Rural water supply project drought disaster prediction and early warning method and system

Abstract

The invention relates to the technical field of disaster prediction and early warning, in particular to a rural water supply project drought disaster prediction and early warning method and system. The method comprises the following steps: acquiring a hydrological data set; performing data preprocessing on the hydrological data set to generate a hydrological core processing data set; performing data feature extraction on the hydrological core processing data set to generate a hydrological feature data set; quantifying the hydrological characteristic data set to generate a multi-water-source drought early warning threshold calculation algorithm; constructing a multi-dimensional interaction model by using a multi-water-source drought early warning threshold calculation algorithm in combination with the hydrological characteristic data set, and generating a multi-source hydrological drought early warning and prediction model; therefore, by optimizing hydrological data processing and early warning threshold calculation, the problem of inaccurate prediction caused by inconsistent multi-source data in a traditional early warning system is solved, and the accuracy and reliability of early warning of drought disasters are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of disaster prediction and early warning, and in particular 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 makes the system less responsive to drought events. The interaction between reservoir, groundwater and river water resources has not been fully considered, and reservoir water, groundwater and river water are all sources of water for rural water supply projects. Single consideration will lead to a lack of comprehensiveness in drought prediction. Traditional drought early warning models mostly use 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 thus 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 fusion of multi-source data, resulting in inaccurate comprehensive assessment of drought conditions. 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 existing early warning system lags behind in information transmission and response mechanisms, and fails to effectively integrate various types of data to form a unified decision support platform. This information island effect makes relevant departments lack the necessary coordination and interaction when dealing with drought disasters, reducing the efficiency and accuracy of decision-making. Therefore, there is an urgent need for a drought disaster prediction and early warning method and system 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 warning drought disasters in rural water supply projects to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a method for predicting and warning drought disasters in rural water supply projects is provided, the method comprising the following steps:

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

[0006] Step S2: extracting data features from the hydrological core processing data set to generate a hydrological feature data set; quantifying the hydrological feature data set to generate a multi-water source drought warning threshold calculation algorithm;

[0007] Step S3: using a multi-water source drought warning threshold calculation algorithm and combining it with a hydrological characteristic data set to construct a multi-dimensional interactive model and generate a multi-source hydrological drought warning and prediction model;

[0008] Step S4: Use the multi-source hydrological drought warning and prediction model to perform multi-dimensional drought prediction and generate drought disaster prediction data, thereby completing the drought disaster prediction and warning operation.

[0009] The beneficial effect of the present invention is that a hydrological core processing data set is generated by acquiring 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. The key to this step is to ensure that hydrological data of different water source types can be effectively integrated on the same platform, avoiding the processing complexity caused by differences in data sources. By extracting data features from the core processing data set, the accurate extraction of various water source features such as reservoirs, groundwater, and river water is ensured, and a drought warning threshold calculation algorithm is further generated through quantitative processing. The algorithm innovatively introduces multi-layer threshold settings 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 guaranteed, providing a solid data foundation for subsequent model construction. By interacting the drought warning threshold calculation algorithm with the hydrological feature data set, a multidimensional drought warning and prediction model is constructed, which further enhances the accuracy and robustness of the system in predicting drought disasters. The multidimensional interactive model achieves accurate predictions under various hydrological conditions by integrating the hydrological characteristics and warning thresholds of different water sources, and effectively improves the adaptability of the prediction model under different regions and different water source conditions. The multidimensional interactive model is used to perform multidimensional predictions of drought disasters and generate specific disaster prediction data, thereby providing comprehensive data support in the drought early warning system and achieving full process automation and efficiency of the prediction operation. Therefore, the present invention solves the problem of inaccurate predictions caused by inconsistent multi-source data in traditional early warning systems by optimizing hydrological data processing and early warning threshold calculation, and greatly improves the accuracy and reliability of drought disaster early warnings.

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

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

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

[0013] Step S13: annotate the hydrological cleansing data into a data set format to generate a hydrological core processing data set.

[0014] The present invention realizes the comprehensive collection of different hydrological information by acquiring hydrological data sets including water storage-precipitation data, water level change data, surface infiltration data and underground aquifer recharge data. These data cover the hydrological characteristics of various water source types such as reservoirs, rivers, groundwater, etc., 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, so that the core information under various hydrological conditions can be integrated into the data set without omission. The collected hydrological data are subjected to data set cleaning processing, and the processing process significantly improves the quality and consistency of the data. Through cleaning, redundant, incomplete or noisy data are eliminated, ensuring the accuracy, validity and stability of the data during subsequent processing. The cleaning of hydrological data is particularly important for ensuring the accuracy of the analysis results, because the original hydrological data often contains many irregular factors, such as sensor errors, data missing, data anomalies, etc., and cleaning can significantly reduce the impact of such adverse factors on the analysis. The cleaned hydrological data are further annotated in the data set format, so that the hydrological core processing data set can be generated in a standardized manner. Through this formatted annotation process, the data of different water source types are structured and integrated, ensuring that the data can be consistently extracted and threshold values ​​calculated in the subsequent steps. At the same time, this step of annotation processing also greatly simplifies the complexity of subsequent data processing and provides a standardized data foundation for further data mining and model building.

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

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

[0017] Step S22: confirm the warning threshold according to the hydrological characteristic data set, and quantify the warning through the warning threshold, so as to generate a multi-water source drought warning threshold calculation algorithm.

[0018] The present invention converts the hydrological core processing data set of different water sources such as reservoirs, groundwater, and rivers into an operable hydrological characteristic data set through data feature extraction. 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 characteristic indicator system based on the characteristics of different water sources. In this way, the hydrological characteristic data set effectively captures the hydrological change laws of various water sources before the occurrence of drought, so that the subsequent warning threshold calculation has the reliability of data support. The confirmation of the warning threshold based on the hydrological characteristic data set further improves the scientificity and accuracy of drought warning. First, this step clarifies the critical threshold of drought in different water sources by comprehensively analyzing multi-source hydrological characteristic 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 quantitative processing of the warning threshold transform the drought warning from qualitative description to quantitative analysis, especially through the dynamic analysis of reservoir capacity changes, groundwater infiltration paths, and water level fluctuation characteristics, a multi-source drought warning threshold calculation algorithm is generated. The algorithm combines the changing trends of various hydrological characteristic data and dynamically adjusts the warning threshold, thus providing quantitative support for drought disaster prediction under different hydrological conditions. This data-based warning mechanism can quickly respond to changes in the hydrological environment and significantly improve 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 the water storage-precipitation data to generate reservoir storage capacity change rate replenishment data; Calculate the reservoir water replenishment contribution rate of the reservoir storage capacity change rate replenishment data and water level changes to generate reservoir hydrological characteristic data;

[0021] Step S212: Draw a groundwater recharge path for the surface infiltration data to generate a groundwater recharge path; perform aquifer recharge and consumption balance analysis on the surface infiltration data and the underground aquifer recharge data through the groundwater recharge path to generate groundwater hydrological characteristic data;

[0022] Step S213: Obtain historical river seasonal water level change data; perform water level fluctuation amplitude analysis based on the water level change data, and analyze water level fluctuation characteristics in combination with historical river seasonal water level change data to generate river water level fluctuation data; perform flow characteristic evaluation on the river water level fluctuation data to generate river water hydrological characteristic data;

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

[0024] The present invention accurately evaluates the water storage dynamics of the reservoir by calculating the storage capacity change rate of the water storage-precipitation data, and calculates the recharge contribution rate in combination with the water level change data, so that the reservoir hydrological characteristic data can reflect the recharge contribution of the reservoir water volume in a refined manner. This process not only reflects the water volume change trend of the reservoir, but also captures the response mode of the water storage system before and after the drought, ensuring the scientific basis for subsequent drought warnings. Through the collaborative analysis of surface infiltration data and underground aquifer recharge data, the groundwater recharge path is generated, and then the balance between aquifer recharge and consumption is deeply analyzed. This process not only reveals the dynamic changes of groundwater resources, but also makes the groundwater recharge and loss paths clearer through the drawing of the infiltration path. 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 a drought situation, thereby improving the accuracy of the early warning mechanism. The introduction of historical river seasonal water level change data enables the accurate quantification of river water level fluctuation characteristics. By combining the analysis of the water level fluctuation amplitude with historical data, detailed river water level fluctuation data is formed, which provides a key reference for the subsequent flow characteristic evaluation. The evaluation of flow characteristics not only captures the dynamic changes of river water resources in different seasons, but also reveals the relationship between flow changes and drought occurrence through data analysis, so that river hydrological characteristic data can provide a reliable quantitative basis for drought warning. By integrating the hydrological characteristic data of reservoirs, groundwater and rivers, 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 warning threshold algorithm. Through the construction of this data set, the present invention can monitor the hydrological change trends of different water sources in real time and provide accurate drought warning references.

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

[0026] Step S221: Acquire historical drought data of the reservoir; compare and analyze the reservoir capacity change rate replenishment data with the historical drought data of the reservoir to generate reservoir water level drought critical point data; compare the reservoir capacity change rate replenishment data with the critical capacity change threshold to generate the first-level threshold of reservoir drought warning; calculate the minimum recharge rate of water storage in the reservoir based on the reservoir hydrological characteristic data to generate the second-level threshold of reservoir drought warning; generate a dynamic curve of the reservoir hydrological characteristic data, the first-level threshold of drought warning and the second-level threshold of reservoir drought warning to generate a dynamic curve of reservoir warning threshold; mark the dynamic curve of reservoir warning threshold with the warning critical value to generate reservoir drought disaster data;

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

[0028] Step S223: Perform a minimum river water level threshold analysis on the historical river seasonal water level change data, and use the river water hydrological characteristic data to confirm the minimum threshold to generate the first-layer river water threshold; perform a flow dynamic critical value analysis based on the river water hydrological characteristic data to generate the second-layer river water threshold; bind the water level change data, the first-layer river water threshold and the second-layer river water threshold to trigger conditions, and mark the trigger time point to generate river water drought disaster data;

[0029] Step S224: The reservoir drought disaster data, groundwater drought disaster data, river water drought disaster data and hydrological characteristic data set are uniformly quantified by threshold, and an algorithm is generated to generate a multi-water source drought warning threshold calculation algorithm.

[0030] The present invention generates reservoir water level drought critical point data by comparing and analyzing the historical drought data of the reservoir and the recharge data of the reservoir storage capacity change rate, and forms the first layer threshold of the reservoir drought warning by calculating the critical storage capacity change threshold. This step generates the second layer threshold by calculating the minimum recharge rate of the reservoir hydrological characteristic data, so that the reservoir warning threshold has a more comprehensive dynamic performance. By generating a dynamic curve of the reservoir warning threshold and marking the key warning critical value 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 subsequent hydrological system warning through the marking of the dynamic curve. The historical recharge path of groundwater and the existing groundwater recharge path are detected for seepage fractures, and the first layer threshold is generated based on the fracture degree. The second layer threshold is further generated by the time series analysis of the surface infiltration data and the underground aquifer recharge data, combined with the historical drought data of groundwater. This step obtains the groundwater drought disaster data by analyzing the fluctuation safety range of the groundwater characteristic data and the two-layer threshold, and the drought disaster point analysis based on the groundwater recharge path. The generation of this data provides a multi-dimensional analysis basis for groundwater resources in drought warning, and improves the timeliness and accuracy of drought warning by introducing time series data. The historical river seasonal water level change data is used to analyze the minimum river water level threshold, and the dynamic critical value of flow is confirmed in combination with the river water hydrological characteristic data. By binding the trigger conditions of the first and second layer thresholds, river water drought disaster data is generated. This process makes the critical point triggering of river water resources in drought warning more accurate through dynamic critical value analysis, providing a scientific basis for river water characteristic warning in multi-source hydrological system. By quantifying the thresholds of drought disaster data of reservoirs, groundwater and river water, and combining them with hydrological characteristic data sets, a multi-source drought warning threshold calculation algorithm is generated. The algorithm integrates multi-level warning thresholds and dynamic data to ensure the comprehensiveness and efficiency of the multi-source drought warning system.

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

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

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

[0034] The present invention effectively processes multi-source hydrological data such as reservoirs, groundwater and river water in multiple dimensions by performing dimensional analysis on the hydrological characteristic data set, and generates multi-dimensional input data. These input data correspond to reservoir dimensional data, groundwater dimensional data and river water dimensional data, respectively, and can fully reflect the unique characteristics and interrelationships of each water source system. Dimensional analysis not only effectively separates the hydrological information of different water sources, but also lays the foundation for the subsequent multi-dimensional model construction through the comprehensive processing of each dimensional data. This multi-dimensional data decomposition method allows the data characteristics of each water source to be fully retained, while ensuring the correlation and independence between dimensions in a complex environment. By utilizing these multi-dimensional input data and applying them to the multi-water source drought warning threshold calculation algorithm, 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 the model with key information such as storage capacity changes and historical drought data. The groundwater dimension data ensures the accuracy of groundwater resource warning through the infiltration path and the recharge dynamics of the underground aquifer, while 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 data from various dimensions, the model can accurately reflect the dynamic changes of multi-source hydrological systems and realize multi-dimensional and all-round monitoring of drought warning. The multi-dimensional characteristics of the model enable it to have a 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 interrelationship between dimensions. Compared with the traditional single water source warning model, the construction of the multi-dimensional model significantly improves the model's adaptability to different hydrological characteristics, making drought warning more comprehensive and flexible.

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

[0036] Step S321: Performing a permeation complementary association between the reservoir dimension data and the groundwater dimension data to generate reservoir-groundwater association interaction data;

[0037] Step S322: Complementarily associate the river water dimension data and the reservoir dimension data with each other in terms of water level and storage capacity to generate river-reservoir correlation interaction data;

[0038] Step S323: performing temporal change complementary association between groundwater dimension data and river water dimension data to generate groundwater-river correlation interaction data;

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

[0040] The present invention generates reservoir-groundwater correlation interaction data by performing infiltration and complementary association between reservoir dimension data and groundwater dimension data. This process is based on the dynamic relationship between reservoir storage capacity and groundwater recharge, and captures the interactive characteristics of the two in the process of infiltration and recharge, thereby realizing the quantification of the complex balance between reservoir water storage and groundwater recharge. This provides a key foundation for the drought early warning system, because changes in storage capacity and groundwater level are often early signals of drought early warning. The river water dimension data and reservoir dimension data are complementaryly associated with water level and storage capacity to generate river-reservoir correlation interaction data. In this process, combined with the historical data of river 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 water resources and reservoir water storage. This correlation interaction analysis improves the ability to analyze the interdependence between river water and reservoir water resources under extreme climatic conditions, thereby improving the response speed to sudden drought events in the hydrological system. By making a complementary association between the groundwater dimension data and the river water dimension data in terms of temporal changes, the groundwater-river correlation interaction data was generated. This step is based on the temporal changes of the groundwater level and the river water level, combined with the seasonal fluctuations in different historical periods, to construct a temporal dynamic model, reflecting the interaction between groundwater and river water. This temporal association enables the model to capture the dynamic process of mutual influence between groundwater and river water, especially in the early stage of drought, when the decline of river water level is often accompanied by changes in groundwater level. Therefore, this step plays a key role in drought prediction. By constructing a hydrological multidimensional interaction model for multidimensional input data and various types of generated interactive data, a multi-source hydrological drought warning and prediction model was generated. This model not only integrates the characteristics of multiple dimensions such as reservoirs, groundwater and river water, but also further enhances the model's ability to reflect the complex relationship between various water source systems through the introduction of interactive data. This multidimensional interactive 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 comprises the following steps:

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

[0043] Step S42: Utilize the multi-source hydrological drought warning and prediction model in combination with real-time hydrological data to perform multi-dimensional drought prediction and generate drought disaster prediction data, thereby completing the drought disaster prediction task, wherein the drought disaster prediction data includes reservoir drought prediction data, groundwater level water supply drought prediction data and river 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 rivers, such as changes in reservoir capacity, groundwater level fluctuations, and river water supply capacity, wherein reservoir real-time data includes real-time data mainly for water level data, reservoir capacity data, groundwater real-time data includes real-time data mainly for groundwater level data, spring flow data, and river water real-time data includes real-time data mainly for river water level data and river flow data. The acquisition of these real-time data not only improves the dynamic update capability of the model, but also can capture abnormal changes in the hydrological system, ensuring the timeliness and accuracy of drought warning. Further using the multi-source hydrological drought warning and prediction model, these real-time data are incorporated into the model operation framework to perform multi-dimensional drought prediction. The model integrates the dynamic relationship between reservoirs, groundwater and river water systems, and can timely update the drought conditions of different water source systems based on real-time data. Specifically, reservoir drought prediction data is based on the real-time storage capacity and water supply capacity of the reservoir. Through the dynamic changes of reservoir storage capacity, combined with the designed water supply scale, actual water supply capacity, and water supply plan of rural water supply projects, drought risk is assessed; groundwater supply drought prediction data relies on real-time groundwater level fluctuation information, combined with historical data of groundwater recharge and consumption, to predict the possibility of groundwater depletion in the future period; river water supply drought prediction data uses river water level and flow changes, combined with the designed water supply scale, actual water supply capacity, and water supply plan of rural water supply projects to predict the degree of drought in the river water supply system. The beneficial effects of this real-time prediction mechanism are reflected in many aspects. First, through the combination of real-time data and models, early drought signals can be effectively captured, which improves the timeliness and response speed of drought warning. Secondly, multi-dimensional prediction not only covers multiple water source systems such as reservoirs, groundwater and rivers, but also dynamically reflects the mutual influence between systems, such as the interaction between groundwater and river water supply, or the mutual replenishment relationship between reservoir storage capacity and surrounding groundwater levels. Therefore, this model can comprehensively consider the water supply capacity of multiple water sources and their changing trends, provide more comprehensive and accurate drought prediction data, and significantly improve the accuracy and reliability of disaster predictions.

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

[0046] Step S421: using a multi-source hydrological drought warning and prediction model to perform time series drought prediction on reservoir real-time data to generate reservoir drought prediction data;

[0047] Step S422: using a multi-source hydrological drought warning and prediction model to perform water level recharge balance drought prediction on groundwater real-time data to generate groundwater supply drought prediction data;

[0048] Step S423: using a multi-source hydrological drought warning and prediction model to perform water flow and water supply prediction on the real-time river water data to generate river water supply drought prediction data;

[0049] Step S424: The reservoir drought prediction data, the groundwater supply drought prediction data and the river water supply drought prediction data are sent through the cloud platform, and drought disasters are reminded, thereby 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 warning and prediction model, predicts the time window of drought occurrence through the 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's water storage capacity, identify the risk of reservoir drought, and make water resource scheduling and management more forward-looking. The focus is on the balance of 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 of 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. The model is further used to predict the water supply of the real-time flow data of river water, evaluate the impact of river water level changes on water supply capacity, and generate river water supply drought prediction data. This analysis is based on a comprehensive assessment of flow characteristics, river water supply paths, and historical water level changes. It can promptly detect the decline in river water supply capacity and issue early warnings for weak links in the water supply chain, thereby preventing the water supply system from being seriously affected during droughts. All types of drought prediction data, including drought prediction results for reservoirs, groundwater, and rivers, are transmitted through the cloud platform and relevant managers are reminded in real time. This operation not only ensures the instant transmission of information, but also enables efficient data processing and sharing through the cloud computing platform, making early warnings 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, which is used to execute 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 hydrological data sets; perform data preprocessing on the hydrological data sets to generate hydrological core processing data sets;

[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-water source drought early warning threshold calculation algorithm;

[0054] Multi-dimensional interactive model building module: Utilizes the multi-water source drought warning threshold calculation algorithm and combines the hydrological characteristic data set to build a multi-dimensional interactive model to generate a multi-source hydrological drought warning and prediction model;

[0055] Drought disaster prediction and warning module: Use multi-source hydrological drought warning and prediction models to conduct multi-dimensional drought prediction, generate drought disaster prediction data, and thus complete drought disaster prediction and warning operations.

[0056] The beneficial effect of the present invention is that a hydrological core processing data set is generated by acquiring 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. The key to this step is to ensure that hydrological data of different water source types can be effectively integrated on the same platform, avoiding the processing complexity caused by differences in data sources. By extracting data features from the core processing data set, the accurate extraction of various water source features such as reservoirs, groundwater, and river water is ensured, and a drought warning threshold calculation algorithm is further generated through quantitative processing. The algorithm innovatively introduces multi-layer threshold settings 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 guaranteed, providing a solid data foundation for subsequent model construction. By interacting the drought warning threshold calculation algorithm with the hydrological feature data set, a multidimensional drought warning and prediction model is constructed, which further enhances the accuracy and robustness of the system in predicting drought disasters. The multidimensional interactive model achieves accurate predictions under various hydrological conditions by integrating the hydrological characteristics and warning thresholds of different water sources, and effectively improves the adaptability of the prediction model under different regions and different water source conditions. The multidimensional interactive model is used to perform multidimensional predictions of drought disasters and generate specific disaster prediction data, thereby providing comprehensive data support in the drought early warning system and achieving full process automation and efficiency of the prediction operation. Therefore, the present invention solves the problem of inaccurate predictions caused by inconsistent multi-source data in traditional early warning systems by optimizing hydrological data processing and early warning threshold calculation, and greatly improves the accuracy and reliability of drought disaster early warnings. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A schematic diagram of the steps of a method for predicting and warning drought disasters in rural water supply projects;

[0058] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0059] Figure 3 for Figure 2 Detailed implementation steps of step S21;

[0060] Figure 4 for Figure 2 Detailed implementation steps of step S22;

[0061] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0062] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0063] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying 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 herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

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

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

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

[0068] Step S3: using a multi-water source drought warning threshold calculation algorithm and combining it with a hydrological characteristic data set to construct a multi-dimensional interactive model and generate a multi-source hydrological drought warning and prediction model;

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

[0070] The beneficial effect of the present invention is that a hydrological core processing data set is generated by acquiring 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. The key to this step is to ensure that hydrological data of different water source types can be effectively integrated on the same platform, avoiding the processing complexity caused by differences in data sources. By extracting data features from the core processing data set, the accurate extraction of various water source features such as reservoirs, groundwater, and river water is ensured, and a drought warning threshold calculation algorithm is further generated through quantitative processing. The algorithm innovatively introduces multi-layer threshold settings 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 guaranteed, providing a solid data foundation for subsequent model construction. By interacting the drought warning threshold calculation algorithm with the hydrological feature data set, a multidimensional drought warning and prediction model is constructed, which further enhances the accuracy and robustness of the system in predicting drought disasters. The multidimensional interactive model achieves accurate predictions under various hydrological conditions by integrating the hydrological characteristics and warning thresholds of different water sources, and effectively improves the adaptability of the prediction model under different regions and different water source conditions. The multidimensional interactive model is used to perform multidimensional predictions of drought disasters and generate specific disaster prediction data, thereby providing comprehensive data support in the drought early warning system and achieving full process automation and efficiency of the prediction operation. Therefore, the present invention solves the problem of inaccurate predictions caused by inconsistent multi-source data in traditional early warning systems by optimizing hydrological data processing and early warning threshold calculation, and greatly improves the accuracy and reliability of drought disaster early warnings.

[0071] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of a method for predicting and warning drought disasters in rural water supply projects of the present invention. In this example, the method for predicting and warning drought disasters in rural water supply projects includes the following steps:

[0072] Step S1: obtaining a hydrological data set; performing data preprocessing on the hydrological data set to generate a hydrological core processing data set;

[0073] In the embodiment 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 underground aquifer recharge data. These data are acquired in real time through sensor networks, remote sensing technology, 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 will contain redundant, missing or noisy information, which requires effective data preprocessing in subsequent steps. Data preprocessing refers to the cleaning, filtering, format unification, and invalid data removal of the acquired hydrological data set to ensure that the generated hydrological core processing data set has higher accuracy and consistency. In this process, the data cleaning algorithm is first applied to remove noise data and outliers, such as identifying and removing abnormal fluctuations or invalid water level records through a time series detection algorithm. In addition, for the processing of missing data, interpolation methods (such as linear interpolation or multivariate interpolation) can be used to complete it to ensure the integrity of the data set. Subsequently, the unification of data formats is a key step. In order to make data from different sources operational, multiple data types (such as time series, spatial distribution data) must be converted into the same format, 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 geographic location, and clearly mark it in the internal structure of the data set. The technical means of generating hydrological core processing data sets also include standardization of data. Specifically, in order to ensure the comparability between various hydrological indicators, data of different dimensions need to be normalized or standardized. Therefore, the standardization step adjusts different indicators to the same scale to facilitate the smooth progress of subsequent analysis and model building.

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

[0075] In the embodiment of the present invention, the feature extraction process includes statistical feature analysis and time series feature analysis. For the hydrological core processing data set, the statistical feature analysis includes calculating the mean, variance, extreme value and other statistics of key indicators such as water level, precipitation, and flow, which can effectively reflect the overall trend and change law of hydrological data. At the same time, the time series feature analysis can decompose the data seasonally, trending and periodically through the time series decomposition technology to extract the characteristics of periodic changes and emergencies. In order to enhance the prediction ability of the model, the feature extraction process will also use time series modeling methods such as autoregressive model (AR) and moving average model (MA) to model the hydrological data, so as to identify the key driving factors affecting the hydrological characteristics. These methods can help identify important trends in the hydrological feature data set and convert them into feature parameters that can be used for modeling. After the extraction is completed, the generated hydrological feature data set 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 warning. The quantification of the hydrological feature data set is a key step in generating a multi-water source drought warning threshold calculation algorithm. The quantification process involves converting the extracted features into quantitative indicators. This process can be done through standardization and normalization to ensure that each feature is compared under the same measurement. In the quantification process, hydrological characteristics such as water level, precipitation and evaporation can be adjusted to percentage changes relative to historical data. This relative change measurement helps to more intuitively understand the impact of hydrological characteristics on drought warning. By training the model to identify the relationship between features and drought occurrence, a set of dynamic drought warning thresholds can be obtained. These thresholds can reflect the weight and influence of different hydrological characteristics in drought warning. The final multi-water source drought warning threshold calculation algorithm is generated.

[0076] Step S3: using a multi-water source drought warning threshold calculation algorithm and combining it with a hydrological characteristic data set to construct a multi-dimensional interactive model and generate a multi-source hydrological drought warning and prediction model;

[0077] In the embodiment of the present invention, the multi-water source drought warning threshold calculation algorithm used is designed based on the hydrological characteristic data extracted in the previous step. This algorithm can integrate multiple hydrological data sources, identify the correlation between each water source, quantify the drought risk and generate a warning threshold suitable for multiple water sources. In the model construction process, the hydrological characteristic data set needs to be preprocessed first to ensure the integrity and consistency of the data. This process includes data cleaning, missing value processing and outlier detection to eliminate the noise that misleads the model. Next, the hydrological characteristic data set is divided into a training set and a test set to facilitate the training and verification of subsequent models. The construction of the multidimensional interactive model is achieved by introducing machine learning or deep learning algorithms. Common methods include support vector machines (SVM), random forests (RF) and neural networks, etc. These algorithms can process high-dimensional feature data and identify complex nonlinear relationships between features. When training the model, the variables in the hydrological characteristic data set are used as input, combined with the calculated warning threshold, and the model can learn the degree of influence of different water sources on drought risk. This multi-dimensional interactive 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. Evaluation indicators can include mean square error (MSE), determination coefficient (R 2 ), etc. These indicators can reflect the degree of deviation between the model prediction value and the actual value, thereby ensuring that the constructed multi-source hydrological drought warning and prediction model is practical and reliable. The multi-source hydrological drought warning and prediction model generated by the above steps can not only monitor the changes in hydrological characteristics in real time, but also dynamically update based on real-time data to achieve early warning of drought risks. The model provides effective data support for decision makers, enabling them to take timely response measures to reduce the impact of drought on water resources management and the ecological environment.

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

[0079] In the embodiment of the present invention, the specific technical means of using the multi-source hydrological drought early warning and prediction model to perform multi-dimensional drought prediction mainly focuses on the collection, processing and analysis process of real-time hydrological data, aiming to generate accurate drought disaster prediction data to achieve early warning of drought disasters. This process first involves the acquisition of real-time hydrological data, specifically including real-time monitoring data of 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 acquired real-time hydrological data is then pre-processed 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 to enter the multi-source hydrological drought early warning and prediction model. The model uses the multi-dimensional interactive characteristics constructed in the previous step to identify the correlation between hydrological variables and their impact on the occurrence of drought through dynamic analysis of each water source data. The model compares and analyzes real-time hydrological data with historical data to identify potential drought risks and predict trends. By analyzing the time series changes of reservoir water levels, groundwater levels and river flows, the model can effectively evaluate the similarity of the current hydrological state to historical drought events and generate drought risk levels based on this. In order to further improve the accuracy of the prediction, the model uses time series analysis, regression analysis or other machine learning algorithms to process multidimensional data to output drought disaster prediction data. The generated drought disaster prediction data includes the drought risk level of each water source, the warning threshold and the probability distribution of its occurrence.

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

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

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

[0083] Step S13: annotate the hydrological cleansing data into a data set format to generate a hydrological core processing data set.

[0084] In an embodiment of the present invention, a multi-dimensional data set is formed by integrating multiple hydrological data sources, including water storage-precipitation data, water level change data, surface infiltration data, and underground aquifer recharge data. This process requires the use of standardized data acquisition methods to ensure the accuracy and consistency of the data, and uses API or data crawler technology to extract data from the real-time rain and water condition monitoring system and the historical rain and water condition database. The hydrological data set is subjected to data cleaning, which involves removing redundant data, filling missing values, and removing 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 technology is used to uniformly process data from different sources so that they have the same dimension and range for subsequent analysis. The cleaned hydrological data is formatted and annotated, and structured attributes are given to the core processing data set by defining data labels and metadata. This stage usually uses feature engineering technology in machine learning to convert raw data into a format suitable for model training through feature selection and transformation. In addition, data frameworks (such as Pandas) are used to reshape and optimize the data framework to ensure that the generated hydrological core processing dataset 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 As shown, in this example, step S2 includes:

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

[0087] Step S22: confirm the warning threshold according to the hydrological characteristic data set, and quantify the warning through the warning threshold, so as to generate a multi-water source drought warning threshold calculation algorithm.

[0088] In the embodiment of the present invention, the specific technical means involve in-depth analysis and processing of the hydrological core processing data set to extract key features and establish an effective drought warning threshold calculation algorithm. First, in step S21, data feature extraction is performed on the hydrological core processing data set, which is to identify the main features of the hydrological variables through a variety of statistical analysis and machine learning techniques. This process includes descriptive statistical analysis of hydrological data, such as calculation of basic statistics such as mean, variance, extreme value, etc., 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., which are crucial for drought warning. The warning threshold is confirmed and quantified based on the extracted hydrological feature data set, and the specific technical means include threshold setting and verification process. First, the relationship between historical drought events and current hydrological characteristics is analyzed by statistical methods to determine the drought warning threshold. The percentile method or standard deviation method can be used to set the thresholds of variables such as water level, flow and precipitation according to the distribution of historical data. The water supply capacity of water sources at different times (such as statutory holidays, working days, weekends, etc.) can also be set according to the distribution of historical conditions to ensure that these thresholds can accurately reflect the changes in drought risks. In addition, machine learning models such as support vector machines (SVM) or random forests (RF) are used to predict the occurrence of droughts and further optimize the threshold setting process. By repeatedly verifying the model prediction results, a more accurate multi-water source drought warning threshold calculation algorithm can be formed.

[0089] As an example of the present invention, refer to Figure 3 As 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 reservoir storage capacity change rate replenishment data; Calculate the reservoir water replenishment contribution rate of the reservoir storage capacity change rate replenishment data and water level changes to generate reservoir hydrological characteristic data;

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

[0092] Step S213: Obtain historical river seasonal water level change data; perform water level fluctuation amplitude analysis based on the water level change data, and analyze water level fluctuation characteristics in combination with historical river seasonal water level change data to generate river water level fluctuation data; perform flow characteristic evaluation on the river water level fluctuation data to generate river water hydrological characteristic data;

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

[0094] In the embodiment 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 and evaluating the storage capacity change rate of water storage-precipitation data, the change rate of storage capacity is calculated using a time series analysis method. This process involves correlation analysis of historical storage capacity data and precipitation data to determine the direct impact of precipitation on storage capacity changes and generate reservoir storage capacity change rate replenishment data. Subsequently, the water supply contribution rate is calculated in combination with the reservoir water level change, and statistical methods such as linear regression analysis are applied to identify the relationship between water level changes and storage capacity replenishment, and finally generate reservoir hydrological characteristic data, which provides a quantitative basis for understanding the water volume dynamics of the reservoir. The groundwater recharge path drawing of 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 integrated analysis of the spatial distribution of permeability, soil type and groundwater level to generate groundwater recharge paths. At the same time, the surface infiltration data and the groundwater aquifer recharge data are combined to conduct aquifer recharge and consumption balance analysis, 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 seasonal water level change data involves time series statistical techniques and fluctuation analysis methods. By calculating the amplitude of water level fluctuations and extracting features in combination with historical data, seasonal water level change patterns can be identified and river water level fluctuation data can be generated. In addition, when evaluating the flow characteristics of river water level fluctuation data, the flow calculation formula and empirical model are applied to reveal the relationship between water level changes and flow, and further generate river water hydrological characteristic data. The hydrological characteristic data of reservoirs, groundwater and rivers are integrated, and multivariate statistical analysis techniques such as principal component analysis (PCA) or cluster analysis are used to integrate 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, step S22 includes:

[0096] Step S221: Acquire historical drought data of the reservoir; compare and analyze the reservoir capacity change rate replenishment data with the historical drought data of the reservoir to generate reservoir water level drought critical point data; compare the reservoir capacity change rate replenishment data with the critical capacity change threshold to generate the first-level threshold of reservoir drought warning; calculate the minimum recharge rate of water storage in the reservoir based on the reservoir hydrological characteristic data to generate the second-level threshold of reservoir drought warning; generate a dynamic curve of the reservoir hydrological characteristic data, the first-level threshold of drought warning and the second-level threshold of reservoir drought warning to generate a dynamic curve of reservoir warning threshold; mark the dynamic curve of reservoir warning threshold with the warning critical value to generate reservoir drought disaster data;

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

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

[0099] Step S224: The reservoir drought disaster data, groundwater drought disaster data, river water drought disaster data and hydrological characteristic data set are uniformly quantified by threshold, and an algorithm is generated to generate a multi-water source drought warning threshold calculation algorithm.

[0100] In the embodiment of the present invention, by obtaining the historical drought data of the reservoir, combined with the recharge data of the reservoir storage capacity change rate, comparative analysis is performed to identify the critical point of the reservoir drought. This analysis process uses the regression analysis or correlation analysis method in statistics to establish a relationship model between the reservoir water level change and the occurrence of drought, thereby generating the critical point data of the reservoir water level drought. In addition, by comparing the critical storage capacity change threshold, the threshold judgment technology is used to further generate the first layer threshold of the reservoir drought warning. The setting of this layer of threshold is based on the comprehensive analysis of the historical storage capacity data, so that the early warning system can respond to the dynamic changes of the reservoir water level in a timely manner. Then, the minimum recharge rate of the internal reservoir water storage 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 recharge path of the historical surface infiltration data is drawn, which involves the application of the geographic information system (GIS), and the spatial analysis technology is used to depict the changes in the groundwater recharge path. The groundwater recharge path and the historical groundwater recharge path are tested for infiltration fractures, and the pattern recognition technology is used to identify anomalies in the infiltration path, and the first-layer groundwater threshold is generated based on the fracture degree. Next, the groundwater level time series analysis of the surface infiltration data and the groundwater aquifer recharge data is used, and the minimum threshold analysis is performed in combination with the historical groundwater drought data, and finally the second-layer groundwater threshold is generated. This series of steps effectively ensures the dynamic monitoring of groundwater resources and drought early warning. The analysis of the minimum river water level threshold of the historical river seasonal water level change data relies on the time series analysis technology, and the relationship between the river water flow and the water level is established by using the flow dynamic critical value analysis. 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 conditions, thereby marking the time point of drought triggering and forming river water drought disaster data. The drought disaster data and hydrological characteristic data sets of reservoirs, groundwater and river water are uniformly quantified by thresholds, and the multivariate statistical analysis method is used to generate a multi-water source drought early warning threshold calculation algorithm.

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

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

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

[0104] In the embodiment of the present invention, the dimensional analysis of the hydrological characteristic data set is first involved, the purpose is to extract the hydrological characteristics of different sources from the original data and construct a multi-dimensional input data set. The composition of the multi-dimensional input data includes reservoir dimension data, groundwater dimension data and river water dimension data, which are processed by statistical analysis and data mining technology to reveal the relationship and characteristics between different water sources. In this process, the hydrological characteristic data set is first reduced in dimension and feature extracted by methods such as principal component analysis (PCA) or factor analysis. These methods can effectively convert high-dimensional data into representations in 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 data set lays a solid foundation for subsequent model construction. Multi-dimensional models are constructed using reservoir dimension data, groundwater dimension data and river water dimension data to realize the comprehensive application of multi-water source drought warning threshold calculation algorithm. This process mainly realizes the integration and analysis of multi-source hydrological data by constructing multiple linear regression models, support vector machines (SVM) or deep learning algorithms. These technologies can handle nonlinear relationships and complex data structures, allowing the model to accurately reflect the mutual influence between different water sources. In the process of model construction, the above multi-dimensional input data must first be standardized to eliminate the impact of the dimension and improve the convergence speed and stability of the model. Then, by dividing 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: Performing a permeation complementary association between the reservoir dimension data and the groundwater dimension data to generate reservoir-groundwater association interaction data;

[0107] Step S322: Complementarily associate the river water dimension data and the reservoir dimension data with each other in terms of water level and storage capacity to generate river-reservoir correlation interaction data;

[0108] Step S323: performing temporal change complementary association between groundwater dimension data and river water dimension data to generate groundwater-river correlation interaction data;

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

[0110] In an embodiment of the present invention, the reservoir dimension data and the groundwater dimension data are infiltrated and complementary. This technical means uses correlation analysis and regression models in statistics to explore the mutual influence between reservoirs and groundwater. By analyzing the relationship between the water storage capacity of the reservoir and the change of groundwater level, reservoir-groundwater correlation interaction data can be generated to reveal the dynamic balance and mutual replenishment mechanism of water resources. The river water dimension data and the reservoir dimension data are complementary associated with water level and storage capacity. Here, time series data analysis methods such as time series model (ARIMA) or sliding average method are used to analyze the dynamic relationship between river water level change and reservoir water level. Through this association, river-reservoir correlation interaction data can be generated, further enriching the understanding and early warning capabilities of the hydrological system. The groundwater dimension data and the river water dimension data are complementary associated with time series changes, and time series analysis technology, especially using algorithms such as dynamic time warping (DTW), are used to analyze the mutual influence between groundwater and river water at different time nodes. The groundwater-river interaction data generated by this process helps to identify the causal relationship between groundwater recharge and river flow changes, thereby achieving more accurate drought prediction. A hydrological multidimensional interaction model is constructed based on multidimensional input data, reservoir-groundwater interaction data, river-reservoir interaction data, and groundwater-river interaction data. Multiple regression analysis, machine learning algorithms (such as random forests or support vector machines), and deep learning models (such as LSTM) are applied here to integrate multiple data sources and extract the complex relationships between various hydrological dimensions.

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

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

[0113] Step S42: Utilize the multi-source hydrological drought warning and prediction model in combination with real-time hydrological data to perform multi-dimensional drought prediction and generate drought disaster prediction data, thereby completing the drought disaster prediction task, wherein 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 embodiment of the present invention, obtaining real-time hydrological data is a basic work, involving multidimensional data of reservoirs, groundwater and rivers, wherein the real-time data of reservoirs includes real-time data mainly including water level data, reservoir storage capacity data, real-time groundwater data including real-time data mainly including groundwater level data, spring flow data and real-time river water data including real-time data mainly including river water level data and river flow data. This process uses an automated data acquisition system to collect various types of hydrological data in real time through sensors and monitoring equipment, including water level, flow, precipitation and groundwater level changes. These data are integrated through the Internet of Things technology to form a real-time hydrological database, providing raw data support for subsequent analysis. The real-time hydrological data is analyzed using a multi-source hydrological drought warning and prediction model, mainly using a combination of statistics and machine learning. By controlling and screening the quality of real-time data, the accuracy and reliability of the data used are ensured, and then time series analysis techniques such as ARIMA (autoregressive integrated moving average model) are used to model historical data and mine the time series characteristics of hydrological data to predict future hydrological conditions. In addition, by combining real-time data with existing multi-source hydrological drought warning models, deep learning algorithms (such as LSTM, i.e. long short-term memory network) are used to capture complex nonlinear 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 water supply drought prediction data. To achieve this goal, the model conducts a comprehensive analysis through multi-dimensional input data to generate drought risk assessments for different water sources. Reservoir drought prediction data evaluates the possibility of future droughts by analyzing reservoir water level changes and water storage trends, combined with historical drought events; while groundwater supply drought prediction data focuses on the balance of groundwater recharge and consumption, and identifies potential risks of water level decline. River water supply drought prediction data evaluates the sustainability and reliability of river water supply through flow dynamic analysis combined with precipitation data.

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

[0116] Step S421: using a multi-source hydrological drought warning and prediction model to perform time series drought prediction on reservoir real-time data to generate reservoir drought prediction data;

[0117] Step S422: using a multi-source hydrological drought warning and prediction model to perform water level recharge balance drought prediction on groundwater real-time data to generate groundwater supply drought prediction data;

[0118] Step S423: using a multi-source hydrological drought warning and prediction model to perform water flow and water supply prediction on the real-time river water data to generate river water supply drought prediction data;

[0119] Step S424: The reservoir drought prediction data, the groundwater supply drought prediction data and the river water supply drought prediction data are sent through the cloud platform, and drought disasters are reminded, thereby completing the drought disaster prediction and early warning operation.

[0120] In the embodiment of the present invention, a multi-source hydrological drought warning and prediction model is used to predict the time series drought of the real-time data of the reservoir. This process uses time series analysis technology, combines historical data with real-time monitoring data, and models the changes in reservoir water levels through autoregressive models, sliding averages, and seasonal trend analysis methods to identify potential time nodes for drought. 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 groundwater real-time data also relies on the multi-source hydrological drought warning and prediction model. Here, the model dynamically monitors the recharge and consumption of groundwater, combines the data of precipitation, evaporation, and surface permeability, implements the analysis and prediction of water level change trends, and then generates groundwater supply drought prediction data. This process uses a balance equation model to emphasize the dynamic changes in water source recharge capacity to reflect the health of the groundwater level and its sensitivity to drought risks. The water flow and water supply prediction of river water real-time data uses a similar modeling idea. Through real-time water flow monitoring, combined with historical flow change data, flow regression analysis and machine learning algorithms (such as random forest regression) are used to predict the changing trend of river water supply and generate river water supply drought prediction data. This method not only takes into account the seasonal changes in flow, but also introduces the correlation between meteorological factors and hydrological characteristics to achieve a comprehensive assessment of the river's water supply capacity. The generated reservoir drought prediction data, groundwater supply drought prediction data, and river water supply drought prediction data are sent through the cloud platform to achieve real-time information sharing and drought disaster reminders.

[0121] In this specification, a rural water supply project drought disaster prediction and early warning system is provided, which is used to execute 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 hydrological data sets; perform data preprocessing on the hydrological data sets to generate hydrological core processing data sets;

[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-water source drought early warning threshold calculation algorithm;

[0124] Multi-dimensional interactive model building module: Utilizes the multi-water source drought warning threshold calculation algorithm and combines the hydrological characteristic data set to build a multi-dimensional interactive model to generate a multi-source hydrological drought warning and prediction model;

[0125] Drought disaster prediction and warning module: Use multi-source hydrological drought warning and prediction models to conduct multi-dimensional drought prediction, generate drought disaster prediction data, and thus complete drought disaster prediction and warning operations.

[0126] The beneficial effect of the present invention is that a hydrological core processing data set is generated by acquiring 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. The key to this step is to ensure that hydrological data of different water source types can be effectively integrated on the same platform, avoiding the processing complexity caused by differences in data sources. By extracting data features from the core processing data set, the accurate extraction of various water source features such as reservoirs, groundwater, and river water is ensured, and a drought warning threshold calculation algorithm is further generated through quantitative processing. The algorithm innovatively introduces multi-layer threshold settings 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 guaranteed, providing a solid data foundation for subsequent model construction. By interacting the drought warning threshold calculation algorithm with the hydrological feature data set, a multidimensional drought warning and prediction model is constructed, which further enhances the accuracy and robustness of the system in predicting drought disasters. The multidimensional interactive model achieves accurate predictions under various hydrological conditions by integrating the hydrological characteristics and warning thresholds of different water sources, and effectively improves the adaptability of the prediction model under different regions and different water source conditions. The multidimensional interactive model is used to perform multidimensional predictions of drought disasters and generate specific disaster prediction data, thereby providing comprehensive data support in the drought early warning system and achieving full process automation and efficiency of the prediction operation. Therefore, the present invention solves the problem of inaccurate predictions caused by inconsistent multi-source data in traditional early warning systems by optimizing hydrological data processing and early warning threshold calculation, and greatly improves the accuracy and reliability of drought disaster early warnings.

[0127] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0128] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may 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 the embodiments shown herein, but should conform to the widest 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: The following steps are involved: Step S1: Obtain hydrological data set; Preprocess the hydrological data set to generate a hydrological core processing data set; Step S2: extracting data features from the hydrological core processing data set to generate a hydrological feature data set; Quantify the hydrological characteristic dataset to generate a multi-water source drought warning threshold calculation algorithm; Step S3: using a multi-water source drought warning threshold calculation algorithm and combining it with a hydrological characteristic data set to construct a multi-dimensional interactive model and 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 and generate drought disaster prediction data, thereby completing the drought disaster prediction and warning operation.

2. The method for predicting and warning drought disasters in rural water supply projects according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire a hydrological data set, wherein the hydrological data set includes water storage-precipitation data, water level change data, surface infiltration data, and underground aquifer recharge data; Step S12: performing data set cleaning processing on the hydrological data set to generate hydrological cleaned data; Step S13: annotate the hydrological cleansing data into a data set format to generate a hydrological core processing data set.

3. The method for predicting and warning drought disasters in rural water supply projects according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: extracting data features from the hydrological core processing data set to generate a hydrological feature data set; Step S22: confirm the warning threshold according to the hydrological characteristic data set, and quantify the warning through the warning threshold, so as to generate a multi-water source drought warning threshold calculation algorithm.

4. The method for predicting and warning drought disasters in rural water supply projects according to claim 3 is characterized in that: Step S21 includes the following steps: Step S211: Calculate and evaluate the storage capacity change rate of the water storage-precipitation data to generate reservoir storage capacity change rate replenishment data; Calculate the reservoir water replenishment contribution rate of the reservoir storage capacity change rate replenishment data and water level changes to generate reservoir hydrological characteristic data; Step S212: Draw a groundwater recharge path for the surface infiltration data to generate a groundwater recharge path; perform aquifer recharge and consumption balance analysis on the surface infiltration data and the underground aquifer recharge data through the groundwater recharge path to generate groundwater hydrological characteristic data; Step S213: Obtain historical river seasonal water level change data; perform water level fluctuation amplitude analysis based on the water level change data, and analyze water level fluctuation characteristics in combination with historical river seasonal water level change data to generate river water level fluctuation data; perform flow characteristic evaluation on the river water level fluctuation data to generate river water hydrological characteristic data; Step S214: Integrate the reservoir hydrological characteristic data, groundwater hydrological characteristic data and river water hydrological characteristic data into a hydrological characteristic data set to generate a hydrological characteristic data set.

5. The method for predicting and warning drought disasters in rural water supply projects according to claim 3, characterized in that: Step S22 includes the following steps: Step S221: Acquire historical drought data of the reservoir; compare and analyze the reservoir capacity change rate replenishment data with the historical drought data of the reservoir to generate reservoir water level drought critical point data; compare the reservoir capacity change rate replenishment data with the critical capacity change threshold to generate the first-level threshold of reservoir drought warning; calculate the minimum recharge rate of water storage in the reservoir based on the reservoir hydrological characteristic data to generate the second-level threshold of reservoir drought warning; generate a dynamic curve of the reservoir hydrological characteristic data, the first-level threshold of drought warning and the second-level threshold of reservoir drought warning to generate a dynamic curve of reservoir warning threshold; mark the dynamic curve of reservoir warning threshold with the warning critical value to generate reservoir drought disaster data; Step S222: Acquire historical surface infiltration data and historical groundwater drought data; draw historical recharge paths for historical surface infiltration data to generate historical groundwater recharge paths; perform infiltration fracture detection on the groundwater recharge paths and historical groundwater recharge paths, and generate groundwater thresholds based on the fracture degree to generate the first-layer groundwater thresholds; perform groundwater level time series analysis on the surface infiltration data and underground aquifer recharge data, and perform minimum threshold analysis based on the historical groundwater drought data to generate the second-layer groundwater thresholds; perform fluctuation safety range analysis based on the groundwater hydrological characteristic data, the first-layer groundwater thresholds, and the second-layer groundwater thresholds, and perform drought disaster point analysis based on the groundwater recharge paths to generate groundwater drought disaster data; Step S223: Perform a minimum river water level threshold analysis on the historical river seasonal water level change data, and use the river water hydrological characteristic data to confirm the minimum threshold to generate the first-layer river water threshold; perform a flow dynamic critical value analysis based on the river water hydrological characteristic data to generate the second-layer river water threshold; bind the water level change data, the first-layer river water threshold and the second-layer river water threshold to trigger conditions, and mark the trigger time point to generate river water drought disaster data; Step S224: The reservoir drought disaster data, groundwater drought disaster data, river water drought disaster data and hydrological characteristic data set are uniformly quantified by threshold, and an algorithm is generated to generate a multi-water source drought warning threshold calculation algorithm.

6. The method for predicting and warning drought disasters in rural water supply projects according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: Performing dimensional analysis on the hydrological characteristic data set to generate multi-dimensional input data, wherein the multi-dimensional input data includes reservoir dimensional data, groundwater dimensional data and river water dimensional data; Step S32: construct a multi-dimensional model of the multi-water source drought warning threshold calculation algorithm using reservoir dimension data, groundwater dimension data and river water dimension data to generate a multi-source hydrological drought warning and prediction model.

7. The method for predicting and warning drought disasters in rural water supply projects according to claim 6, characterized in that: Step S32 includes the following steps: Step S321: Performing a permeation complementary association between the reservoir dimension data and the groundwater dimension data to generate reservoir-groundwater association interaction data; Step S322: Complementarily associate the river water dimension data and the reservoir dimension data with each other in terms of water level and storage capacity to generate river-reservoir correlation interaction data; Step S323: performing temporal change complementary association between groundwater dimension data and river water dimension data to generate groundwater-river correlation interaction data; Step S324: construct a hydrological multidimensional interactive model for the multi-dimensional input data, reservoir-groundwater correlation interaction data, river-reservoir correlation interaction data and groundwater-river correlation interaction data to generate a multi-source hydrological drought warning and prediction model.

8. The method for predicting and warning drought disasters in rural water supply projects according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: acquiring real-time hydrological data, wherein the real-time hydrological data includes real-time reservoir data, real-time groundwater data and real-time river water data; Step S42: Utilize the multi-source hydrological drought warning and prediction model in combination with real-time hydrological data to perform multi-dimensional drought prediction and generate drought disaster prediction data, thereby completing the drought disaster prediction task, wherein the drought disaster prediction data includes reservoir drought prediction data, groundwater supply drought prediction data and river water supply drought prediction data.

9. The method for predicting and warning drought disasters in rural water supply projects according to claim 8, characterized in that: Step S42 includes the following steps: Step S421: using a multi-source hydrological drought warning and prediction model to perform time series drought prediction on reservoir real-time data to generate reservoir drought prediction data; Step S422: using a multi-source hydrological drought warning and prediction model to perform water level recharge balance drought prediction on groundwater real-time data to generate groundwater supply drought prediction data; Step S423: using a multi-source hydrological drought warning and prediction model to perform water flow and water supply prediction on the real-time river water data to generate river water supply drought prediction data; Step S424: The reservoir drought prediction data, the groundwater supply drought prediction data and the river water supply drought prediction data are sent through the cloud platform, and drought disasters are reminded, thereby completing the drought disaster prediction and early warning operation.

10. A drought disaster prediction and early warning system for rural water supply projects, characterized in that: Used to execute the method for predicting and warning drought disasters in rural water supply projects as claimed in claim 1, the rural water supply project drought disaster prediction and warning system comprises: Hydrological data processing module: used to obtain hydrological data sets; perform data preprocessing on the hydrological data sets to generate hydrological core processing data sets; 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-water source drought early warning threshold calculation algorithm; Multi-dimensional interactive model building module: used to use the multi-water source drought warning threshold calculation algorithm and combine it with the hydrological characteristic data set to build a multi-dimensional interactive model to generate a multi-source hydrological drought warning and prediction model; Drought disaster prediction and warning module: used to use multi-source hydrological drought warning and prediction models to conduct multi-dimensional drought prediction, generate drought disaster prediction data, and thus complete drought disaster prediction and warning operations.

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