Intelligent Data-Based Reservoir Water Resources Information Management Method and System

Abstract

The present invention relates to the technical field of information management, and particularly to a method and system for reservoir water resource information management based on intelligent data. The method includes the following steps: obtaining a dataset of the reservoir surface area; performing data preprocessing on the dataset of the reservoir surface area to generate a preprocessed dataset of the reservoir surface area; performing discrete particle node processing on the preprocessed dataset of the reservoir surface area and constructing a loss impact factor database to generate an upper-layer loss impact factor database; constructing a loss rate calculation model for the upper-layer loss impact factor database to generate an upper-layer loss rate prediction model; Therefore, the present invention solves the problems of information asymmetry and slow response in traditional water resource management by constructing an intelligent data reservoir water resource information management system, and improves the scientificity and pertinence of reservoir water resource loss monitoring and protection measures.

Description

Technical Field

[0001] The present invention relates to the technical field of information management, and particularly to a method and system for reservoir water resource information management based on intelligent data. Background Art

[0002] Existing systems lack dynamic monitoring of the operation status of reservoirs, are unable to respond in a timely manner to environmental changes and fluctuations in hydrological conditions, resulting in the lag of water resource management. In addition, the data analysis means are relatively single, lacking the ability of in-depth multi-dimensional analysis, and it is difficult to effectively mine potential loss risks and environmental impact factors. At the same time, traditional technologies have obvious shortcomings in data integration and sharing, and the interconnection between different data sources is insufficient, leading to the formation of information islands, making decision-makers face the dilemma of incomplete information when formulating management strategies. In addition, existing technologies rely mostly on static models for the monitoring and early warning of soil erosion, and do not fully consider the impacts of changes in the reservoir surrounding environment, land use changes, and climate factors, resulting in insufficient risk assessment and early warning capabilities. Finally, the current reservoir water resource management system lacks intelligent and automated functions and does not fully utilize modern information technologies such as big data analysis, Internet of Things technology, and artificial intelligence. These deficiencies limit the scientific nature and accuracy of reservoir water resource information management and affect the sustainable utilization of water resources. Summary of the Invention

[0003] Based on this, it is necessary to provide a method and system for reservoir water resource information management based on intelligent data to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for reservoir water resource information management based on intelligent data, the method includes the following steps:

[0005] Step S1: Obtain a data set of the reservoir surface area; perform data preprocessing on the data set of the reservoir surface area to generate a preprocessed data set of the reservoir surface area; perform discrete particle node processing on the preprocessed data set of the reservoir surface area, and construct a loss impact factor database to generate an upper-layer loss impact factor database; construct a loss rate calculation model for the upper-layer loss impact factor database to generate an upper-layer loss rate prediction model;

[0006] Step S2: Obtain historical groundwater level data, real-time groundwater level data, and surface permeability data; compare the historical groundwater level data and the real-time groundwater level data to generate groundwater level change data; combine the groundwater level change data with the surface permeability data to draw a spatial distribution map and mark high-loss areas to generate a loss distribution map of the lower-layer reservoir area;

[0007] Step S3: Conduct in-depth particle loss relationship analysis on the upper-layer loss rate prediction model and the lower-layer reservoir area loss distribution map to generate upper and lower layer loss relationship result data; perform linkage model correlation processing on the upper-layer loss rate prediction model based on the lower-layer reservoir area loss distribution map to generate an upper and lower layer linkage model for the reservoir area;

[0008] Step S4: Generate soil and water protection strategies for the upper and lower layer linkage model of the reservoir area based on the upper and lower layer loss relationship result data to generate upper and lower layer protection strategies for the reservoir, thus completing the intelligent data reservoir water resource information management operation.

[0009] The beneficial effects of the present invention are as follows. By obtaining the reservoir surface area data set and performing discrete particle node processing, the generated upper-layer loss impact factor database provides a solid data foundation for the construction of the loss rate prediction model. This process ensures comprehensive consideration of various factors affecting the surface loss of the reservoir, thereby improving the accuracy and reliability of the loss rate prediction. By comparing historical and real-time groundwater level data, generating groundwater level change data, and combining with surface permeability to draw a spatial distribution map, high-loss areas can be clearly identified. This method makes the monitoring and evaluation of the loss phenomenon more intuitive and helps to formulate corresponding management measures in a timely manner. The in-depth particle analysis of the upper and lower layer loss relationships provides correlation data between the loss rate and reservoir management, enabling a comprehensive analysis of the water loss conditions at different levels. Through the construction of the linkage model, the quantification of the mutual influence of upper and lower layer losses is realized, promoting the comprehensive management and optimization of water resources. Finally, the soil and water protection strategies generated based on the upper and lower layer loss relationship result data can specifically solve the problems in severely eroded areas. For example, vegetation restoration and cover layer reinforcement are carried out in areas with severe upper-layer erosion, while deep water blocking or groundwater recharge measures are implemented in areas with severe lower-layer infiltration erosion. Such strategies not only improve the protection effect of soil and water resources but also enhance the ecological stability of the reservoir. Therefore, the present invention solves the problems of information asymmetry and slow response in traditional water resource management by constructing an intelligent data reservoir water resource information management system, and improves the scientificity and pertinence of reservoir water resource loss monitoring and protection measures.

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

[0011] Step S11: Obtain the reservoir surface area data set; perform data preprocessing on the reservoir surface area data set to generate a preprocessed data set for the reservoir surface area;

[0012] Step S12: Perform discrete particle node processing on the preprocessed data set for the reservoir surface area to generate upper-layer discrete particles for the reservoir area; perform coefficient calculation and analysis on the upper-layer discrete particles for the reservoir area, and construct a loss impact factor database to generate an upper-layer loss impact factor database;

[0013] Step S13: Construct a loss rate calculation model for the upper-layer loss impact factor database to generate an upper-layer loss rate prediction model.

[0014] The process of obtaining the reservoir surface area dataset in the present invention lays a foundation for subsequent analysis, ensuring the comprehensiveness and reliability of the data source. This process not only provides preliminary data support for identifying loss characteristics but also lays a foundation for subsequent data integration and analysis. The discrete particle node processing of the preprocessed dataset in the reservoir surface area can accurately reflect the surface hydrological characteristics and their dynamic changes related to loss by subdividing the data into smaller granularities. This refinement process not only improves the accuracy of data analysis but also provides more detailed basic data for in-depth calculation of loss impact factors. By performing coefficient calculation and analysis on the upper-layer discrete particles to construct a loss impact factor database, key factors affecting the loss of the reservoir surface can be systematically summarized, thus providing strong data support for quantitative prediction of the loss rate. The establishment of this database comprehensively presents the correlation of loss impact factors, providing sufficient basis and conditions for the subsequent loss rate calculation model. Finally, by constructing a loss rate calculation model for the upper-layer loss impact factor database, accurate prediction of the loss rate of the reservoir surface is achieved. This model can not only effectively simulate loss changes under different environmental conditions but also provide a scientific basis for the management and decision-making of reservoir water resources, helping managers formulate reasonable soil and water conservation measures.

[0015] Preferably, step S12 includes the following steps:

[0016] Step S121: Perform discrete particle node processing on the preprocessed dataset of the reservoir surface area to generate upper-layer discrete particles in the reservoir area;

[0017] Step S122: Extract spatial position eigenvalue data for the upper-layer discrete particles in the reservoir area to generate spatial position eigenvalue data for the reservoir area; extract loss potential eigenvalue data for the upper-layer discrete particles in the reservoir area to generate loss potential eigenvalue data for the reservoir area; extract environmental eigenvalue data for the upper-layer discrete particles in the reservoir area to generate environmental eigenvalue data for the reservoir area;

[0018] Step S123: Perform rainfall erosion coefficient calculation and analysis on the spatial position eigenvalue data and environmental eigenvalue data of the reservoir area to generate a surface rainfall-erosion coefficient; perform wind speed loss coefficient calculation and analysis on the spatial position eigenvalue data and loss potential eigenvalue data of the reservoir area to generate a wind speed-loss coefficient; perform slope soil coefficient calculation on the loss potential eigenvalue data and environmental eigenvalue data of the reservoir area to generate a soil type-slope coefficient;

[0019] Step S124: Construct a loss impact factor database with the surface rainfall-erosion coefficient, wind speed-loss coefficient, and soil type-slope coefficient, and generate an upper-layer loss impact factor database.

[0020] Through the discrete particle node processing of the preprocessing dataset in the surface area of the reservoir, the data of the present invention becomes more refined, which helps to capture more microscopic hydrological features. The generated upper-layer discrete particles lay a foundation for the extraction of subsequent eigenvalue. By extracting the spatial position eigenvalue, loss potential eigenvalue, and environmental eigenvalue of the discrete particles, a multi-dimensional feature system is comprehensively constructed. This process not only reflects the influence of different spatial positions on loss, but also reveals the complex relationship between environmental conditions and loss potential, forming systematic data support for subsequent analysis. By calculating and analyzing the extracted eigenvalues for the rainfall erosion coefficient, wind speed loss coefficient, and soil type-slope coefficient, the composition of the loss impact factor is further refined. The calculation of the rainfall erosion coefficient can reveal the direct impact of rainfall intensity on the surface loss of the reservoir. The wind speed loss coefficient takes into account the influence of wind speed on soil stability. The soil type-slope coefficient provides a quantitative basis for the loss characteristics under different soil types and slope conditions. The calculation of these coefficients makes the analysis of the loss impact factor more accurate, thus providing a clear guiding basis for the formulation of management measures. Integrating the above calculation results into an upper-layer loss impact factor database forms a structured data platform. This database not only systematically summarizes various loss impact factors, but also provides important basic data for subsequent loss rate prediction models and management strategies. In this way, the present invention realizes the in-depth analysis of the surface loss characteristics of the reservoir, making the loss monitoring and management more scientific and targeted, and further improving the effective management and protection ability of water resources.

[0021] Preferably, step S13 includes the following steps:

[0022] Step S131: Divide the preset upper-layer loss rate model into three layers to generate a three-layer reservoir area loss rate model, where the three-layer reservoir area loss rate model includes the first layer of the reservoir area, the second layer of the reservoir area, and the third layer of the reservoir area;

[0023] Step S132: Calculate the rainfall erosion impact on the first layer of the reservoir area with the surface rainfall-erosion coefficient to generate the first layer of the reservoir area loss rate; Quantify the wind erosion impact on the first layer of the reservoir area with the surface rainfall-erosion coefficient to generate the second layer of the reservoir area loss rate; Calculate the soil slope adaptability of the third layer of the reservoir area with the soil type-slope coefficient to generate the third layer of the reservoir area loss rate

[0024] Step S133: Perform multi-layer weight value assignment on the first layer, second layer, and third layer of the reservoir area loss rate, and perform model recombination to generate an upper-layer loss rate prediction model.

[0025] In the present invention, by dividing the preset upper-layer loss rate model into three layers, a structured three-layer model of the reservoir area loss rate is formed. This stratification strategy not only makes the analysis of loss characteristics more systematic but also provides a clear framework for the application of loss impact factors at different levels subsequently. The division of the first layer, second layer, and third layer of the reservoir area enables each layer to independently reflect the impact of different environmental factors on loss, thereby improving the flexibility and adaptability of the model. By calculating and replacing the eigenvalue for each layer, the loss rate model can dynamically reflect the loss characteristics under the current environmental conditions. Specifically, the application of the surface rainfall-erosion coefficient in the first layer can directly reveal the impact of rainfall on reservoir loss; the application of the wind speed-loss coefficient in the second layer takes into account the dynamic effect of wind speed changes on soil loss; the use of the soil type-slope coefficient in the third layer provides a quantitative basis for the loss characteristics under different soil and slope conditions. Through this hierarchical eigenvalue replacement, the prediction ability of the model is significantly enhanced, and it can more accurately reflect the change trend of the loss rate. Perform multi-layer weight value assignment and model recombination on the three-layer loss rate data to generate an upper-layer loss rate prediction model. The key to this process lies in the weight assignment of the loss rates at different levels, enabling the reasonable integration of the influence relationships between each layer, thereby forming a comprehensive and refined loss rate prediction system. By combining the loss characteristics at different levels, this model can not only effectively predict the loss rate but also provide targeted decision-making support for reservoir managers, enabling them to formulate more scientific soil and water conservation strategies.

[0026] Preferably, step S2 includes the following steps:

[0027] Step S21: Obtain historical groundwater level data, real-time groundwater level data, and surface permeability data; compare the historical groundwater level data and the real-time groundwater level data to generate groundwater level change data;

[0028] Step S22: Perform spatial processing on the surface permeability data to generate lower-layer loss rate data; perform coordinate mapping on the lower-layer loss rate data to generate a lower-layer groundwater infiltration distribution coordinate map;

[0029] Step S23: Draw a spatial distribution map of the groundwater level change data and the lower-layer groundwater infiltration distribution coordinate map to generate a lower-layer reservoir area loss distribution map.

[0030] The present invention establishes a comprehensive hydrological data foundation by obtaining historical groundwater level data, real-time groundwater level data, and surface permeability data. The comparative analysis of historical and real-time data not only provides a clear time-series perspective for identifying groundwater level changes but also lays a solid foundation for the subsequent assessment of erosion characteristics. The generated groundwater level change data provides an important basis for understanding the hydrological dynamics of the lower layer of the reservoir, enabling managers to promptly grasp the fluctuations in water levels and thus better predict erosion risks. Spatial processing of the surface permeability data enables the conversion of the data structure of erosion rates into more intuitive spatial information. This process not only enhances the visualization of the data but also generates a coordinate map of the lower groundwater infiltration distribution through coordinate mapping. The generation of this map reveals the spatial distribution characteristics of erosion rates, thereby providing strong support for subsequent hydrological analysis. Through these spatial data, high-risk erosion areas can be clearly identified, providing a scientific basis for relevant protection measures and decision-making. Plotting the spatial distribution maps of the groundwater level change data and the coordinate map of the lower groundwater infiltration distribution generates a loss distribution map of the lower reservoir area, further enhancing the spatial analysis ability of erosion monitoring. This loss distribution map intuitively shows the relationship between erosion characteristics and groundwater dynamics, enabling managers to quickly locate potential erosion areas and then take timely intervention measures. By spatially integrating different data levels, the generated loss distribution map not only improves the efficiency of erosion monitoring but also provides an important scientific basis for reservoir management decision-making.

[0031] Preferably, step S23 includes the following steps:

[0032] Step S231: Calculate the erosion rate of the monitoring points based on the groundwater level change data and the surface permeability data to generate the erosion rate data of the lower monitoring points;

[0033] Step S232: Use GIS technology to upload the erosion rate data of the lower monitoring points to the coordinate map of the lower groundwater infiltration distribution for drawing the spatial infiltration distribution map, generating a loss distribution map of the lower reservoir area;

[0034] Step S233: Mark the high-erosion areas on the loss distribution map of the lower reservoir area to generate the high-erosion area data of the lower reservoir area.

[0035] The present invention calculates the loss rate of monitoring points based on the groundwater level change data and the surface permeability data, and generates the loss rate data of the lower-layer monitoring points. This process not only realizes the quantitative analysis of the loss rate, but also provides key data support for subsequent spatial distribution and management strategies. The calculation of the loss rate of monitoring points, combined with real-time and historical data, makes the assessment of the loss risk more scientific and reliable, providing a basis for reservoir managers to timely grasp the loss dynamics. Using GIS technology, the loss rate data of the lower-layer monitoring points is uploaded to the lower-layer groundwater infiltration distribution coordinate map for drawing the spatial infiltration distribution map, and the generated loss distribution map of the lower-layer reservoir area visually displays the spatial distribution of the loss characteristics. Through GIS technology, not only the visualization effect of the data is improved, but also the comparison and analysis ability of the loss rates between different monitoring points is enhanced. This loss distribution map can effectively reveal the spatial variation law of the loss rate, providing an intuitive reference basis for reservoir managers to formulate targeted protection measures. Mark the high-loss areas on the loss distribution map of the lower-layer reservoir area to generate the high-loss area data of the lower-layer reservoir area. This marking process makes the focus of loss monitoring clearer, helps managers quickly identify and concentrate resources on high-risk areas, thereby improving the efficiency and effectiveness of loss management. By combining the loss rate data with the spatial distribution map, the generated high-loss area data provides a scientific basis for subsequent soil and water conservation strategies, enabling the precise implementation of intervention measures for different loss characteristics.

[0036] Preferably, step S3 includes the following steps:

[0037] Step S31: Use factor analysis method to conduct in-depth particle loss relationship analysis on the upper-layer loss rate prediction model and the loss distribution map of the lower-layer reservoir area, and generate the upper and lower layer loss relationship result data;

[0038] Step S32: Identify the soil and water loss relationship of the upper-layer loss rate prediction model based on the high-loss area data of the lower-layer reservoir area, and generate the different loss spatial characteristic data of the reservoir area;

[0039] Step S33: Use the different loss spatial characteristic data of the reservoir area and the loss distribution map of the lower-layer reservoir area to conduct in-depth particle loss relationship analysis on the upper-layer loss rate prediction model, and generate the upper and lower layer loss relationship result data;

[0040] Step S34: Conduct linkage model association processing on the upper-layer loss rate prediction model based on the loss distribution map of the lower-layer reservoir area, and generate the upper and lower layer linkage model of the reservoir area.

[0041] The present invention conducts in-depth analysis of the particle loss relationship between the upper-layer loss rate prediction model and the lower-layer reservoir area loss distribution map by using factor analysis, thereby generating the upper and lower layer loss relationship result data. This process not only reveals the dynamic relationship between the upper and lower layer losses, but also provides data support for the identification of loss characteristics, enabling managers to scientifically understand the mutual influence of losses at different levels and providing a basis for loss management decisions. Based on the data of high-loss areas in the lower-layer reservoir area, the soil and water loss relationship of the upper-layer loss rate prediction model is identified, and the different loss spatial characteristic data of the reservoir area are generated. The generation of this data clarifies the spatial characteristics of losses, thus achieving a comprehensive understanding of the loss phenomenon and improving the efficiency of formulating management strategies for high-loss areas. Through this analysis, the variation law of the loss rate under different environmental conditions can be identified, providing an important reference for the design of subsequent protection measures. Using the different loss spatial characteristic data of the reservoir area and the lower-layer reservoir area loss distribution map, in-depth analysis of the particle loss relationship of the upper-layer loss rate prediction model is carried out to further generate the upper and lower layer loss relationship result data. This link not only deepens the understanding of the upper and lower layer loss relationship, but also provides richer data support for loss risk assessment. By analyzing the relationship between different spatial characteristics and losses, potential risk areas can be more effectively identified, providing a scientific basis for precise intervention. Based on the lower-layer reservoir area loss distribution map, linkage model association processing is carried out on the upper-layer loss rate prediction model to generate the upper and lower layer linkage model of the reservoir area. The generation of this model enables managers to comprehensively consider the loss relationship between the upper and lower layers, thereby formulating more effective management strategies and realizing the coordinated prevention and control of soil and water loss. By conducting linkage analysis on the loss characteristics of the upper and lower layers, the monitoring ability of the overall loss situation can be improved, providing strong data support for the sustainable management of water resources.

[0042] Preferably, step S4 includes the following steps:

[0043] Step S41: Based on the upper and lower layer loss relationship result data, conduct loss potential assessment on the upper and lower layer linkage model of the reservoir area to generate the upper and lower layer loss potential assessment data of the reservoir area; generate the upper and lower layer linkage factor result data from the upper and lower layer loss potential assessment data of the reservoir area, where the upper and lower layer linkage factor result data includes the upper layer severe loss result and the lower layer severe penetration result;

[0044] Step S42: Generate soil and water protection strategies from the upper and lower layer linkage factor result data to generate the upper and lower layer protection strategies of the reservoir;

[0045] Step S43: Use the upper and lower layer protection strategies of the reservoir to complete the reservoir information management operation.

[0046] Based on the result data of the upper and lower layer loss relationship, the present invention generates the upper and lower layer linkage factor results for the upper and lower layer linkage model of the reservoir area. The generated upper and lower layer linkage factor result data specifically includes the result of serious upper layer loss and the result of serious lower layer seepage. This process not only realizes the accurate identification of the loss characteristics between the upper and lower layers, but also lays a data foundation for the subsequent formulation of protection strategies. By analyzing the upper and lower layer linkage factors, the severity of losses at different levels can be clarified, providing managers with an intuitive understanding of the loss conditions in different areas of the reservoir, thus helping to formulate more targeted management measures. Generating soil and water protection strategies for the upper and lower layer linkage factor result data forms the upper and lower layer protection strategies for the reservoir. The formulation of this strategy not only relies on scientific data analysis, but also combines a profound understanding of the loss characteristics, ensuring the effectiveness and feasibility of the protection measures. By accurately identifying the loss problems in the upper and lower layers, managers can consider the characteristics of different levels when formulating strategies, thus achieving the overall coordination and optimization of soil and water loss prevention and control. Especially when facing a complex natural environment, this strategy can more effectively respond to potential loss risks and improve the ecological security of the reservoir. Using the upper and lower layer protection strategies for the reservoir, the information management operation of the reservoir is completed. The implementation of this link makes the entire reservoir management system more complete and efficient, ensuring the smooth progress of the information management operation. By combining the protection strategy with actual management operations, the soil and water resources of the reservoir can be effectively maintained under scientific guidance, thereby promoting the sustainable utilization of water resources. In addition, the implementation of the strategy can timely feedback the effect of loss management, further enrich the data, and promote the optimization and adjustment of subsequent management measures, thus forming a dynamic management system.

[0047] Preferably, step S42 includes the following steps:

[0048] Step S421: Generate soil and water protection strategies for the upper and lower layer linkage factor result data to generate the upper and lower layer protection strategies for the reservoir, where the upper and lower layer protection strategies for the reservoir include the upper layer protection strategy for the reservoir area and the lower layer protection strategy for the reservoir area;

[0049] Step S422: When the upper and lower layer linkage factor result data is the result of serious upper layer loss, apply the upper layer protection strategy for the reservoir area to extract and analyze the soil type of the reservoir area environmental characteristic value data to generate the upper layer soil type data; screen the covering plants based on the upper layer soil type data to generate the upper layer reinforcement plant type data for the reservoir area, and send an alarm message, thereby completing the upper layer protection strategy for the reservoir area;

[0050] Step S423: When the result data of the upper and lower layer linkage factors is the result of severe lower layer penetration, apply the lower layer protection strategy for the reservoir area, screen the deep water-blocking material for the lower layer loss rate data to generate the lower layer water-blocking material data; generate the artificial water supply path for the high loss area in the lower layer reservoir area to generate the water supply path data for the high loss area in the lower layer, and send the disaster prevention and protection information of the lower layer water-blocking material data and the water supply path data for the high loss area in the lower layer, so as to complete the lower layer protection strategy for the reservoir area.

[0051] The present invention generates soil and water protection strategies based on the result data of the upper and lower layer linkage factors. The upper and lower layer protection strategies for the reservoir formed respectively target the upper and lower layer loss problems, thus realizing the comprehensive protection of the reservoir ecological environment. This two-level protection strategy not only improves the prevention and control ability of soil and water loss, but also can effectively cope with complex natural condition changes, laying a foundation for the sustainable management of the reservoir. When it is identified that the result data of the upper and lower layer linkage factors is the result of severe upper layer loss, immediately apply the upper layer protection strategy for the reservoir area, and generate the upper layer soil type data by extracting and analyzing the soil type of the environmental characteristic value data. The implementation of this process not only concretizes the soil characteristic data, but also provides a scientific basis for the subsequent screening of covering plants. Based on the generated upper layer soil type data, screening the covering plants can select the most suitable plant type for soil reinforcement, and this method effectively enhances the soil erosion resistance ability. In addition, the mechanism of sending alarm information ensures that when the loss risk increases, relevant managers can respond in time and take corresponding measures, further improving the real-time performance and flexibility of the protection strategy. When the result data of the upper and lower layer linkage factors is the result of severe lower layer penetration, apply the lower layer protection strategy for the reservoir area, screen the deep water-blocking material for the lower layer loss rate data to generate the lower layer water-blocking material data. This process not only scientifically selects the suitable water-blocking material based on the data, but also provides the necessary data support for the subsequent generation of the artificial water supply path. Through the water supply path generated based on the high loss area data, it ensures the effective replenishment of water resources in the reservoir area and enhances the overall benefit of soil and water conservation. Sending the disaster prevention and protection information of the lower layer water-blocking material data and the water supply path data forms a dynamic prevention and control mechanism for potential soil and water loss risks, ensuring the comprehensive monitoring and protection of the reservoir.

[0052] In this specification, a reservoir water resource information management system based on intelligent data is provided for executing the above-mentioned reservoir water resource information management method based on intelligent data. The reservoir water resource information management system based on intelligent data:

[0053] Upper-layer data acquisition and integration module: used to obtain the reservoir surface area dataset; perform data preprocessing on the reservoir surface area dataset to generate a preprocessed dataset for the reservoir surface area; perform discrete particle node processing on the preprocessed dataset for the reservoir surface area, and construct a loss impact factor database to generate an upper-layer loss impact factor database; construct a loss rate calculation model based on the upper-layer loss impact factor database to generate an upper-layer loss rate prediction model;

[0054] Lower-layer groundwater monitoring and analysis module: used to obtain historical groundwater level data, real-time groundwater level data, and surface permeability data; compare the historical groundwater level data and the real-time groundwater level data to generate groundwater level change data; draw a spatial distribution map by combining the groundwater level change data with the surface permeability data, and mark high-loss areas to generate a lower-layer reservoir area loss distribution map;

[0055] Upper and lower-layer loss relationship analysis module: used to perform in-depth particle loss relationship analysis on the upper-layer loss rate prediction model and the lower-layer reservoir area loss distribution map to generate upper and lower-layer loss relationship result data; perform linkage model association processing on the upper-layer loss rate prediction model based on the lower-layer reservoir area loss distribution map to generate an upper and lower-layer linkage model for the reservoir area;

[0056] Soil and water protection strategy generation module: used to generate soil and water protection strategies for the upper and lower-layer linkage model of the reservoir area based on the upper and lower-layer loss relationship result data to generate upper and lower-layer protection strategies for the reservoir, thus completing the intelligent data reservoir information management operation.

[0057] The beneficial effects of the present invention are as follows. By obtaining the data set of the reservoir surface area and performing discrete particle node processing, the generated upper-layer loss influence factor database provides a solid data foundation for the construction of the loss rate prediction model. This process ensures a comprehensive consideration of various factors affecting the surface loss of the reservoir, thereby improving the accuracy and reliability of the loss rate prediction. By comparing historical and real-time groundwater level data, groundwater level change data is generated, and a spatial distribution map is drawn in combination with the surface permeability, so as to clearly identify high-loss areas. This method makes the monitoring and evaluation of the loss phenomenon more intuitive and helps to formulate corresponding management measures in a timely manner. The in-depth particle analysis of the upper and lower layer loss relationships provides correlation data between the loss rate and reservoir management, enabling a comprehensive analysis of the water loss conditions at different levels. Through the construction of a linkage model, the quantification of the mutual influence between the upper and lower layer losses is realized, promoting the comprehensive management and optimization of water resources. Finally, the soil and water protection strategies generated based on the result data of the upper and lower layer loss relationships can specifically solve the problems in severely eroded areas. For example, vegetation restoration and cover layer reinforcement are carried out in areas with severe upper-layer loss, while deep water blocking or groundwater recharge measures are implemented in areas with severe lower-layer infiltration loss. Such strategies not only improve the protection effect of soil and water resources but also enhance the ecological stability of the reservoir. Therefore, the present invention solves the problems of information asymmetry and slow response in traditional water resource management by constructing an intelligent data reservoir information management system, and improves the scientificity and pertinence of reservoir water resource loss monitoring and protection measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 FIG. is a schematic flow chart of the steps of a method for managing reservoir water resource information based on intelligent data;

[0059] Figure 2 is Figure 1 a detailed implementation step flow chart of step S2 in;

[0060] Figure 3 is Figure 1 a detailed implementation step flow chart of step S3 in;

[0061] Figure 4 is Figure 1 a detailed implementation step flow chart of step S4 in;

[0062] The realization, functional characteristics, and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0063] The following clearly and completely describes the technical method of the present invention in combination with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative work belong to the scope of protection of the present invention.

[0064] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0065] It should be understood that although terms such as "first" and "second" may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0066] To achieve the above object, please refer to Figures 1 to 4 , a reservoir water resource information management method based on intelligent data, the method comprising the following steps:

[0067] Step S1: Obtain a reservoir surface area data set; perform data preprocessing on the reservoir surface area data set to generate a preprocessed reservoir surface area data set; perform discrete particle node processing on the preprocessed reservoir surface area data set and construct a loss impact factor database to generate an upper-layer loss impact factor database; construct a loss rate calculation model for the upper-layer loss impact factor database to generate an upper-layer loss rate prediction model;

[0068] Step S2: Obtain historical groundwater level data, real-time groundwater level data, and surface permeability data; compare the historical groundwater level data and the real-time groundwater level data to generate groundwater level change data; combine the groundwater level change data with the surface permeability data to draw a spatial distribution map and mark high-loss areas to generate a lower-layer reservoir area loss distribution map;

[0069] Step S3: Conduct a depth particle loss relationship analysis on the upper-layer loss rate prediction model and the lower-layer reservoir area loss distribution map to generate upper and lower layer loss relationship result data; perform linkage model association processing on the upper-layer loss rate prediction model based on the lower-layer reservoir area loss distribution map to generate an upper and lower layer linkage model for the reservoir area;

[0070] Step S4: Generate a soil and water protection strategy for the upper and lower layer linkage model of the reservoir area based on the upper and lower layer loss relationship result data to generate an upper and lower layer protection strategy for the reservoir, thereby completing the intelligent data reservoir information management operation.

[0071] The beneficial effects of the present invention are as follows. By obtaining the reservoir surface area data set and performing discrete particle node processing, the generated upper-layer loss impact factor database provides a solid data foundation for the construction of the loss rate prediction model. This process ensures a comprehensive consideration of various factors affecting the surface loss of the reservoir, thereby improving the accuracy and reliability of the loss rate prediction. By comparing historical and real-time groundwater level data, groundwater level change data is generated, and combined with the surface permeability to draw a spatial distribution map, thus clearly identifying high-loss areas. This method makes the monitoring and evaluation of the loss phenomenon more intuitive and helps to formulate corresponding management measures in a timely manner. The in-depth particle analysis of the upper and lower layer loss relationships provides correlation data between the loss rate and reservoir management, enabling a comprehensive analysis of the water loss conditions at different levels. Through the construction of the linkage model, the quantification of the mutual influence of upper and lower layer losses is achieved, promoting the comprehensive management and optimization of water resources. Finally, the soil and water protection strategy generated based on the upper and lower layer loss relationship result data can specifically solve the problems in severely eroded areas, such as implementing vegetation restoration and cover layer reinforcement in areas with severe upper-layer erosion, and implementing deep water blocking or groundwater recharge measures in areas with severe lower-layer infiltration erosion. Such a strategy not only improves the protection effect of soil and water resources but also enhances the ecological stability of the reservoir. Therefore, the present invention solves the problems of information asymmetry and slow response in traditional water resource management by constructing an intelligent data reservoir information management system, and improves the scientific nature and pertinence of reservoir water resource loss monitoring and protection measures.

[0072] In the embodiment of the present invention, with reference to Figure 1 as described, it is a schematic diagram of the step flow of the method for managing reservoir water resource information based on intelligent data. In this example, the method for managing reservoir water resource information based on intelligent data includes the following steps:

[0073] Step S1: Obtain the reservoir surface area dataset; perform data preprocessing on the reservoir surface area dataset to generate a preprocessed dataset of the reservoir surface area; perform discrete particle node processing on the preprocessed dataset of the reservoir surface area, and construct a database of erosion impact factors to generate an upper-layer erosion impact factor database; construct an erosion rate calculation model based on the upper-layer erosion impact factor database to generate an upper-layer erosion rate prediction model.

[0074] In the embodiment of the present invention, obtaining the reservoir surface area dataset is the first step in the data processing flow. This dataset contains multi-dimensional information such as terrain, soil type, precipitation, vegetation cover, and hydrological characteristics. The comprehensive analysis of this information is the basis for erosion impact factors. In the subsequent data preprocessing stage, data processing techniques such as standardization, noise reduction, and normalization are used to ensure the consistency and comparability of the data, thereby generating a preprocessed dataset of the reservoir surface area. This process is crucial as it provides reliable input data for subsequent analysis. Perform discrete particle node processing on the preprocessed dataset of the reservoir surface area. This step involves converting continuous geographical information into discrete particle nodes, which represent the soil erosion characteristics of specific geographical locations. Through methods such as spatial interpolation technology and cluster analysis, the spatial positions of the particle nodes can be accurately determined to better reflect the soil erosion situation in the region. Subsequently, based on the characteristics of these particle nodes, construct a database of erosion impact factors. Extract key factors affecting soil erosion, such as rainfall intensity, soil moisture, and terrain slope, through multi-factor analysis methods. The generation of this database provides the necessary variable basis for the calculation of the erosion rate. Construct an erosion rate calculation model based on the upper-layer erosion impact factor database, using a combination of statistical modeling and machine learning methods, such as regression analysis, random forest, or neural network, to achieve the prediction of the erosion rate in the reservoir area. By taking various impact factors as inputs, the model can learn the influence laws of different factors on the erosion rate, and then generate an upper-layer erosion rate prediction model.

[0075] Step S2: Obtain historical groundwater level data, real-time groundwater level data, and surface permeability data; compare the historical groundwater level data and the real-time groundwater level data to generate groundwater level change data; combine the groundwater level change data with the surface permeability data to draw a spatial distribution map and mark high-erosion areas to generate a lower-layer reservoir area erosion distribution map.

[0076] In the embodiments of the present invention, historical groundwater level data, real-time groundwater level data, and surface permeability data need to be obtained. These data are collected through an automatic monitoring system and hydrological stations to ensure the timeliness and accuracy of the data. The historical groundwater level data provides a time series background for analysis, while the real-time groundwater level data reflects the current dynamic changes in the water level. The surface permeability data is an important indicator for evaluating soil erosion and groundwater recharge capacity. After the data is obtained, a comparative analysis of the historical groundwater level data and the real-time groundwater level data is carried out, and time series analysis techniques, such as the moving average method and the exponential smoothing method, are used to extract the differences and change trends between the two. The groundwater level change data generated in this process can reflect the rise and fall of the groundwater level in different time periods, laying a foundation for subsequent erosion analysis. The generated groundwater level change data is combined with the surface permeability data, and spatial analysis techniques (such as the geographic information system GIS) are used to draw a spatial distribution map. In this process, the discrete monitoring point data is converted into a continuous spatial distribution map through an interpolation method (such as Kriging interpolation or inverse distance weighted interpolation), thereby generating a soil erosion distribution map of the lower reservoir area. The spatial distribution map can not only intuitively display the spatial relationship between the groundwater level change and the surface permeability, but also mark the high erosion areas through comparative analysis. These areas indicate serious soil erosion and need to be protected preferentially. The generated soil erosion distribution map of the lower reservoir area provides an important basis for the implementation of soil and water conservation measures.

[0077] Step S3: Conduct a deep particle erosion relationship analysis on the upper layer erosion rate prediction model and the soil erosion distribution map of the lower reservoir area to generate upper and lower layer erosion relationship result data; perform linkage model association processing on the upper layer erosion rate prediction model based on the soil erosion distribution map of the lower reservoir area to generate an upper and lower layer linkage model of the reservoir area;

[0078] In the embodiments of the present invention, a deep particle loss relationship analysis is performed on the upper-layer loss rate prediction model and the lower-layer reservoir area loss distribution map. This process involves using deep learning algorithms, especially a combined method based on particle swarm optimization (PSO) and convolutional neural network (CNN), to achieve feature extraction and correlation analysis in complex datasets. By comprehensively considering various loss factors included in the model (such as rainfall intensity, soil type, and vegetation coverage rate), the algorithm can capture the potential relationship between the upper-layer loss rate and the lower-layer loss situation, and then generate the upper and lower layer loss relationship result data. These result data not only include the change situation of the loss rate, but also can reflect the spatial distribution characteristics of the influencing factors. Based on the lower-layer reservoir area loss distribution map, a linkage model association process is performed on the upper-layer loss rate prediction model. The linkage model here uses algorithms such as multiple linear regression or support vector machine (SVM) to perform correlation analysis between the key features (such as high-loss areas and soil and water conservation conditions) in the lower-layer loss distribution map and the upper-layer loss rate. The purpose of this process is to explore the mutual influence and dynamic relationship between the two, so as to establish a more accurate upper and lower layer linkage model. The generation of the linkage model depends on cross-validation and data fusion technologies from multiple data sources, including but not limited to geographic information system (GIS), time series data analysis, and multi-dimensional statistical analysis, to ensure that the model has good prediction ability and reliability. The generated upper and lower layer linkage model of the reservoir area can not only provide a basis for future soil and water loss prediction, but also provide scientific guidance for reservoir management and decision support systems.

[0079] Step S4: Based on the upper and lower layer loss relationship result data, generate a soil and water conservation strategy for the upper and lower layer linkage model of the reservoir area, and generate the upper and lower layer protection strategy of the reservoir, thus completing the intelligent data reservoir information management operation.

[0080] In the embodiments of the present invention, based on the result data of the upper and lower layer loss relationship, it is first necessary to deeply analyze the upper and lower layer linkage model of the reservoir area to generate effective soil and water protection strategies. This process relies on the comprehensive utilization of various data analysis techniques, including decision tree algorithms, random forest models, and deep learning models, etc., to realize the intelligent generation of soil and water protection measures. By analyzing the upper and lower layer loss relationship data, areas with severe loss and potential risk points can be identified, and visualized through spatial data analysis techniques (such as Geographic Information System GIS) to more intuitively understand the dynamic of soil and water loss in the reservoir area. Based on the generated upper and lower layer linkage model, optimization algorithms (such as genetic algorithms or particle swarm optimization) are used to explore the effects of different soil and water protection strategies. This optimization process evaluates the potential of each strategy in reducing soil and water loss by simulating the implementation effects of different protection measures, such as vegetation restoration, soil reinforcement, building water retaining dams, etc. Through quantitative analysis of the implementation effects of each strategy, specific suggestions for the upper and lower layer protection strategies of the reservoir are generated, including corresponding measures for upper layer soil loss and lower layer infiltration problems.

[0081] Preferably, step S1 includes the following steps:

[0082] Step S11: Obtain the data set of the reservoir surface area; perform data preprocessing on the data set of the reservoir surface area to generate a preprocessed data set of the reservoir surface area;

[0083] Step S12: Perform discrete particle node processing on the preprocessed data set of the reservoir surface area to generate upper layer discrete particles of the reservoir area; perform coefficient calculation and analysis on the upper layer discrete particles of the reservoir area, and construct a loss impact factor database to generate an upper layer loss impact factor database;

[0084] Step S13: Construct a loss rate calculation model for the upper layer loss impact factor database to generate an upper layer loss rate prediction model.

[0085] In the embodiments of the present invention, a dataset of the reservoir surface area is obtained through means such as sensors, remote sensing technology, and on-site surveys. These data include multi-dimensional information such as soil type, vegetation cover, precipitation, and human activities. Next, data preprocessing is performed to improve the accuracy and usability of the data. This step involves data cleaning, missing value filling, and outlier handling. By applying statistical methods and data interpolation techniques, the integrity and consistency of the dataset are ensured, thereby generating a preprocessed dataset of the reservoir surface area. The preprocessed dataset is processed with discrete particle nodes to divide the reservoir area into several small units. This method helps to capture spatial heterogeneity and dynamic changes. Through spatial analysis techniques such as Kriging interpolation or grid analysis, upper-layer discrete particles in the reservoir area can be generated, and then the coefficient calculation and analysis of environmental characteristics and erosion potential are performed for each particle. This process uses regression analysis or machine learning methods to establish a database of erosion impact factors, generating an upper-layer database of erosion impact factors, which includes various factors affecting soil erosion such as rainfall intensity, slope, soil type, and vegetation coverage. The upper-layer database of erosion impact factors is applied to the construction of an erosion rate calculation model. By using models such as multiple linear regression, decision tree, or random forest, the relationship between multiple impact factors and the erosion rate is analyzed, thereby generating an upper-layer erosion rate prediction model.

[0086] Preferably, step S12 includes the following steps:

[0087] Step S121: Perform discrete particle node processing on the preprocessed dataset of the reservoir surface area to generate upper-layer discrete particles in the reservoir area;

[0088] Step S122: Extract spatial position eigenvalue data of the reservoir area by extracting spatial position eigenvalue data of the upper-layer discrete particles in the reservoir area; extract erosion potential eigenvalue data of the reservoir area by extracting erosion potential eigenvalue data of the upper-layer discrete particles in the reservoir area; extract environmental eigenvalue data of the reservoir area by extracting environmental eigenvalue data of the upper-layer discrete particles in the reservoir area;

[0089] Step S123: Perform rainfall erosion coefficient calculation and analysis on the spatial position eigenvalue data of the reservoir area and the environmental eigenvalue data of the reservoir area to generate a surface rainfall-erosion coefficient; perform wind speed erosion coefficient calculation and analysis on the spatial position eigenvalue data of the reservoir area and the erosion potential eigenvalue data of the reservoir area to generate a wind speed-erosion coefficient; perform slope soil coefficient calculation on the erosion potential eigenvalue data of the reservoir area and the environmental eigenvalue data of the reservoir area to generate a soil type-slope coefficient;

[0090] Step S124: Construct a database of erosion impact factors from the surface rainfall-erosion coefficient, wind speed-erosion coefficient, and soil type-slope coefficient to generate an upper-layer database of erosion impact factors.

[0091] In the embodiment of the present invention, by performing discrete particle node processing on the preprocessed data set of the reservoir surface area, the data set is transformed into a spatially analyzable particle form. This discretization process uses spatial analysis techniques to subdivide the entire area into multiple particle units, enabling more accurate subsequent analysis and capturing the spatial heterogeneity of the reservoir area. For the generated discrete particles in the upper layer of the reservoir area, through the extraction of spatial position eigenvalue, geometric and topological analysis methods are used to extract the spatial position characteristics of each particle, such as coordinates, adjacency relationships, etc. In addition, the extraction of the loss potential eigenvalue and the environmental eigenvalue is also carried out. These characteristics are determined by multivariate statistical analysis or machine learning methods (such as principal component analysis) to determine the loss potential and environmental impact corresponding to each particle, generating the loss potential eigenvalue data and environmental eigenvalue data of the reservoir area, which provides the basic data for the subsequent calculation of the loss coefficient. Combining the spatial position eigenvalue data and environmental eigenvalue data of the reservoir area, rainfall erosion coefficient calculation and analysis are carried out. This process uses a regression model or environmental simulation software to calculate the soil erosion potential under rainfall conditions and generate the surface rainfall-erosion coefficient. Combining the spatial position eigenvalue data and the loss potential eigenvalue data, the impact of wind speed on soil loss is calculated to generate the wind speed-loss coefficient. In addition, by combining the loss potential eigenvalue and the environmental eigenvalue, the slope soil coefficient calculation is carried out to extract the loss characteristics of different soil types under slope changes and generate the soil type-slope coefficient. Integrating the above three types of coefficients (rainfall-erosion coefficient, wind speed-loss coefficient, soil type-slope coefficient), a loss impact factor database is constructed to generate the upper layer loss impact factor database.

[0092] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0093] Step S21: Obtain historical groundwater level data, real-time groundwater level data, and surface permeability data; compare the historical groundwater level data and the real-time groundwater level data to generate groundwater level change data;

[0094] Step S22: Perform spatial processing on the surface permeability data to generate the lower layer loss rate data; perform coordinate mapping on the lower layer loss rate data to generate the lower layer groundwater infiltration distribution coordinate map;

[0095] Step S23: Draw a spatial distribution map of the groundwater level change data and the lower layer groundwater infiltration distribution coordinate map to generate the lower layer reservoir area loss distribution map.

[0096] In the embodiments of the present invention, data acquisition is achieved by integrating historical groundwater level data, real-time groundwater level data, and surface permeability data to construct a comprehensive dataset. Historical groundwater level data provides a time series background for the study, while real-time groundwater level data reflects the current state. The comparative analysis of the two can reveal the dynamic changes in the groundwater level, thereby generating groundwater level change data. This process uses statistical analysis tools to calculate the water level differences between different time points, and then evaluates the trend and amplitude of hydrological changes. The spatial processing of surface permeability data is a crucial step, involving spatial interpolation methods such as Kriging or inverse distance weighting to convert discrete permeability data into a continuous spatial distribution model, thereby generating underlying loss rate data. In addition, coordinate mapping of the underlying loss rate data is to combine spatial data with actual geographical coordinates. This process relies on Geographic Information System (GIS) technology to display the data in a spatial coordinate system and generate an underlying groundwater infiltration distribution coordinate map. The combination of groundwater level change data and the underlying groundwater infiltration distribution coordinate map is achieved through the drawing of a spatial distribution map, using GIS technology to achieve a visualization effect. This step uses spatial analysis tools, combines relevant datasets, and performs overlay analysis to generate an underlying reservoir area loss distribution map, which can clearly show the relationship between groundwater level changes and soil erosion.

[0097] Preferably, step S23 includes the following steps:

[0098] Step S231: Calculate the loss rate of the monitoring points based on the groundwater level change data and the surface permeability data to generate underlying monitoring point loss rate data;

[0099] Step S232: Use GIS technology to upload the underlying monitoring point loss rate data to the underlying groundwater infiltration distribution coordinate map for drawing a spatial infiltration distribution map, generating an underlying reservoir area loss distribution map;

[0100] Step S233: Mark the high-loss areas on the underlying reservoir area loss distribution map to generate underlying reservoir area high-loss area data.

[0101] In the embodiments of the present invention, it is crucial to calculate the loss rate of monitoring points based on the groundwater level change data and the surface permeability data. By using the loss rate calculation formula, the groundwater level change of the monitoring points is combined with the corresponding surface permeability to quantify the loss rate of each monitoring point. This process involves various statistical methods and calculation models. By setting different loss thresholds, the loss rate data of the lower-layer monitoring points is generated, which provides the basic data for subsequent analysis. Uploading the loss rate data of the lower-layer monitoring points to the lower-layer groundwater infiltration distribution coordinate map by using GIS technology and drawing the spatial infiltration distribution map is one of the core links of this process. The GIS system can effectively process and display spatial data. By using spatial analysis tools, the loss rate data is mapped to the corresponding geographical locations, thus generating the loss distribution map of the lower-layer reservoir area. This process includes spatial interpolation of data, generation of heat maps, and superposition of vector layers, ensuring that the generated loss distribution map is not only scientific but also can intuitively display the spatial characteristics of soil erosion. Marking the high-loss areas on the loss distribution map of the lower-layer reservoir area is an important link in applying the analysis results. By setting the threshold of the loss rate and using spatial analysis technology to automatically identify and mark the high-loss areas, this process includes clustering analysis or classification algorithms based on the distribution map to form the high-loss area data.

[0102] As an example of the present invention, with reference to Figure 3 shown, in this example, step S3 includes:

[0103] Step S31: Use factor analysis method to conduct in-depth particle loss relationship analysis on the upper-layer loss rate prediction model and the loss distribution map of the lower-layer reservoir area, and generate the upper and lower layer loss relationship result data;

[0104] Step S32: Identify the soil erosion relationship of the upper-layer loss rate prediction model based on the high-loss area data of the lower-layer reservoir area, and generate the different loss spatial characteristic data of the reservoir area;

[0105] Step S33: Use the different loss spatial characteristic data of the reservoir area and the loss distribution map of the lower-layer reservoir area to conduct in-depth particle loss relationship analysis on the upper-layer loss rate prediction model, and generate the upper and lower layer loss relationship result data;

[0106] Step S34: Conduct linkage model association processing on the upper-layer loss rate prediction model based on the loss distribution map of the lower-layer reservoir area, and generate the upper and lower layer linkage model of the reservoir area.

[0107] In the embodiments of the present invention, factor analysis is used to conduct in-depth analysis of the relationship between particle loss in the upper-layer loss rate prediction model and the lower-layer reservoir area loss distribution map. This process requires extracting the main factors affecting soil and water loss from multiple variables through statistical methods. Factor analysis can effectively reduce the dimensionality of data, identify potential factors affecting loss, and generate result data on the relationship between upper and lower-layer losses, providing a clear direction and basis for subsequent analysis. Based on the data of high-loss areas in the lower-layer reservoir area, the relationship between soil and water loss in the upper-layer loss rate prediction model is identified. In this process, first, the high-loss areas need to be accurately located, and then the characteristic data of these areas are combined with the upper-layer loss rate prediction model, and the relationship between the two is identified through comparative analysis. The generated different loss spatial characteristic data of the reservoir area will provide a scientific basis for subsequent management measures to help identify areas with higher loss risks under specific conditions. Once again, the data of different loss spatial characteristics in the reservoir area and the lower-layer reservoir area loss distribution map are used to conduct in-depth analysis of the relationship between particle loss in the upper-layer loss rate prediction model. This process is actually a further verification and deepening of the previous analysis results. By comparing and analyzing the impacts of different spatial characteristics, the loss relationship between the upper and lower layers is confirmed again. The generated new round of result data on the relationship between upper and lower-layer losses further improves the model, making it more accurate and reliable in practical applications. Based on the lower-layer reservoir area loss distribution map, linkage model association processing is performed on the upper-layer loss rate prediction model. The key to this process lies in using the spatial characteristics of the lower-layer loss distribution to associate the upper and lower-layer models through a linkage mechanism, enabling them to interact and feedback with each other, thereby generating an upper and lower-layer linkage model for the reservoir area.

[0108] As an example of the present invention, refer to Figure 4 shown, in this example, step S4 includes:

[0109] Step S41: Based on the result data of the relationship between upper and lower-layer losses, evaluate the loss potential of the upper and lower-layer linkage model for the reservoir area to generate upper and lower-layer loss potential evaluation data for the reservoir area; generate upper and lower-layer linkage factor result data from the upper and lower-layer loss potential evaluation data for the reservoir area, where the upper and lower-layer linkage factor result data includes the result of severe upper-layer loss and the result of severe lower-layer penetration;

[0110] Step S42: Generate soil and water protection strategies based on the upper and lower-layer linkage factor result data to generate upper and lower-layer protection strategies for the reservoir;

[0111] Step S43: Use the upper and lower-layer protection strategies for the reservoir to complete the reservoir information management operation.

[0112] In the embodiments of the present invention, through a variety of sensors and monitoring devices, environmental variables in the reservoir area are obtained in real time, such as soil humidity, rainfall, wind speed, groundwater level, etc. In the stage of erosion potential assessment, statistical analysis and machine learning algorithms are used to deeply analyze the collected multi-dimensional data, and decision tree or random forest methods are used to establish a prediction model for the upper and lower layer erosion relationships. This process aims to quantify the dynamic relationship between upper layer erosion and lower layer infiltration, and generate upper and lower layer erosion potential assessment data through calculation to identify erosion risk areas. Once the erosion potential assessment data is generated, the extraction and generation of upper and lower layer linkage factors can be further carried out. By analyzing the result data of the upper and lower layer erosion relationships, the key factors affecting soil and water loss in the reservoir area are identified, and linkage factor result data including the serious result of upper layer erosion and the serious result of lower layer infiltration is generated. In the strategy generation stage, by integrating the upper and lower layer linkage factor result data, corresponding soil and water protection measures are formulated using a decision support system. These measures include the selection of covering plants, soil improvement plans, rainwater management strategies, etc. Finally, relying on the generated upper and lower layer protection strategies of the reservoir, comprehensive management of reservoir information is realized.

[0113] Preferably, step S42 includes the following steps:

[0114] Step S421: Generate soil and water protection strategies for the upper and lower layer linkage factor result data to generate upper and lower layer protection strategies for the reservoir, where the upper and lower layer protection strategies for the reservoir include the upper layer protection strategy for the reservoir area and the lower layer protection strategy for the reservoir area;

[0115] Step S422: When the upper and lower layer linkage factor result data is the serious result of upper layer erosion, apply the upper layer protection strategy for the reservoir area, extract and analyze the soil type of the reservoir area environmental characteristic value data to generate upper layer soil type data; screen the covering plants based on the upper layer soil type data to generate upper layer reinforcement plant type data for the reservoir area, and send an alarm message, thereby completing the upper layer protection strategy for the reservoir area;

[0116] Step S423: When the upper and lower layer linkage factor result data is the serious result of lower layer infiltration, apply the lower layer protection strategy for the reservoir area, screen the deep water blocking materials for the lower layer erosion rate data to generate lower layer water blocking material data; generate the artificial water supply path for the high erosion area in the lower layer reservoir area to generate the water supply path data for the high erosion area in the lower layer, and send the lower layer water blocking material data and the water supply path data for the high erosion area in the lower layer for disaster prevention information, thereby completing the lower layer protection strategy for the reservoir area.

[0117] In the embodiments of the present invention, through the comprehensive analysis of the result data of the upper and lower layer linkage factors, a protection strategy for the upper and lower layers of the reservoir is generated. This process adopts multi-factor analysis and a decision support system to ensure the scientific nature and pertinence of the protection strategy. The formulation of the upper layer protection strategy and the lower layer protection strategy for the reservoir area is based on in-depth research on hydrometeorological data, soil characteristic data, and historical erosion conditions, enabling the protection measures to effectively respond to erosion risks at different levels. When the result data of the upper and lower layer linkage factors indicates severe upper layer erosion, the upper layer protection strategy for the reservoir area is implemented. At this time, the soil type extraction and analysis are carried out on the environmental characteristic value data of the reservoir area to generate upper layer soil type data. This process mainly relies on geographic information system (GIS) technology and soil classification standards. Subsequently, based on the extracted soil type data, the screening of covering plants is carried out to generate the upper layer reinforcement plant type data for the reservoir area. This step combines ecological principles and plant physiological characteristics to select plants suitable for local environmental conditions and soil types for covering. At the same time, the system also has a real-time monitoring function, which can send alarm information in a timely manner to notify relevant management personnel to take necessary measures to ensure the implementation of the upper layer protection strategy in place. When the result data of the upper and lower layer linkage factors shows severe lower layer seepage, the lower layer protection strategy for the reservoir area is adopted. In this process, through the analysis of the lower layer erosion rate data, the screening of deep water retaining materials is carried out to generate lower layer water retaining material data. This step utilizes materials science and engineering technology to select suitable water retaining materials to reduce soil erosion. In addition, based on the high erosion area data of the lower layer reservoir area, the generation of the artificial water replenishment path is carried out to ensure that the high erosion area receives timely water resource replenishment. Finally, the lower layer water retaining material data and the water replenishment path data are integrated, and disaster prevention information is sent to form a closed-loop management.

[0118] In this specification, a reservoir water resource information management system based on intelligent data is provided for implementing the above-mentioned reservoir water resource information management method based on intelligent data. The reservoir water resource information management system based on intelligent data:

[0119] Upper layer data acquisition and integration module: used to obtain the data set of the reservoir surface area; perform data preprocessing on the data set of the reservoir surface area to generate a preprocessed data set of the reservoir surface area; perform discrete particle node processing on the preprocessed data set of the reservoir surface area, and construct a database of erosion impact factors to generate an upper layer erosion impact factor database; construct an erosion rate calculation model for the upper layer erosion impact factor database to generate an upper layer erosion rate prediction model;

[0120] Lower groundwater monitoring and analysis module: used to obtain historical groundwater level data, real-time groundwater level data, and surface permeability data; compare the historical groundwater level data and real-time groundwater level data to generate groundwater level change data; combine the groundwater level change data with the surface permeability data to draw a spatial distribution map and mark high-loss areas to generate a loss distribution map of the lower reservoir area;

[0121] Upper and lower layer loss relationship analysis module: used to conduct in-depth particle loss relationship analysis on the upper layer loss rate prediction model and the loss distribution map of the lower reservoir area to generate upper and lower layer loss relationship result data; perform linkage model association processing on the upper layer loss rate prediction model based on the loss distribution map of the lower reservoir area to generate an upper and lower layer linkage model for the reservoir area;

[0122] Soil and water protection strategy generation module: used to generate soil and water protection strategies for the upper and lower layer linkage model of the reservoir area based on the upper and lower layer loss relationship result data to generate upper and lower layer protection strategies for the reservoir, thereby completing the intelligent data reservoir water resource information management operation.

[0123] The beneficial effects of the present invention are as follows. By obtaining the data set of the reservoir surface area and performing discrete particle node processing, the generated upper layer loss impact factor database provides a solid data foundation for the construction of the loss rate prediction model. This process ensures a comprehensive consideration of various factors affecting the loss of the reservoir surface layer, thereby improving the accuracy and reliability of the loss rate prediction. By comparing the historical and real-time groundwater level data, groundwater level change data is generated, and a spatial distribution map is drawn in combination with the surface permeability, thus clearly identifying high-loss areas. This method makes the monitoring and evaluation of the loss phenomenon more intuitive and helps to formulate corresponding management measures in a timely manner. The in-depth particle analysis of the upper and lower layer loss relationship provides correlation data between the loss rate and reservoir management, enabling a comprehensive analysis of the water loss conditions at different levels. Through the construction of the linkage model, the quantification of the mutual influence of the upper and lower layer losses is realized, promoting the comprehensive management and optimization of water resources. Finally, the soil and water protection strategies generated based on the upper and lower layer loss relationship result data can specifically solve the problems in areas with serious losses. For example, in areas with serious upper layer losses, vegetation restoration and cover layer reinforcement are carried out, while in areas with serious lower layer infiltration losses, deep water blocking or groundwater recharge measures are implemented. Such strategies not only improve the protection effect of soil and water resources but also enhance the ecological stability of the reservoir. Therefore, the present invention solves the problems of information asymmetry and slow response in traditional water resource management by constructing an intelligent data reservoir information management system, and improves the scientificity and pertinence of the reservoir water resource loss monitoring and protection measures.

[0124] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to encompass all changes that fall within the meaning and scope of the equivalent elements of the application documents within the present invention.

[0125] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A reservoir water resource information management method based on intelligent data, characterized in that, It includes the following steps: Step S1: Obtain the reservoir surface area dataset; perform data preprocessing on the reservoir surface area dataset to generate a preprocessed reservoir surface area dataset; perform discrete particle node processing on the preprocessed reservoir surface area dataset and construct a loss impact factor database to generate an upper-layer loss impact factor database; Construct a loss rate calculation model for the upper-layer loss impact factor database to generate an upper-layer loss rate prediction model; Step S2: Obtain historical groundwater level data, real-time groundwater level data, and surface permeability data; compare the historical groundwater level data and the real-time groundwater level data to generate groundwater level change data; combine the groundwater level change data with the surface permeability data to draw a spatial distribution map and mark high-loss areas to generate a lower-layer reservoir area loss distribution map; Step S3: Conduct in-depth particle loss relationship analysis on the upper-layer loss rate prediction model and the lower-layer reservoir area loss distribution map to generate upper and lower layer loss relationship result data; perform linkage model association processing on the upper-layer loss rate prediction model based on the lower-layer reservoir area loss distribution map to generate an upper and lower layer linkage model for the reservoir area; Step S4: Generate a water and soil protection strategy for the upper and lower layer linkage model of the reservoir area based on the upper and lower layer loss relationship result data to generate an upper and lower layer protection strategy for the reservoir, thereby completing the intelligent data reservoir water resource information management operation.

2. The method for managing reservoir water resource information based on intelligent data according to claim 1, wherein Step S1 includes the following steps: Step S11: Obtain the reservoir surface area dataset; perform data preprocessing on the reservoir surface area dataset to generate a preprocessed reservoir surface area dataset; Step S12: Perform discrete particle node processing on the preprocessed reservoir surface area dataset to generate upper-layer discrete particles in the reservoir area; perform coefficient calculation and analysis on the upper-layer discrete particles in the reservoir area and construct a loss impact factor database to generate an upper-layer loss impact factor database; Step S13: Construct a loss rate calculation model for the upper-layer loss impact factor database to generate an upper-layer loss rate prediction model.

3. The method for managing reservoir water resource information based on intelligent data according to claim 2, wherein, Step S12 includes the following steps: Step S121: Perform discrete particle node processing on the preprocessed reservoir surface area dataset to generate upper-layer discrete particles in the reservoir area; Step S122: Extract spatial position eigenvalue data for the upper-layer discrete particles in the reservoir area to generate reservoir area spatial position eigenvalue data; extract loss potential eigenvalue data for the upper-layer discrete particles in the reservoir area to generate reservoir area loss potential eigenvalue data; extract environmental eigenvalue data for the upper-layer discrete particles in the reservoir area to generate reservoir area environmental eigenvalue data; Step S123: Perform rainfall erosion coefficient calculation and analysis on the reservoir area spatial position eigenvalue data and the reservoir area environmental eigenvalue data to generate surface rainfall-erosion coefficient; perform wind speed loss coefficient calculation and analysis on the reservoir area spatial position eigenvalue data and the reservoir area loss potential eigenvalue data to generate wind speed-loss coefficient; perform slope soil coefficient calculation on the reservoir area loss potential eigenvalue data and the reservoir area environmental eigenvalue data to generate soil type-slope coefficient; Step S124: Construct a loss impact factor database for surface rainfall - erosion coefficient, wind speed - loss coefficient, and soil type - slope coefficient, and generate an upper - layer loss impact factor database.

4. The method for managing reservoir water resource information based on intelligent data according to claim 2, wherein, Step S13 includes the following steps: Step S131: Divide the preset upper - layer loss rate model into three layers to generate a three - layer loss rate model for the reservoir area, where the three - layer loss rate model for the reservoir area includes the first layer of the reservoir area, the second layer of the reservoir area, and the third layer of the reservoir area; Step S132: Calculate the rainfall erosion impact of the surface rainfall - erosion coefficient on the first layer of the reservoir area to generate the first - layer loss rate of the reservoir area; Quantify the wind erosion impact of the surface rainfall - erosion coefficient on the first layer of the reservoir area to generate the second - layer loss rate of the reservoir area; Calculate the soil slope adaptability of the soil type - slope coefficient to the third layer of the reservoir area to generate the third - layer loss rate of the reservoir area Step S133: Assign multi - layer weight values to the first - layer loss rate of the reservoir area, the second - layer loss rate of the reservoir area, and the third - layer loss rate of the reservoir area, and perform model recombination to generate an upper - layer loss rate prediction model.

5. The method for managing reservoir water resource information based on intelligent data according to claim 1, characterized in that Step S2 includes the following steps: Step S21: Obtain historical groundwater level data, real - time groundwater level data, and surface permeability data; Compare the historical groundwater level data and the real - time groundwater level data to generate groundwater level change data; Step S22: Spatialize the surface permeability data to generate lower - layer loss rate data; Map the coordinates of the lower - layer loss rate data to generate a lower - layer groundwater infiltration distribution coordinate map; Step S23: Draw a spatial distribution map of the groundwater level change data and the lower - layer groundwater infiltration distribution coordinate map to generate a lower - layer reservoir area loss distribution map.

6. The method for managing reservoir water resource information based on intelligent data according to claim 5, characterized in that, Step S23 includes the following steps: Step S231: Calculate the loss rate of monitoring points based on the groundwater level change data and the surface permeability data to generate lower - layer monitoring point loss rate data; Step S232: Use GIS technology to upload the lower - layer monitoring point loss rate data to the lower - layer groundwater infiltration distribution coordinate map for drawing a spatial infiltration distribution map, and generate a lower - layer reservoir area loss distribution map; Step S233: Mark the high - loss areas on the lower - layer reservoir area loss distribution map to generate lower - layer reservoir area high - loss area data.

7. The reservoir water resource information management method based on intelligent data according to claim 1, characterized in that Step S3 includes the following steps: Step S31: Use factor analysis to conduct in - depth particle loss relationship analysis on the upper - layer loss rate prediction model and the lower - layer reservoir area loss distribution map to generate upper - and lower - layer loss relationship result data; Step S32: Identify the soil erosion relationship of the upper - layer loss rate prediction model based on the lower - layer reservoir area high - loss area data to generate different loss spatial feature data for the reservoir area; Step S33: Use the different loss spatial feature data of the reservoir area and the lower - layer reservoir area loss distribution map to conduct in - depth particle loss relationship analysis on the upper - layer loss rate prediction model to generate upper - and lower - layer loss relationship result data; Step S34: Perform linkage model association processing on the upper - layer loss rate prediction model based on the lower - layer reservoir area loss distribution map to generate an upper - and lower - layer linkage model for the reservoir area.

8. The method for managing reservoir water resource information based on intelligent data according to claim 1, wherein Step S4 includes the following steps: Step S41: Based on the upper and lower layer loss relationship result data, conduct a loss potential assessment on the upper and lower layer linkage model of the reservoir area to generate upper and lower layer loss potential assessment data of the reservoir area; generate upper and lower layer linkage factor result data through the upper and lower layer loss potential assessment data of the reservoir area, where the upper and lower layer linkage factor result data includes the upper layer severe loss result and the lower layer severe infiltration result; Step S42: Generate a soil and water protection strategy based on the upper and lower layer linkage factor result data to generate a reservoir upper and lower layer protection strategy; Step S42: Utilize the reservoir upper and lower layer protection strategy to complete the reservoir information management operation.

9. The method for managing reservoir water resource information based on intelligent data according to claim 8, characterized in that, Step S42 includes the following steps: Step S421: Generate a soil and water protection strategy based on the upper and lower layer linkage factor result data to generate a reservoir upper and lower layer protection strategy, where the reservoir upper and lower layer protection strategy includes the upper layer protection strategy of the reservoir area and the lower layer protection strategy of the reservoir area; Step S422: When the upper and lower layer linkage factor result data is the upper layer severe loss result, apply the upper layer protection strategy of the reservoir area, extract and analyze the soil type from the reservoir area environmental characteristic value data to generate upper layer soil type data; screen the covering plants based on the upper layer soil type data to generate the upper layer reinforcement plant type data of the reservoir area, and send an alarm message to complete the upper layer protection strategy of the reservoir area; Step S423: When the upper and lower layer linkage factor result data is the lower layer severe infiltration result, apply the lower layer protection strategy of the reservoir area, screen the deep water retaining materials from the lower layer loss rate data to generate lower layer water retaining material data; generate the artificial water replenishment path data for the lower layer high loss area based on the lower layer reservoir area high loss area data, and send the disaster prevention information with the lower layer water retaining material data and the lower layer high loss area water replenishment path data to complete the lower layer protection strategy of the reservoir area.

10. A reservoir water resource information management system based on intelligent data, characterized in that, A reservoir water resource information management system based on intelligent data for implementing the method of claim 1, the reservoir water resource information management system based on intelligent data includes: Upper layer data collection and integration module: used to obtain the reservoir surface area data set; preprocess the reservoir surface area data set to generate a preprocessed data set of the reservoir surface area; conduct discrete particle node processing on the preprocessed data set of the reservoir surface area, and construct a loss impact factor database to generate an upper layer loss impact factor database; construct a loss rate calculation model based on the upper layer loss impact factor database to generate an upper layer loss rate prediction model; Lower layer groundwater monitoring and analysis module: used to obtain historical groundwater level data, real-time groundwater level data, and surface permeability data; compare the historical groundwater level data and the real-time groundwater level data to generate groundwater level change data; draw a spatial distribution map by combining the groundwater level change data with the surface permeability data, and mark the high loss areas to generate a lower layer reservoir area loss distribution map; Upper and lower layer loss relationship analysis module: It is used to deeply analyze the particle loss relationship between the upper layer loss rate prediction model and the lower layer reservoir area loss distribution map, and generate the upper and lower layer loss relationship result data; based on the lower layer reservoir area loss distribution map, perform linkage model association processing on the upper layer loss rate prediction model to generate the upper and lower layer linkage model of the reservoir area; Soil and water protection strategy generation module: It is used to generate soil and water protection strategies for the upper and lower layer linkage model of the reservoir area based on the upper and lower layer loss relationship result data, and generate the upper and lower layer protection strategies of the reservoir, so as to complete the intelligent data reservoir water resource information management operation.

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