the historical groundwater recharge paths, and generate the first-layer groundwater threshold based on the fracture degree; conduct time-series analysis of the groundwater level for the surface infiltration data and the underground aquifer recharge data, and conduct minimum threshold analysis based on the historical groundwater drought data to generate the second-layer groundwater threshold; conduct analysis of the fluctuation safety range based on the groundwater hydrological characteristic data, the first-layer groundwater threshold, and the second-layer groundwater threshold, and conduct analysis of drought disaster points based on the groundwater recharge path to generate groundwater drought disaster data;

[0028] Step S223: Conduct minimum river water level threshold analysis on the historical seasonal water level change data of the river course, and confirm the minimum threshold using the river water hydrological characteristic data to generate the first-layer river water threshold; conduct analysis of the dynamic critical value of the flow rate based on the river water hydrological characteristic data to generate the second-layer river water threshold; bind the trigger conditions for the water level change data, the first-layer river water threshold, and the second-layer river water threshold, and mark the trigger time point to generate river water drought disaster data;

[0029] Step S224: Uniformly quantify the thresholds of the reservoir drought disaster data, the groundwater drought disaster data, the river water drought disaster data, and the hydrological characteristic data set, and generate an algorithm to generate a multi-source drought early warning threshold calculation algorithm.

[0030] Through the comparative analysis of the historical drought data of the reservoir and the replenishment data of the reservoir storage capacity change rate, the drought critical point data of the reservoir water level is generated, and through the calculation of the critical storage capacity change threshold, the first-layer threshold of the reservoir drought warning is formed. This step generates the second-layer threshold through the calculation of the lowest replenishment rate of the reservoir hydrological characteristic data, making the warning threshold of the reservoir have a more comprehensive dynamic performance. By generating the dynamic curve of the reservoir warning threshold and marking the key warning critical values therein, the reservoir drought disaster data is formed. This process not only improves the response ability of the reservoir in drought prediction, but also provides an accurate reference for the subsequent hydrological system warning through the marking of the dynamic curve. The penetration fracture detection is carried out on the historical groundwater recharge path and the existing groundwater recharge path, and the first-layer threshold is generated based on the fracture degree. Through the time series analysis of the surface penetration data and the underground aquifer recharge data, combined with the historical groundwater drought data, the second-layer threshold is further generated. This step obtains the groundwater drought disaster data through the analysis of the fluctuation safety range of the groundwater characteristic data and the two-layer threshold, and the analysis of the drought disaster points based on the groundwater recharge path. The generation of this data enables the groundwater resources to have a multi-dimensional analysis basis in drought warning, and improves the timeliness and accuracy of drought warning through the introduction of time series data. The historical seasonal water level change data of the river channel is used for the analysis of the lowest river channel water level threshold, and the dynamic critical value of the flow rate is confirmed in combination with the river channel hydrological characteristic data. By binding the trigger conditions of the first-layer and second-layer thresholds, the river channel water drought disaster data is generated. This process makes the critical point trigger of the river channel water resources in drought warning more accurate through the dynamic critical value analysis, providing a scientific basis for the warning of the river channel water characteristics in the multi-source hydrological system. By uniformly quantifying the drought disaster data of the reservoir, groundwater and river channel water and combining with the hydrological characteristic data set, a multi-source drought warning threshold calculation algorithm is generated. This algorithm integrates multi-level warning thresholds and dynamic data, ensuring the comprehensiveness and efficiency of the multi-source drought warning system.

[0031] Preferably, step S3 includes the following steps:

[0032] Step S31: Perform dimensional analysis on the hydrological characteristic data set to generate multi-dimensional input data, where the multi-dimensional input data includes reservoir dimension data, groundwater dimension data, and river channel water dimension data;

[0033] Step S32: Use the reservoir dimension data, groundwater dimension data, and river channel water dimension data to construct a multi-dimensional model for the multi-source drought warning threshold calculation algorithm to generate a multi-source hydrological drought warning and prediction model.

[0034] Through dimensional analysis of the hydrological feature dataset, the present invention effectively processes multi-source hydrological data such as reservoir water, groundwater, and river water in multiple dimensions to generate multi-dimensional input data. These input data respectively correspond to reservoir dimension data, groundwater dimension data, and river water dimension data, and can fully reflect the unique characteristics and mutual correlations of each water source system. Dimensional analysis not only effectively separates the hydrological information of different water sources, but also lays a foundation for the subsequent construction of multi-dimensional models through comprehensive processing of each dimension data. This multi-dimensional data decomposition method enables the data characteristics of each water source to be fully retained, while ensuring the correlation and independence between dimensions in a complex environment. By using these multi-dimensional input data and applying them to the calculation algorithm of the multi-source drought warning threshold, a multi-dimensional drought warning and prediction model is constructed. This process combines the dynamic characteristics between different hydrological systems and realizes the joint modeling of multi-source hydrological systems. Specifically, the reservoir dimension data provides key information such as reservoir capacity changes and historical drought data for the model, the groundwater dimension data ensures the warning accuracy of groundwater resources through the infiltration path and the recharge dynamics of the underground aquifer, and the river water dimension data provides real-time prediction support for river water resources through flow changes and river water level fluctuations. Through the deep integration of each dimension data, the model can accurately reflect the dynamic changes of the multi-source hydrological system and realize multi-dimensional and all-round monitoring of drought warning. The multi-dimensional characteristics of this model endow it with stronger ability to cope with complex environments. It can not only improve the accuracy of drought warning through the independence of each water source dimension, but also enhance the model's prediction ability for future hydrological events through the mutual correlation between dimensions. Compared with the traditional single-source warning model, the construction of the multi-dimensional model significantly improves the adaptability of the model to different hydrological characteristics, making drought warning more comprehensive and flexible.

[0035] Preferably, step S32 includes the following steps:

[0036] Step S321: Perform infiltration complementary correlation on the reservoir dimension data and the groundwater dimension data to generate reservoir-groundwater associated interaction data;

[0037] Step S322: Perform water level-capacity complementary correlation on the river water dimension data and the reservoir dimension data to generate river-reservoir associated interaction data;

[0038] Step S323: Perform time-series change complementary correlation on the groundwater dimension data and the river water dimension data to generate groundwater-river associated interaction data;

[0039] Step S324: Construct a hydrological multi-dimensional interaction model for the multi-dimensional input data, reservoir-groundwater associated interaction data, river-reservoir associated interaction data, and groundwater-river associated interaction data to generate a multi-source hydrological drought warning and prediction model.

[0040] In the present invention, by performing infiltration and complementary association on reservoir dimension data and groundwater dimension data, reservoir-groundwater associated interaction data is generated. This process is based on the dynamic relationship between reservoir storage capacity and groundwater recharge, capturing the interaction characteristics of the two during the infiltration and recharge processes, thereby realizing the quantification of the complex balance between reservoir water storage and groundwater recharge. This provides a key basis for the drought warning system because changes in storage capacity and groundwater level are often early signals for drought warning. By performing water level and storage capacity complementary association on river channel water dimension data and reservoir dimension data, river channel-reservoir associated interaction data is generated. In this process, by combining historical data on river channel water level changes and reservoir storage capacity fluctuations, a complementary interaction model between the two is generated, which can better reflect the dynamic balance between river channel water resources and reservoir water storage. This associated interaction analysis improves the ability to analyze the mutual dependence between river channel water and reservoir water resources under extreme climate conditions, and further improves the response speed to sudden drought events in the hydrological system. By performing time-series change complementary association on groundwater dimension data and river channel water dimension data, groundwater-river channel associated interaction data is generated. This step is based on the time-series changes of groundwater level and river channel water level, and combines seasonal fluctuations in different historical periods to construct a time-series dynamic model, reflecting the interaction between groundwater and river channel water. This time-series association enables the model to capture the dynamic process of the mutual influence between groundwater and river channel water. Especially in the initial stage of drought, the decline of river channel water level is often accompanied by changes in groundwater level, so this step plays a key role in drought prediction. By constructing a multi-dimensional hydrological interaction model for multi-dimensional input data and various generated interaction data, a multi-source hydrological drought warning and prediction model is generated. This model not only synthesizes the characteristics of multiple dimensions such as reservoirs, groundwater, and river channel water, but also further improves the model's ability to reflect the complex relationships between various water source systems through the introduction of interaction data. This multi-dimensional interaction model can capture the dynamic complementary effects between different water source systems, generate high-precision drought warning data, and make the entire drought warning system more comprehensive and intelligent.

[0041] Preferably, step S4 includes the following steps:

[0042] Step S41: Obtain real-time hydrological data, where the real-time hydrological data includes reservoir real-time data, groundwater real-time data, and river channel water real-time data;

[0043] Step S42: Use the multi-source hydrological drought warning and prediction model to perform multi-dimensional drought prediction in combination with real-time hydrological data, generating drought disaster prediction data, thereby completing the drought disaster prediction operation, where the drought disaster prediction data includes reservoir drought prediction data, groundwater level water supply drought prediction data, and river channel water supply drought prediction data.

[0044] The present invention obtains key hydrological data in real time from multiple hydrological dimensions such as reservoirs, groundwater, and river channels, such as changes in reservoir storage capacity, fluctuations in groundwater levels, and river channel water supply capabilities. Among them, the real-time data of the reservoir mainly includes water level data and reservoir storage capacity data. The real-time data of groundwater mainly includes groundwater level data and spring flow data. The real-time data of river channel water mainly includes river channel water level data and river channel flow data. The acquisition of these real-time data not only improves the dynamic update ability of the model but also can capture abnormal changes occurring in the hydrological system, ensuring the timeliness and accuracy of drought early warnings. Further, by using a multi-source hydrological drought early warning and prediction model, these real-time data are incorporated into the model operation framework for multi-dimensional drought prediction. This model integrates the dynamic relationships of the reservoir, groundwater, and river channel water systems and can update the drought conditions of different water source systems in a timely manner based on real-time data. Specifically, the reservoir drought prediction data is based on the real-time reservoir storage capacity and water supply capacity. Through the dynamic changes in the reservoir storage capacity, combined with the designed water supply scale, actual water supply capacity, and water supply plan of rural water supply projects, the drought risk is evaluated. The groundwater supply drought prediction data relies on real-time groundwater level fluctuation information and, combined with historical data on groundwater recharge and consumption, predicts the possibility of groundwater depletion in future periods. The river channel water supply drought prediction data uses changes in river channel water levels and flows and, combined with the designed water supply scale, actual water supply capacity, and water supply plan of rural water supply projects, predicts the drought degree of the river channel water supply system. The beneficial effects of this real-time prediction mechanism are reflected in multiple aspects. First, by combining real-time data with the model, it can effectively capture early drought signals, improving the timeliness and response speed of drought early warnings. Second, the multi-dimensional prediction not only covers multiple water source systems such as reservoirs, groundwater, and river channels but also can dynamically reflect the mutual influences among these systems, such as the interaction between groundwater and river channel water supply or the mutual recharge relationship between reservoir storage capacity and the surrounding groundwater levels. Therefore, this model can comprehensively consider the water supply capabilities of multiple water sources and their changing trends, providing more comprehensive and accurate drought prediction data and significantly improving the accuracy and reliability of disaster prediction.

[0045] Preferably, step S42 includes the following steps:

[0046] Step S421: Use the multi-source hydrological drought early warning and prediction model to predict the time series drought time of the real-time reservoir data and generate reservoir drought prediction data;

[0047] Step S422: Use the multi-source hydrological drought early warning and prediction model to predict the water level recharge balance drought of the real-time groundwater data and generate groundwater supply drought prediction data;

[0048] Step S423: Use the multi-source hydrological drought early warning and prediction model to predict the water flow supply of the real-time river channel water data and generate river channel water supply drought prediction data;

[0049] Step S424: Send the reservoir drought prediction data, groundwater supply drought prediction data, and river channel water supply drought prediction data through the cloud platform, and remind of drought disasters, thus completing the drought disaster prediction and early warning operation.

[0050] The present invention performs time series analysis on the real-time data of the reservoir based on a multi-source hydrological drought early warning and prediction model, predicts the time window of drought occurrence through time series analysis of the reservoir storage capacity change, and generates accurate reservoir drought prediction data. This method can timely capture the downward trend of the reservoir water storage capacity, identify the reservoir drought risk, and make water resource scheduling and management more forward-looking. Focusing on the balance between groundwater recharge and consumption, by dynamically analyzing the real-time change data of the groundwater level, combining the groundwater recharge path and historical recharge data, groundwater supply drought prediction data is generated. This process can detect the risk of groundwater level depletion in advance by evaluating the balance between groundwater recharge and consumption. Especially in the dry season, through the comprehensive evaluation of water level fluctuations and recharge paths, the future groundwater supply capacity can be accurately predicted to prevent insufficient supply of the groundwater system. Further, the model is used to predict the water supply of the real-time flow data of the river channel water, evaluate the impact of river channel water level changes on the water supply capacity, and generate river channel water supply drought prediction data. This analysis is based on the comprehensive evaluation of flow characteristics, river channel water supply paths, and historical water level changes, can timely detect the decline of the river channel water supply capacity, and warn of the weak links in the water supply chain, thus preventing the water supply system from being severely affected during the drought period. Transmit various drought prediction data, including the drought prediction results of reservoirs, groundwater, and river channels, through the cloud platform, and remind relevant management personnel in real time. This operation not only ensures the immediate transmission of information, but also enables efficient processing and sharing of data through the cloud computing platform, making the early warning of drought disasters more rapid and comprehensive.

[0051] In this specification, a rural water supply project drought disaster prediction and early warning system is provided for implementing the above-mentioned rural water supply project drought disaster prediction and early warning method. The rural water supply project drought disaster prediction and early warning system includes:

[0052] Hydrological data processing module: Obtain a hydrological data set; perform data preprocessing on the hydrological data set to generate a hydrological core processing data set;

[0053] Hydrological feature extraction and early warning algorithm generation module: Extract data features from the hydrological core processing data set to generate a hydrological feature data set; quantify the hydrological feature data set to generate a multi-source drought early warning threshold calculation algorithm;

[0054] Multi - dimensional Interaction Model Construction Module: Using the multi - water - source drought warning threshold calculation algorithm and combining with the hydrological feature dataset to construct a multi - dimensional interaction model, generating a multi - source hydrological drought warning and prediction model;

[0055] Drought Disaster Prediction and Warning Module: Using the multi - source hydrological drought warning and prediction model to conduct multi - dimensional drought prediction, generating drought disaster prediction data, thus completing the drought disaster prediction and warning operation.

[0056] The beneficial effects of the present invention are as follows: By obtaining a multi - source hydrological dataset (including water storage - precipitation data, water level change data, surface infiltration data, and underground aquifer recharge data) and performing data pre - processing on it, a core hydrological processing dataset is generated. The key to this step lies in ensuring the effective integration of hydrological data of different water source types on the same platform, avoiding the processing complexity caused by differences in data sources. Through data feature extraction from the core processing dataset, accurate extraction of various water source characteristics such as reservoirs, groundwater, and river water is ensured. Further, through quantization processing, a drought warning threshold calculation algorithm is generated. This algorithm innovatively introduces multi - layer threshold settings and combines historical drought data for dynamic analysis, ensuring the flexibility and accuracy of the warning system. Through the quantization of the feature dataset, the organic combination of different water source data under the same algorithm framework is guaranteed, providing a solid data foundation for subsequent model construction. By interacting the drought warning threshold calculation algorithm with the hydrological feature dataset, a multi - dimensional drought warning and prediction model is constructed, further enhancing the accuracy and robustness of the system in predicting drought disasters. The multi - dimensional interaction model realizes accurate prediction under various hydrological conditions by integrating the hydrological characteristics and warning thresholds of different water sources, effectively improving the adaptability of the prediction model under different regions and different water source conditions. Using the multi - dimensional interaction model to conduct multi - dimensional prediction of drought disasters, generating specific disaster prediction data, thus providing comprehensive data support in the drought warning system and realizing the full - process automation and high efficiency of the prediction operation. Therefore, the present invention solves the problem of inaccurate prediction caused by inconsistent multi - source data in traditional warning systems by optimizing hydrological data processing and warning threshold calculation, greatly improving the accuracy and reliability of drought disaster warning. Description of the Drawings

[0057] Figure 1 It is a schematic diagram of the step - by - step process of a drought disaster prediction and warning method for rural water supply projects;

[0058] Figure 2 It is Figure 1 a schematic diagram of the detailed implementation steps of step S2 in

[0059] Figure 3 It is Figure 2 a schematic diagram of the detailed implementation steps of step S21 in

[0060] Figure 4 For Figure 2 the detailed implementation step flow schematic diagram of step S22 in

[0061] The realization, functional features and advantages of the object of the present invention will be further described in conjunction with the embodiments with reference to the accompanying drawings. Specific embodiments

[0062] The technical method of the present invention patent will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0063] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0064] It should be understood that although the terms "first", "second", etc. may be used here to describe each unit, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be called the second unit, and similarly the second unit may be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0065] To achieve the above object, please refer to Figures 1 to 4 , a method for predicting and warning drought disasters in rural water supply projects, the method includes the following steps:

[0066] Step S1: Obtain a hydrological data set; perform data preprocessing on the hydrological data set to generate a hydrological core processing data set;

[0067] Step S2: Extract data features from the hydrological core processing data set to generate a hydrological feature data set; quantify the hydrological feature data set to generate a multi-source drought warning threshold calculation algorithm;

[0068] Step S3: Use the multi-source drought warning threshold calculation algorithm and combine it with the hydrological feature dataset to construct a multi-dimensional interaction model, generating a multi-source hydrological drought warning and prediction model;

[0069] Step S4: Use the multi-source hydrological drought warning and prediction model to conduct multi-dimensional drought prediction, generating drought disaster prediction data, thereby completing the drought disaster prediction and warning operation.

[0070] The beneficial effects of the present invention are as follows: By obtaining a multi-source hydrological dataset (including water storage-precipitation data, water level change data, surface infiltration data, and groundwater aquifer recharge data) and performing data preprocessing on it, a hydrological core processing dataset is generated. The key to this step is to ensure the effective integration of hydrological data of different water source types on the same platform, avoiding the processing complexity caused by differences in data sources. By extracting data features from the core processing dataset, accurate extraction of various water source characteristics such as reservoirs, groundwater, and river water is ensured. Further, a drought warning threshold calculation algorithm is generated through quantization processing. This algorithm innovatively introduces multi-layer threshold settings and combines historical drought data for dynamic analysis to ensure the flexibility and accuracy of the warning system. Through the quantization of the feature dataset, the organic combination of different water source data under the same algorithm framework is guaranteed, providing a solid data foundation for subsequent model construction. By interacting the drought warning threshold calculation algorithm with the hydrological feature dataset, a multi-dimensional drought warning and prediction model is constructed, further enhancing the accuracy and robustness of the system in predicting drought disasters. The multi-dimensional interaction model realizes accurate prediction under various hydrological conditions by integrating the hydrological characteristics and warning thresholds of different water sources, effectively improving the adaptability of the prediction model under different regions and different water source conditions. Using the multi-dimensional interaction model to conduct multi-dimensional prediction of drought disasters and generating specific disaster prediction data, thereby providing comprehensive data support in the drought warning system and realizing the full-process automation and high efficiency of the prediction operation. Therefore, the present invention solves the problem of inaccurate prediction caused by inconsistent multi-source data in the traditional warning system by optimizing hydrological data processing and warning threshold calculation, greatly improving the accuracy and reliability of drought disaster warning.

[0071] In the embodiment of the present invention, referring to Figure 1 as described, it is a schematic diagram of the step flow of a drought disaster prediction and warning method for rural water supply projects of the present invention. In this example, the drought disaster prediction and warning method for rural water supply projects includes the following steps:

[0072] Step S1: Obtain a hydrological dataset; perform data preprocessing on the hydrological dataset to generate a hydrological core processing dataset;

[0073] In the embodiments of the present invention, the acquisition of the hydrological data set involves the real-time collection and integration of various hydrological factors, including water storage-precipitation data, water level change data, surface infiltration data, and groundwater aquifer recharge data, etc. These data are obtained in real time through means such as sensor networks, remote sensing technologies, and automatic data acquisition systems (SCADA) to ensure the timeliness and wide coverage of the data. The acquired data are in various forms, covering multi-dimensional information such as time series and spatial distribution characteristics. Therefore, the original data may contain redundant, missing, or noisy information, which requires effective data preprocessing in subsequent steps. Data preprocessing refers to operations such as cleaning, filtering, format unification, and elimination of invalid data on the acquired hydrological data set to ensure that the generated core hydrological processing data set has higher accuracy and consistency. In this process, data cleaning algorithms are first applied to remove noisy data and outliers, such as identifying and removing abnormal fluctuations or invalid water level records through time series detection algorithms. In addition, for the processing of missing data, interpolation methods (such as linear interpolation or multi-variate interpolation methods) can be used to complete the filling to ensure the integrity of the data set. Subsequently, data format unification is a key step. In order to make the data from different sources operable, various data types (such as time series and spatial distribution data) must be converted into the same format, with a unified time step and spatial grid format. This step can be achieved through data annotation tools, which automatically associate each piece of data with its corresponding timestamp and geographical location and clearly label them in the internal structure of the data set. The technical means for generating the core hydrological processing data set also includes standardizing the data. Specifically, in order to ensure the comparability between various hydrological indicators, it is necessary to perform normalization or standardization operations on data with different dimensions. Therefore, the standardization step adjusts different indicators to the same scale to facilitate the subsequent analysis and model construction.

[0074] Step S2: Extract data features from the core hydrological processing data set to generate a hydrological feature data set; quantify the hydrological feature data set to generate a multi-source drought early warning threshold calculation algorithm;

[0075] In the embodiments of the present invention, the feature extraction process includes statistical feature analysis and temporal feature analysis. For the hydrological core processing dataset, statistical feature analysis includes calculating statistics such as the mean, variance, and extreme values of key indicators such as water level, precipitation, and flow rate. These statistics can effectively reflect the overall trend and variation law of hydrological data. At the same time, temporal feature analysis can decompose the data into seasonal, trend, and periodic components through time series decomposition techniques to extract the characteristics of periodic changes and emergencies. To enhance the prediction ability of the model, time series modeling methods such as autoregressive model (AR) and moving average model (MA) are also used in the feature extraction process to model the hydrological data, so as to identify the key driving factors affecting hydrological characteristics. These methods can help identify important trends in the hydrological feature dataset and convert them into feature parameters that can be used for modeling. After extraction, the generated hydrological feature dataset will contain feature data related to multiple dimensions such as water source, groundwater, and river water, which can provide strong data support for subsequent drought early warning. The quantification of the hydrological feature dataset is a key step in generating the multi-source drought early warning threshold calculation algorithm. The quantification process involves converting the extracted features into quantitative indicators, which can be carried out through methods such as standardization and normalization to ensure that each feature is compared under the same measure. In the quantification process of hydrological features such as water level, precipitation, and evaporation, their percentage changes relative to historical data can be adjusted. This measure of relative change helps to more intuitively understand the impact of hydrological features on drought early warning. By training the model to identify the relationship between features and the occurrence of drought, a set of dynamic drought early warning thresholds can be obtained, which can reflect the weights and impacts of different hydrological features in drought early warning. Finally, the multi-source drought early warning threshold calculation algorithm is generated.

[0076] Step S3: Use the multi-source drought early warning threshold calculation algorithm and combine it with the hydrological feature dataset to construct a multi-dimensional interaction model to generate a multi-source hydrological drought early warning and prediction model;

[0077] In the embodiments of the present invention, the multi-source drought warning threshold calculation algorithm utilized is designed based on the hydrological feature data extracted in the previous step. This algorithm can integrate multiple hydrological data sources, identify the correlations between various water sources, quantify the drought risk, and generate warning thresholds applicable to multiple water sources. During the model construction process, it is first necessary to preprocess the hydrological feature data set to ensure the integrity and consistency of the data. This process includes data cleaning, missing value handling, and outlier detection to eliminate the noise that may mislead the model. Next, the hydrological feature data set is divided into a training set and a test set for subsequent model training and verification. The construction of the multi-dimensional interaction model is achieved by introducing machine learning or deep learning algorithms. Common methods include support vector machine (SVM), random forest (RF), and neural networks, etc. These algorithms can process high-dimensional feature data and identify the complex non-linear relationships between features. When training the model, using the variables in the hydrological feature data set as inputs and combining with the calculated warning thresholds, the model can learn the influence degree of different water sources on the drought risk. This multi-dimensional interaction feature enables the model to simultaneously consider the dynamic changes of multiple water sources such as reservoirs, groundwater, and river water, thereby improving the prediction accuracy of drought events. After the model training is completed, it is necessary to evaluate the model through technical means such as cross-validation to determine its generalization ability on unseen data. The evaluation metrics can include mean squared error (MSE), coefficient of determination (R 2 ) etc. These metrics can reflect the deviation degree between the predicted values and the actual values of the model, thereby ensuring the practicability and reliability of the constructed multi-source hydrological drought warning and prediction model. The multi-source hydrological drought warning and prediction model generated through the above steps can not only monitor the changes of hydrological features in real time but also be dynamically updated based on real-time data to achieve early warning of drought risks. This model provides effective data support for decision-makers, enabling them to take timely countermeasures to mitigate the impact of drought on water resource management and the ecological environment.

[0078] Step S4: Use the multi-source hydrological drought warning and prediction model to conduct multi-dimensional drought prediction and generate drought disaster prediction data, thereby completing the drought disaster prediction and warning operation.

[0079] In the embodiments of the present invention, the specific technical means for multi-dimensional drought prediction using a multi-source hydrological drought early warning and prediction model mainly focus on the collection, processing, and analysis of real-time hydrological data, aiming to generate accurate drought disaster prediction data for drought disaster early warning. This process first involves the acquisition of real-time hydrological data, specifically including real-time monitoring data from multiple water sources such as reservoirs, groundwater, and river water. These data are collected through channels such as sensors, satellite remote sensing, and hydrological monitoring stations to ensure the timeliness and accuracy of the data. The obtained real-time hydrological data then need to be preprocessed to eliminate noise and improve data quality. This process includes technical means such as data cleaning, standardization, and format conversion to facilitate subsequent analysis. On this basis, the processed real-time hydrological data will be used as input and fed into the multi-source hydrological drought early warning and prediction model. The model utilizes the multi-dimensional interaction characteristics constructed in the previous steps to identify the correlations between hydrological variables and their impacts on drought occurrence through dynamic analysis of the data from each water source. The model compares and analyzes the real-time hydrological data with historical data to identify potential drought risks and predict trends. By analyzing the temporal variations in reservoir water levels, groundwater levels, and river flows, the model can effectively evaluate the similarity of the current hydrological state compared to historical drought events and generate drought risk levels based on this. To further improve the accuracy of the prediction, the model uses time series analysis, regression analysis, or other machine learning algorithms to process multi-dimensional data and output drought disaster prediction data. The generated drought disaster prediction data includes the drought risk levels, warning thresholds, and probability distributions of occurrence for each water source.

[0080] Preferably, step S1 includes the following steps:

[0081] Step S11: Obtain a hydrological data set, where the hydrological data set includes storage-precipitation data, water level change data, surface infiltration data, and groundwater aquifer recharge data;

[0082] Step S12: Perform data set cleaning processing on the hydrological data set to generate hydrological cleaning data;

[0083] Step S13: Perform data set format standardization on the hydrological cleaning data to generate a hydrological core processing data set.

[0084] In the embodiments of the present invention, by integrating multiple hydrological data sources, including water storage-precipitation data, water level change data, surface infiltration data, and groundwater aquifer recharge data, a multi-dimensional data set is formed. This process requires the adoption of standardized data acquisition methods to ensure the accuracy and consistency of the data, and data is extracted from real-time rain and water regime monitoring systems and historical rain and water regime databases using APIs or data crawler technologies. Data cleaning processing is performed on the hydrological data set, and this process involves removing redundant data, filling in missing values, and eliminating outliers. Cleaning techniques such as Z-score and IQR methods are applied to data anomaly detection to ensure the integrity and accuracy of the data set. At the same time, data standardization techniques are used to uniformly process data from different sources to make them have the same dimension and range for subsequent analysis. Format standardization processing is performed on the cleaned hydrological data. By defining data tags and metadata, the core processed data set is given structured attributes. This stage usually adopts feature engineering techniques in machine learning. Through feature selection and transformation, the original data is converted into a format suitable for model training. In addition, data frames (such as Pandas) are used to reshape and optimize the data frame to ensure that the generated core processed hydrological data set has good readability and operability, providing a solid data foundation for subsequent analysis and modeling.

[0085] As an example of the present invention, refer to Figure 2 shown, in this example, step S2 includes:

[0086] Step S21: Extract data features from the core processed hydrological data set to generate a hydrological feature data set;

[0087] Step S22: Confirm the warning threshold according to the hydrological feature data set, and quantify the warning through the warning threshold, thereby generating a multi-source drought warning threshold calculation algorithm.

[0088] In the embodiments of the present invention, the specific technical means involve in-depth analysis and processing of the core hydrological processing data set to extract key features and establish an effective calculation algorithm for drought warning thresholds. First, in step S21, data feature extraction is performed on the core hydrological processing data set by using a variety of statistical analysis and machine learning techniques to identify the main features of hydrological variables. This process includes descriptive statistical analysis of hydrological data, such as the calculation of basic statistics such as mean, variance, and extreme values, to help understand the distribution characteristics of hydrological data. In addition, dimensionality reduction techniques such as principal component analysis (PCA) are also applied to reduce the complexity of the data while retaining important hydrological feature information. Feature extraction is not limited to the analysis of numerical features, but also includes the extraction of time series features, seasonal trends, periodic fluctuations, and emergencies, etc. These information are crucial for drought warning. Based on the extracted hydrological feature data set, the confirmation and quantification of the warning threshold are carried out. The specific technical means include the threshold setting and verification process. First, the relationship between historical drought events and current hydrological features is analyzed by statistical methods to determine the drought warning threshold. The percentile method or the standard deviation method can be used to set the thresholds of variables such as water level, flow rate, and precipitation according to the distribution of historical data. The water supply capacity of the water source at different times (such as legal holidays, weekdays, weekends, etc.) can also be set according to the distribution of historical situations to ensure that these thresholds can accurately reflect the changes in drought risks. In addition, machine learning models such as support vector machine (SVM) or random forest (RF) are used to predict the occurrence of drought, and the threshold setting process is further optimized. Through repeated verification of the model prediction results, a more accurate calculation algorithm for multi-source drought warning thresholds can be formed.

[0089] As an example of the present invention, refer to Figure 3 shown, in this example, step S21 includes:

[0090] Step S211: Calculate and evaluate the storage capacity change rate of the water storage-precipitation data to generate the reservoir storage capacity change rate replenishment data; calculate the contribution rate of reservoir water replenishment between the reservoir storage capacity change rate replenishment data and the water level change to generate the reservoir hydrological feature data;

[0091] Step S212: Draw the groundwater recharge path for the surface infiltration data to generate the groundwater recharge path; perform the balance analysis of aquifer recharge and consumption on the surface infiltration data and the underground aquifer recharge data through the groundwater recharge path to generate the groundwater hydrological feature data;

[0092] Step S213: Obtain the historical seasonal water level change data of the river channel; analyze the water level fluctuation amplitude according to the water level change data, and combine the historical seasonal water level change data of the river channel to analyze the water level fluctuation characteristics to generate the river channel water level fluctuation data; evaluate the flow characteristics of the river channel water level fluctuation data to generate the river channel hydrological feature data;

[0093] Step S214: Integrate the reservoir hydrological characteristic data, groundwater hydrological characteristic data, and river channel water hydrological characteristic data to generate a hydrological characteristic data set.

[0094] In the embodiments of the present invention, it mainly involves multi-level data analysis and model construction to comprehensively evaluate hydrological characteristics and generate a comprehensive hydrological characteristic data set. By calculating the storage capacity change rate of the water storage-precipitation data and using time series analysis methods, the change rate of the storage capacity is calculated. This process involves performing a correlation analysis on historical storage capacity data and precipitation data to determine the direct impact of precipitation on the change of the storage capacity and generating reservoir storage capacity change rate replenishment data. Subsequently, in combination with the change of the reservoir water level, the calculation of the water supply contribution rate is carried out, and statistical methods such as linear regression analysis are applied to identify the relationship between the water level change and the reservoir capacity replenishment, and finally the reservoir hydrological characteristic data is generated, which provides a quantitative basis for understanding the water volume dynamics of the reservoir. The drawing of the groundwater recharge path of the surface infiltration data involves Geographic Information System (GIS) technology, and the flow path of surface water to groundwater is depicted through spatial analysis tools. This process includes integrating and analyzing the spatial distribution of permeability, soil type, and groundwater level to generate the groundwater recharge path. At the same time, in combination with the surface infiltration data and the groundwater aquifer recharge data, the balance analysis of aquifer recharge and consumption is carried out, and the water balance equation is used to quantify the contribution of different sources to groundwater recharge and generate groundwater hydrological characteristic data. The acquisition and analysis of historical river channel seasonal water level change data involve time series statistical techniques and fluctuation analysis methods. By calculating the water level fluctuation amplitude and extracting features in combination with historical data, the seasonal water level change pattern can be identified and river channel water level fluctuation data can be generated. In addition, when evaluating the flow characteristics of the river channel water level fluctuation data, flow calculation formulas and empirical models are applied to reveal the relationship between the water level change and the flow rate, and further generate river channel water hydrological characteristic data. Integrate the hydrological characteristic data of the reservoir, groundwater, and river channel, and use multivariate statistical analysis techniques such as principal component analysis (PCA) or cluster analysis to integrate the data from different sources into a unified hydrological characteristic data set.

[0095] As an example of the present invention, refer to Figure 4 As shown, in this example, the said step S22 includes:

[0096] Step S221: Obtain the historical drought data of the reservoir; conduct a comparative analysis of the reservoir storage capacity change rate replenishment data and the historical drought data of the reservoir to generate the reservoir water level drought critical point data; conduct a comparison of the critical reservoir storage capacity change threshold for the reservoir storage capacity change rate replenishment data to generate the first-layer threshold for reservoir drought warning; calculate the minimum replenishment rate of the internal reservoir storage for the reservoir hydrological characteristic data to generate the second-layer threshold for reservoir drought warning; generate a dynamic curve of the reservoir warning threshold by generating a dynamic curve from the reservoir hydrological characteristic data, the first-layer threshold for drought warning, and the second-layer threshold for reservoir drought warning; mark the warning critical value for the dynamic curve of the reservoir warning threshold to generate the reservoir drought disaster data;

[0097] Step S222: Obtain the historical surface infiltration data and the historical groundwater drought data; draw the historical recharge path for the historical surface infiltration data to generate the historical groundwater recharge path; conduct infiltration fracture detection on the groundwater recharge path and the historical groundwater recharge path, and generate the first-layer threshold for groundwater based on the fracture degree; conduct a time series analysis of the groundwater level for the surface infiltration data and the underground aquifer recharge data, and conduct a minimum threshold analysis based on the historical groundwater drought data to generate the second-layer threshold for groundwater; conduct a fluctuating safety range analysis based on the groundwater hydrological characteristic data, the first-layer threshold for groundwater, and the second-layer threshold for groundwater, and conduct a drought disaster point analysis based on the groundwater recharge path to generate the groundwater drought disaster data;

[0098] Step S223: Conduct a minimum river channel water level threshold analysis on the historical seasonal water level change data of the river channel, and confirm the minimum threshold using the river channel water hydrological characteristic data to generate the first-layer threshold for river channel water; conduct a dynamic critical value analysis of the flow rate based on the river channel water hydrological characteristic data to generate the second-layer threshold for river channel water; bind the trigger conditions for the water level change data, the first-layer threshold for river channel water, and the second-layer threshold for river channel water, and mark the trigger time point to generate the river channel water drought disaster data;

[0099] Step S224: Uniformly quantify the thresholds of the reservoir drought disaster data, the groundwater drought disaster data, the river channel water drought disaster data, and the hydrological characteristic data set, and generate an algorithm to generate a multi-source drought warning threshold calculation algorithm.

[0100] In the embodiments of the present invention, by obtaining the historical drought data of the reservoir and combining it with the replenishment data of the reservoir storage capacity change rate, a comparative analysis is carried out to identify the critical point of reservoir drought. This analysis process uses regression analysis or correlation analysis methods in statistics to establish a relationship model between the reservoir water level change and the occurrence of drought, so as to generate the critical point data of the reservoir water level drought. In addition, by comparing the critical storage capacity change threshold and using threshold judgment technology, the first layer threshold of reservoir drought warning is further generated. The setting of this layer of threshold is based on the comprehensive analysis of historical storage capacity data, enabling the warning system to respond in a timely manner to the dynamic changes of the reservoir water level. Then, the minimum replenishment rate of the reservoir storage water is calculated for the hydrological characteristic data, and the second layer threshold of the reservoir drought warning is generated using the water balance equation, which provides data support for multi-level drought warning. After obtaining the historical surface infiltration data and the historical groundwater drought data, the replenishment path of the historical surface infiltration data is drawn, which involves the application of geographic information system (GIS), and uses spatial analysis technology to depict the change of the groundwater replenishment path. The groundwater replenishment path and the historical groundwater replenishment path are subjected to infiltration fracture detection, and pattern recognition technology is used to identify the anomalies in the infiltration path, and the first layer threshold of groundwater is generated based on the degree of fracture. Next, through the time series analysis of the groundwater level of the surface infiltration data and the underground aquifer replenishment data, using the time series analysis method and combining with the historical groundwater drought data for the minimum threshold analysis, the second layer threshold of groundwater is finally generated. This series of steps effectively ensures the dynamic monitoring and drought warning of groundwater resources. The minimum river water level threshold analysis of the historical river season water level change data relies on time series analysis technology, and uses the flow dynamic critical value analysis to establish the relationship between the river water flow and the water level. The generated first layer threshold of river water and the second layer threshold of river water bind the water level change data and the river water threshold to the trigger condition, so as to mark the time point of drought trigger and form the river water drought disaster data. The drought disaster data and hydrological characteristic data sets of the reservoir, groundwater and river water are uniformly quantified by threshold, and a multi-source drought warning threshold calculation algorithm is generated using the multivariate statistical analysis method.

[0101] Preferably, step S3 includes the following steps:

[0102] Step S31: Perform dimensional analysis on the hydrological characteristic data set to generate multi-dimensional input data, where the multi-dimensional input data includes reservoir dimension data, groundwater dimension data and river water dimension data;

[0103] Step S32: Use the reservoir dimension data, groundwater dimension data and river water dimension data to construct a multi-dimensional model for the multi-source drought warning threshold calculation algorithm to generate a multi-source hydrological drought warning and prediction model.

[0104] In the embodiments of the present invention, it first involves the dimensionality analysis of the hydrological feature dataset. The purpose is to extract hydrological features from different sources in the original data and construct a multi-dimensional input dataset. The multi-dimensional input data consists of reservoir-dimensional data, groundwater-dimensional data, and river water-dimensional data. These data are processed through statistical analysis and data mining techniques to reveal the relationships and characteristics between different water sources. In this process, methods such as principal component analysis (PCA) or factor analysis are first used to reduce the dimensionality and extract features of the hydrological feature dataset. These methods can effectively transform high-dimensional data into a representation in a low-dimensional space, retain the main variation information of the data, and reduce redundancy. The application of this technology not only improves the efficiency of data processing but also enhances the interpretability and accuracy of the model. The generated multi-dimensional input dataset lays a solid foundation for the subsequent model construction. The multi-dimensional model is constructed using reservoir-dimensional data, groundwater-dimensional data, and river water-dimensional data to realize the comprehensive application of the multi-source drought warning threshold calculation algorithm. This process mainly realizes the integration and analysis of multi-source hydrological data through technologies such as constructing a multiple linear regression model, support vector machine (SVM), or deep learning algorithm. These technologies can handle non-linear relationships and complex data structures, enabling the model to accurately reflect the mutual influence between different water sources. During the model construction process, first, the above multi-dimensional input data needs to be standardized to eliminate the influence of dimensions and improve the convergence speed and stability of the model. Then, through the division of the training set and the test set, the cross-validation method is used to evaluate the performance of the model to ensure its generalization ability in practical applications. Finally, the generated multi-source hydrological drought warning and prediction model can output drought warning information in a timely manner based on real-time input data.

[0105] Preferably, step S32 includes the following steps:

[0106] Step S321: Perform infiltration and complementary association on the reservoir-dimensional data and the groundwater-dimensional data to generate reservoir-groundwater associated interaction data;

[0107] Step S322: Perform water level-capacity complementary association on the river water-dimensional data and the reservoir-dimensional data to generate river-reservoir associated interaction data;

[0108] Step S323: Perform time-series change complementary association on the groundwater-dimensional data and the river water-dimensional data to generate groundwater-river associated interaction data;

[0109] Step S324: Construct a hydrological multi-dimensional interaction model for the multi-dimensional input data, reservoir-groundwater associated interaction data, river-reservoir associated interaction data, and groundwater-river associated interaction data to generate a multi-source hydrological drought warning and prediction model.

[0110] In the embodiments of the present invention, the reservoir dimension data and the groundwater dimension data are infiltrated and complementarily correlated. This technical means uses correlation analysis and regression models in statistics to explore the mutual influence between the reservoir and the groundwater. By analyzing the relationship between the water storage capacity of the reservoir and the change of the groundwater level, reservoir-groundwater associated interaction data can be generated to reveal the dynamic balance of water resources and the mechanism of mutual recharge. The river water dimension data and the reservoir dimension data are complementarily correlated in terms of water level and storage capacity. Here, time series data analysis methods such as time series models (ARIMA) or moving average methods are used to analyze the dynamic relationship between the change of the river water level and the reservoir water level. Through this correlation, river-reservoir associated interaction data can be generated, further enriching the understanding and early warning ability of the hydrological system. The groundwater dimension data and the river water dimension data are complementarily correlated in terms of time series changes. Time series analysis techniques are adopted, especially algorithms such as dynamic time warping (DTW) are used to analyze the mutual influence between the groundwater and the river water at different time nodes. The groundwater-river associated interaction data generated in this process helps to identify the causal relationship between groundwater recharge and the change of river water flow, so as to achieve more accurate drought prediction. For multi-dimensional input data, reservoir-groundwater associated interaction data, river-reservoir associated interaction data, and groundwater-river associated interaction data, a hydrological multi-dimensional interaction model is constructed. Here, multiple regression analysis, machine learning algorithms (such as random forest or support vector machine), and deep learning models (such as LSTM) are applied to integrate multiple data sources and extract the complex relationships between various hydrological dimensions.

[0111] Preferably, step S4 includes the following steps:

[0112] Step S41: Obtain real-time hydrological data, where the real-time hydrological data includes reservoir real-time data, groundwater real-time data, and river water real-time data;

[0113] Step S42: Use the multi-source hydrological drought early warning and prediction model to combine the real-time hydrological data for multi-dimensional drought prediction, generate drought disaster prediction data, and thus complete the drought disaster prediction operation, where the drought disaster prediction data includes reservoir drought prediction data, groundwater supply drought prediction data, and river water supply drought prediction data.

[0114] In the embodiments of the present invention, obtaining real-time hydrological data is the basic work, involving multi-dimensional data of reservoirs, groundwater, and river channels. Among them, the real-time reservoir data mainly includes water level data and reservoir storage capacity data. The real-time groundwater data mainly includes groundwater level data and spring flow data. The real-time river water data mainly includes river water level data and river flow data. This process uses an automated data acquisition system to collect various hydrological data in real time through sensors and monitoring devices, including water level, flow rate, precipitation, and changes in groundwater level. These data are integrated through Internet of Things technology to form a real-time hydrological database, providing raw data support for subsequent analysis. The multi-source hydrological drought warning and prediction model is used to analyze the real-time hydrological data, mainly adopting a combination of statistical and machine learning methods. Through quality control and screening of the real-time data, the accuracy and reliability of the data used are ensured. Then, time series analysis techniques, such as ARIMA (Autoregressive Integrated Moving Average Model), are used to model the historical data to mine the time series characteristics of the hydrological data for predicting future hydrological conditions. In addition, combining the real-time data with the existing multi-source hydrological drought warning model, deep learning algorithms (such as LSTM, i.e., Long Short-Term Memory Network) are used to capture the complex non-linear relationships in the data and improve the accuracy of drought prediction. When generating drought disaster prediction data, it specifically includes reservoir drought prediction data, groundwater supply drought prediction data, and river supply drought prediction data. To achieve this goal, the model conducts comprehensive analysis through multi-dimensional input data to generate drought risk assessments for different water sources. The reservoir drought prediction data evaluates the possibility of future drought occurrence by analyzing the changes in reservoir water level and the trend of water storage volume, combined with historical drought events. The groundwater supply drought prediction data focuses on the balance between groundwater recharge and consumption to identify potential risks of water level decline. The river supply drought prediction data evaluates the sustainability and reliability of river supply through flow dynamic analysis, combined with precipitation data.

[0115] Preferably, step S42 includes the following steps:

[0116] Step S421: Use the multi-source hydrological drought warning and prediction model to predict the time series drought time of the real-time reservoir data and generate reservoir drought prediction data;

[0117] Step S422: Use the multi-source hydrological drought warning and prediction model to predict the drought of water level recharge balance of the real-time groundwater data and generate groundwater supply drought prediction data;

[0118] Step S423: Use the multi-source hydrological drought warning and prediction model to predict the water flow supply of the real-time river water data and generate river supply drought prediction data;

[0119] Step S424: Send the reservoir drought prediction data, groundwater supply drought prediction data, and river channel supply drought prediction data through the cloud platform and alert of drought disasters, thus completing the drought disaster prediction and early warning operation.

[0120] In the embodiment of the present invention, a multi-source hydrological drought early warning and prediction model is used to predict the timing drought time of the real-time reservoir data. This process adopts time series analysis technology, combines historical data with real-time monitoring data, and models the change of the reservoir water level through methods such as autoregressive model, moving average, and seasonal trend analysis to identify potential time nodes of drought occurrence. In addition, by calculating the correlation between the reservoir water storage and historical drought events, the accuracy of the prediction is further improved to generate reservoir drought prediction data. The water level recharge balance drought prediction of the groundwater real-time data also relies on the multi-source hydrological drought early warning and prediction model. Here, the model dynamically monitors the recharge and consumption of groundwater, combines data on precipitation, evaporation, and surface permeability, and implements the analysis and prediction of the water level change trend, and then generates groundwater supply drought prediction data. This process adopts the balance equation model, emphasizing the dynamic change of the water source recharge ability to reflect the health status of the groundwater level and its sensitivity to drought risk. The water flow supply prediction of the river channel water real-time data uses a similar modeling idea. Through real-time water flow monitoring, combined with historical flow change data, the change trend of the river channel water supply is predicted by using flow regression analysis, machine learning algorithms (such as random forest regression, etc.) to generate river channel supply drought prediction data. This method not only considers the seasonal change of the flow, but also introduces the correlation between meteorological factors and hydrological characteristics to achieve a comprehensive assessment of the river channel water supply capacity. The generated reservoir drought prediction data, groundwater supply drought prediction data, and river channel supply drought prediction data are sent through the cloud platform to achieve real-time information sharing and drought disaster alert.

[0121] In this specification, a rural water supply project drought disaster prediction and early warning system is provided for implementing the above-mentioned rural water supply project drought disaster prediction and early warning method. The rural water supply project drought disaster prediction and early warning system includes:

[0122] Hydrological data processing module: Obtain a hydrological data set; perform data preprocessing on the hydrological data set to generate a hydrological core processing data set;

[0123] Hydrological feature extraction and early warning algorithm generation module: Extract data features from the hydrological core processing data set to generate a hydrological feature data set; quantify the hydrological feature data set to generate a multi-source drought early warning threshold calculation algorithm;

[0124] Multi-dimensional interaction model construction module: Use the multi-source drought early warning threshold calculation algorithm and combine with the hydrological feature data set to construct a multi-dimensional interaction model to generate a multi-source hydrological drought early warning and prediction model;

[0125] Drought disaster prediction and early warning module: Using a multi-source hydrological drought early warning and prediction model to conduct multi-dimensional drought prediction and generate drought disaster prediction data, thereby completing the drought disaster prediction and early warning operation.

[0126] The beneficial effects of the present invention are as follows: By obtaining a multi-source hydrological data set (including water storage-precipitation data, water level change data, surface infiltration data, and underground aquifer recharge data) and performing data preprocessing on it, a hydrological core processing data set is generated. The key to this step is to ensure the effective integration of hydrological data of different water source types on the same platform, avoiding the processing complexity caused by differences in data sources. By extracting data features from the core processing data set, accurate extraction of various water source features such as reservoirs, groundwater, and river water is ensured. Further, a drought warning threshold calculation algorithm is generated through quantization processing. This algorithm innovatively introduces multi-layer threshold setting and combines historical drought data for dynamic analysis to ensure the flexibility and accuracy of the early warning system. Through the quantization of the feature data set, the organic combination of different water source data under the same algorithm framework is ensured, providing a solid data foundation for subsequent model construction. By interacting the drought warning threshold calculation algorithm with the hydrological feature data set, a multi-dimensional drought early warning and prediction model is constructed, further enhancing the accuracy and robustness of the system in predicting drought disasters. The multi-dimensional interaction model realizes accurate prediction under various hydrological conditions by integrating the hydrological features and warning thresholds of different water sources, effectively improving the adaptability of the prediction model under different regions and different water source conditions. Using the multi-dimensional interaction model for multi-dimensional prediction of drought disasters and generating specific disaster prediction data, comprehensive data support is provided in the drought early warning system, realizing the full-process automation and high efficiency of the prediction operation. Therefore, the present invention solves the problem of inaccurate prediction caused by inconsistent multi-source data in the traditional early warning system by optimizing hydrological data processing and warning threshold calculation, greatly improving the accuracy and reliability of drought disaster early warning.

[0127] Therefore, in any aspect, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to encompass all changes within the meaning and scope of the equivalent elements of the application documents in the present invention.

[0128] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A method for predicting and warning drought disasters in rural water supply projects, characterized in that, It includes the following steps: Step S1: Obtain a hydrological data set, where the hydrological data set includes water storage-precipitation data, water level change data, surface infiltration data, and groundwater aquifer recharge data; perform data preprocessing on the hydrological data set to generate a core hydrological processing data set; Step S2: Extract data features from the core hydrological processing data set to generate a hydrological feature data set; Quantify the hydrological feature data set to generate a calculation algorithm for the multi-source drought warning threshold; The extraction of data features from the core hydrological processing data set to generate a hydrological feature data set includes: Calculate and evaluate the storage capacity change rate of the water storage-precipitation data to generate reservoir storage capacity change rate recharge data; calculate the contribution rate of reservoir water supply of the reservoir storage capacity change rate recharge data and the water level change to generate reservoir hydrological feature data; Draw the groundwater recharge path of the surface infiltration data to generate the groundwater recharge path; analyze the balance between aquifer recharge and consumption of the surface infiltration data and the groundwater aquifer recharge data through the groundwater recharge path to generate groundwater hydrological feature data; Obtain historical seasonal water level change data of the river channel; analyze the water level fluctuation amplitude based on the water level change data, and combine the historical seasonal water level change data of the river channel to analyze the water level fluctuation characteristics to generate river channel water level fluctuation data; evaluate the flow characteristics of the river channel water level fluctuation data to generate river channel water hydrological feature data; Integrate the reservoir hydrological feature data, groundwater hydrological feature data, and river channel water hydrological feature data into a hydrological feature data set to generate a hydrological feature data set; Step S3: Use the calculation algorithm for the multi-source drought warning threshold and combine it with the hydrological feature data set to construct a multi-dimensional interaction model to generate a multi-source hydrological drought warning and prediction model; among them, Step S3 includes the following steps: Step S31: Analyze the dimensions of the hydrological feature data set to generate multi-dimensional input data, where the multi-dimensional input data includes reservoir dimension data, groundwater dimension data, and river channel water dimension data; Step S32: Use the reservoir dimension data, groundwater dimension data, and river channel water dimension data to construct a multi-dimensional model for the calculation algorithm of the multi-source drought warning threshold to generate a multi-source hydrological drought warning and prediction model; specifically, Step S32 includes the following steps: Step S321: Perform infiltration complementary association on the reservoir dimension data and the groundwater dimension data to generate reservoir-groundwater associated interaction data; Step S322: Perform water level-storage capacity complementary association on the river channel water dimension data and the reservoir dimension data to generate river channel-reservoir associated interaction data; Step S323: Perform time series change complementary association on the groundwater dimension data and the river channel water dimension data to generate groundwater-river channel associated interaction data; Step S324: Construct a hydrological multi-dimensional interaction model for the multi-dimensional input data, reservoir-groundwater associated interaction data, river channel-reservoir associated interaction data, and groundwater-river channel associated interaction data to generate a multi-source hydrological drought warning and prediction model; Step S4: Use the multi-source hydrological drought warning and prediction model to perform multi-dimensional drought prediction to generate drought disaster prediction data, thereby completing the drought disaster prediction and warning operation.

2. The drought disaster prediction and early warning method for rural water supply projects according to claim 1, wherein Step S1 includes the following steps: Step S11: Obtain a hydrological data set; Step S12: Perform data set cleaning processing on the hydrological data set to generate cleaned hydrological data; Step S13: Standardize the data set format of the cleaned hydrological data to generate a core hydrological processing data set.

3. The drought disaster prediction and early warning method for rural water supply projects according to claim 1, wherein Step S2 includes the following steps: Step S21: Extract data features from the core hydrological processing data set to generate a hydrological feature data set; Step S22: Confirm the warning threshold based on the hydrological feature data set, and quantify the warning through the warning threshold, so as to generate a calculation algorithm for the multi-source drought warning threshold.

4. The drought disaster prediction and early warning method for rural water supply projects according to claim 3, characterized in that Step S22 includes the following steps: Step S221: Obtain the historical drought data of the reservoir; conduct a comparative analysis of the reservoir storage capacity change rate replenishment data and the historical drought data of the reservoir to generate the reservoir water level drought critical point data; conduct a comparison of the critical reservoir storage capacity change threshold for the reservoir storage capacity change rate replenishment data to generate the first layer threshold of the reservoir drought warning; calculate the minimum replenishment rate of the water storage in the reservoir for the reservoir hydrological feature data to generate the second layer threshold of the reservoir drought warning; generate a dynamic curve of the reservoir warning threshold by combining the reservoir hydrological feature data, the first layer threshold of the drought warning, and the second layer threshold of the reservoir drought warning; mark the warning critical value for the dynamic curve of the reservoir warning threshold to generate the reservoir drought disaster data; Step S222: Obtain the historical surface infiltration data and the historical groundwater drought data; draw the historical recharge path for the historical surface infiltration data to generate the historical groundwater recharge path; conduct infiltration fracture detection on the groundwater recharge path and the historical groundwater recharge path, and generate the first layer threshold of the groundwater based on the degree of fracture; conduct time series analysis of the groundwater level for the surface infiltration data and the underground aquifer recharge data, and conduct minimum threshold analysis based on the historical groundwater drought data to generate the second layer threshold of the groundwater; conduct fluctuation safety range analysis based on the groundwater hydrological feature data, the first layer threshold of the groundwater, and the second layer threshold of the groundwater, and conduct drought disaster point analysis based on the groundwater recharge path to generate the groundwater drought disaster data; Step S223: Conduct minimum river water level threshold analysis on the historical seasonal river water level change data, and confirm the minimum threshold with the river water hydrological feature data to generate the first layer threshold of the river water; conduct dynamic critical value analysis of the flow rate based on the river water hydrological feature data to generate the second layer threshold of the river water; bind the trigger conditions for the water level change data, the first layer threshold of the river water, and the second layer threshold of the river water, and mark the trigger time point to generate the river water drought disaster data; Step S224: Uniformly quantify the thresholds of the reservoir drought disaster data, the groundwater drought disaster data, the river water drought disaster data, and the hydrological feature data set, and generate an algorithm to generate a calculation algorithm for the multi-source drought warning threshold.

5. The drought disaster prediction and early warning method for rural water supply projects according to claim 1, characterized in that Step S4 includes the following steps: Step S41: Obtain real-time hydrological data, where the real-time hydrological data includes real-time reservoir data, real-time groundwater data, and real-time river water data; Step S42: Use the multi-source hydrological drought early warning and prediction model to combine with real-time hydrological data for multi-dimensional drought prediction, generate drought disaster prediction data, and thus complete the drought disaster prediction operation, where the drought disaster prediction data includes reservoir drought prediction data, groundwater supply drought prediction data, and river channel water supply drought prediction data.

6. The drought disaster prediction and early warning method for rural water supply projects according to claim 5, characterized in that, Step S42 includes the following steps: Step S421: Use the multi-source hydrological drought early warning and prediction model to predict the drought time series of reservoir real-time data and generate reservoir drought prediction data; Step S422: Use the multi-source hydrological drought early warning and prediction model to predict the water level recharge balance drought of groundwater real-time data and generate groundwater supply drought prediction data; Step S423: Use the multi-source hydrological drought early warning and prediction model to predict the water flow supply of river channel real-time data and generate river channel water supply drought prediction data; Step S424: Send the reservoir drought prediction data, groundwater supply drought prediction data, and river channel water supply drought prediction data through the cloud platform and remind of the drought disaster, thus completing the drought disaster prediction and early warning operation.

7. A drought disaster prediction and early warning system for rural water supply projects, characterized in that, For implementing the rural water supply project drought disaster prediction and early warning method as described in claim 1, the rural water supply project drought disaster prediction and early warning system includes: Hydrological data processing module: Used to obtain the hydrological data set; perform data preprocessing on the hydrological data set to generate a hydrological core processing data set; Hydrological feature extraction and early warning algorithm generation module: Used to extract data features from the hydrological core processing data set to generate a hydrological feature data set; quantify the hydrological feature data set to generate a multi-source drought early warning threshold calculation algorithm; Multi-dimensional interaction model construction module: Used to use the multi-source drought early warning threshold calculation algorithm and combine with the hydrological feature data set to construct a multi-dimensional interaction model and generate a multi-source hydrological drought early warning and prediction model; Drought disaster prediction and early warning module: Used to use the multi-source hydrological drought early warning and prediction model for multi-dimensional drought prediction, generate drought disaster prediction data, and thus complete the drought disaster prediction and early warning operation.

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