Method and device for determining water surface ratio configuration scheme

Abstract

The present invention relates to the technical field of water surface ratio configuration, and specifically to a method and device for determining a water surface ratio configuration scheme. Acquire regional meteorological data, hydrological data, water quality data, and underlying surface factor data corresponding to the target water network area; input the regional meteorological data, hydrological data, water quality data, and underlying surface factor data into a preset water surface ratio configuration scheme model, and output a target water surface ratio configuration scheme corresponding to the target water network area; configure the water surface ratio corresponding to the target water network area based on the target water surface ratio configuration scheme. A scientific and reasonable water surface ratio configuration scheme helps to balance the supply and demand relationship of water resources and ensure the economic development and ecological stability of the target water network area. By optimizing the water surface ratio configuration, it can not only meet the current demand for water resources, but also protect water resources and the ecological environment, lay the foundation for the long-term sustainable development of the region, and achieve coordinated development of the economy, society, and environment. A reasonable water surface ratio configuration is achieved for the target water network area.

Description

Technical Field

[0001] The present invention relates to the technical field of water surface rate configuration, and in particular to a method and device for determining a water surface rate configuration scheme. Background Art

[0002] Water networks are widely distributed around the world and serve as the core vehicles for water storage, allocation, and ecological services. In cities, water networks not only supply water for domestic and industrial use but also play a vital role in regulating urban microclimates and beautifying the environment. In rural areas and natural ecosystems, water networks support agricultural irrigation, maintain rich biodiversity, and are crucial for ecological balance. However, with global population growth, accelerated urbanization, and the impact of climate change, water networks face numerous challenges, including water shortages, the threat of flooding, and ecological degradation.

[0003] However, traditional water surface ratio configuration technology relies primarily on empirical formulas and simple model simulations, making it difficult to accurately analyze and predict complex water network structures and diverse boundary conditions. The lack of support from advanced information technology and data analysis methods leads to significant limitations in data collection, processing, and solution optimization, making it unable to meet the high-precision requirements of modern water network management.

[0004] In summary, how to configure the water surface ratio in the water network area in a scientific, reasonable, comprehensive and dynamically adaptable manner has become a key issue that needs to be urgently addressed in the current fields of water conservancy engineering, ecological environment and urban planning. It has extremely important practical significance for ensuring the sustainable use of water resources, maintaining ecological security and promoting sustainable social and economic development. Summary of the Invention

[0005] In view of this, the present invention provides a method and device for determining a water surface ratio configuration scheme to solve the problem of how to configure the water surface ratio of a water network area in a scientific, reasonable, comprehensive, and dynamically adaptable manner.

[0006] In a first aspect, the present invention provides a method for determining a water surface rate configuration scheme, the method comprising:

[0007] Acquire regional meteorological data, hydrological data, water quality data, and underlying surface factor data corresponding to the target water network area; the underlying surface factor data includes at least one of regional topographic data, regional soil type and property data, and regional vegetation type and coverage data;

[0008] Input regional meteorological data, hydrological data, water quality data and underlying surface factor data into the preset water surface ratio configuration scheme model, and output the target water surface ratio configuration scheme corresponding to the target water network area;

[0009] The water surface rate corresponding to the target water network area is configured based on the target water surface rate configuration plan.

[0010] The method for determining a water surface ratio configuration scheme provided in the embodiments of the present application obtains regional meteorological data, hydrological data, water quality data, and underlying surface factor data corresponding to the target water network area, providing a comprehensive and accurate basis for the water surface ratio configuration scheme. This makes the target water surface ratio configuration scheme output by the preset water surface ratio configuration scheme model more consistent with actual conditions, thereby improving the scientific nature and accuracy of the configuration scheme. Then, the regional meteorological data, hydrological data, water quality data, and underlying surface factor data are input into the preset water surface ratio configuration scheme model, and the target water surface ratio configuration scheme corresponding to the target water network area is output, ensuring the accuracy and scientific nature of the target water surface ratio configuration scheme outputted for the target water network area. The water surface ratio corresponding to the target water network area is configured based on the target water surface ratio configuration scheme. A scientific and reasonable water surface ratio configuration scheme helps balance the supply and demand of water resources and ensure the economic development and ecological stability of the target water network area. By optimizing the water surface ratio configuration, it can not only meet the current demand for water resources, but also protect water resources and the ecological environment, laying the foundation for the long-term sustainable development of the region and achieving coordinated development of the economy, society, and environment. A reasonable water surface ratio configuration is achieved for the target water network area.

[0011] In an optional embodiment, the preset water surface ratio configuration scheme model includes a feature extraction model and a water surface ratio configuration scheme model. Regional meteorological data, hydrological data, water quality data, and underlying surface factor data are input into the preset water surface ratio configuration scheme model, and a target water surface ratio configuration scheme corresponding to the target water network area is output, including:

[0012] Input regional meteorological data, hydrological data, water quality data and underlying surface factor data into the feature extraction model;

[0013] The feature extraction model identifies the input data and determines the data types of various data in the input data; the input data are regional meteorological data, hydrological data, water quality data and underlying surface factor data;

[0014] Set up multi-channel input according to the data type of various data;

[0015] Based on each input channel, feature extraction is performed on various types of input data, and feature representation is output;

[0016] The feature representation is input into the water surface ratio configuration scheme model, and the target water surface ratio configuration scheme corresponding to the target water network area is output based on the feature representation.

[0017] The method for determining a water surface ratio configuration scheme provided in an embodiment of the present application inputs regional meteorological data, hydrological data, water quality data, and underlying surface factor data into a feature extraction model. The feature extraction model identifies the input data and determines the data types of various data within the input data, ensuring the accuracy of the data types determined. Multi-channel input is then configured based on the data types of the various data types. This allows the feature extraction model to perform targeted processing based on the characteristics of different data types. Multi-channel input allows the feature extraction model to adopt processing methods appropriate for each data type, avoiding the loss of key information due to uniform processing, ensuring effective data utilization, and laying the foundation for subsequent accurate feature extraction. The feature extraction model then extracts features from each input data type based on each input channel and outputs feature representations. This ensures the comprehensiveness and accuracy of the feature representations. These rich feature representations more comprehensively reflect the actual conditions of the target water network area, providing the water surface ratio configuration scheme model with more detailed and accurate information, thereby improving the reliability of the scheme. The feature representations are then input into the water surface ratio configuration scheme model, which outputs a target water surface ratio configuration scheme corresponding to the target water network area based on the feature representations. The accuracy of the target water surface rate configuration plan corresponding to the output target water network area is guaranteed.

[0018] In an optional embodiment, based on each input channel, feature extraction is performed on various types of input data, and a feature representation is output, including:

[0019] The image data in the input data is input as image channel;

[0020] For the numerical data in the input data, convert the numerical data into a two-dimensional matrix form and allocate channels according to the feature dimension;

[0021] Use multiple convolution kernels of different sizes to perform convolution processing on the data input from each input channel to extract target features of different scales;

[0022] The target features extracted from different levels and types of data are fused according to the channel dimension to generate feature representation.

[0023] The method for determining the water surface ratio configuration scheme provided in the embodiment of the present application directly uses the image data as the image channel input, which completely retains the spatial information and visual features of the image. Converting the numerical data into a two-dimensional matrix and assigning channels according to the feature dimension can structure the abstract numerical information, which is convenient for model recognition and processing. Then, multiple convolution kernels of different sizes are used to perform convolution processing on the input channel data, which can extract target features of different scales. Small-sized convolution kernels focus on local details. When processing image data, they can accurately identify subtle features such as the edge of the water body and small ditches; large-sized convolution kernels focus on global information and grasp the macro layout and structure of the entire water network. This multi-scale feature extraction method comprehensively covers various feature information of the data and enhances the depth and breadth of understanding of the data. Finally, the target features extracted from different levels and types of data are fused according to the channel dimension, and the generated feature representation integrates the advantages of multi-source data. The spatial features of image data and the quantitative features of numerical data complement each other, providing richer and more comprehensive information for the model. When determining the water surface ratio configuration plan, the fused features can reflect both the actual geographical form of the water network and the changes in related hydrological, meteorological and other data, making the water surface ratio configuration plan model decision more scientific and accurate. In addition, the rich and comprehensive feature representation helps improve the performance of the water surface ratio configuration plan model in subsequent tasks. The water surface ratio configuration plan model analyzes based on the fused feature representation, which can more accurately capture the complex relationships and underlying patterns between data, improving prediction accuracy and evaluation reliability. At the same time, the introduction of multi-scale features enhances the model's adaptability to different situations, allowing it to maintain good performance when faced with complex and changing data.

[0024] In an optional embodiment, the water surface ratio configuration scheme model includes multiple sub-water surface ratio configuration scheme models of different algorithms, the feature representation is input into the water surface ratio configuration scheme model, and a target water surface ratio configuration scheme corresponding to the target water network area is output based on the feature representation, including:

[0025] Input the characteristic representation into each sub-water surface rate configuration scheme model respectively;

[0026] Each sub-water surface rate configuration scheme model identifies the feature representation and outputs a plurality of first candidate water surface rate configuration schemes respectively;

[0027] fusing the first candidate water surface rate configuration schemes to generate a plurality of first fused water surface rate configuration schemes;

[0028] Input each first fused water surface rate configuration scheme into each sub-water surface rate configuration scheme model respectively;

[0029] Each sub-water surface rate configuration scheme model evaluates and updates each first fused water surface rate configuration scheme, and outputs multiple second candidate water surface rate configuration schemes;

[0030] This cycle is repeated until the preset number of times, and each sub-water surface rate configuration scheme model outputs its corresponding current optimal water surface rate configuration scheme;

[0031] Identify the current optimal water surface rate configuration schemes and output the target water surface rate configuration scheme corresponding to the target water network area.

[0032] The present invention provides a method for determining a water surface ratio configuration scheme. The water surface ratio configuration scheme model comprises multiple sub-water surface ratio configuration scheme models based on different algorithms. Each sub-water surface ratio configuration scheme model processes feature representations based on different algorithmic principles and optimization strategies. Multiple algorithms are run in parallel to explore the possibilities of water surface ratio configuration from different perspectives. The multiple first candidate water surface ratio configuration schemes output are highly diverse and, when combined, can more comprehensively cover various possible configuration scenarios, avoiding the limitations of a single algorithm and laying the foundation for subsequently generating a more optimal solution. Then, the multiple first candidate water surface ratio configuration schemes are fused to generate a first fused water surface ratio configuration scheme. This fusion process integrates the advantages of different first candidate water surface ratio configuration schemes. This fusion is not a simple superposition, but rather, through specific rules and algorithms, fully utilizes the strengths of each scheme, compensates for its weaknesses, and improves the overall performance of the scheme, which is more in line with the requirements of multi-objective optimization of water surface ratio configuration in practical applications. Next, the first fused water surface ratio configuration scheme is re-input into each sub-water surface ratio configuration scheme model for evaluation and update, outputting a second candidate water surface ratio configuration scheme, and continuously repeating this process. Each iteration optimizes and improves the previous solution. The sub-water surface ratio configuration scheme model reevaluates and adjusts the fusion scheme based on its own algorithm, gradually exploring the potential of the scheme and continuously moving it closer to the optimal solution. As the number of iterations increases, the target water surface ratio configuration scheme performs increasingly well in meeting multiple objectives such as flood control, water resource utilization, and ecological protection, thereby improving the accuracy and effectiveness of the water surface ratio configuration scheme. During the iteration process, each sub-water surface ratio configuration scheme model continuously adapts to the newly input fusion scheme, evaluating and adjusting it for different situations. This enables the entire model system to cope with complex and changing water network environments and data characteristics. Even in the face of data uncertainty and changes in the actual conditions of the water network area, the water surface ratio configuration scheme model can output a relatively stable and effective target water surface ratio configuration scheme through multiple iterations and adjustments. This enhances the adaptability and robustness of the water surface ratio configuration scheme model and ensures that it can provide a reliable target water surface ratio configuration scheme in different scenarios.

[0033] In an optional embodiment, each sub-water surface ratio configuration scheme model identifies the feature representation and outputs a plurality of first candidate water surface ratio configuration schemes, including:

[0034] For one of the sub-water surface rate configuration scheme models, the sub-water surface rate configuration scheme model identifies the feature representation;

[0035] Generate multiple initial water surface rate configuration plans based on the target constraint conditions in the sub-water surface rate configuration plan model; the target constraint conditions include at least one of the water surface rate range constraint, water quality standard constraint, water level and flood storage capacity constraint, and land use constraint;

[0036] Based on the characteristic representation, each initial water surface ratio configuration scheme is substituted into the objective function of the sub-water surface ratio configuration scheme model to calculate the objective function value corresponding to each initial water surface ratio configuration scheme; wherein the objective function includes at least one of the flood control and drainage sub-function, the water resource utilization sub-function, and the water environment capacity sub-function;

[0037] According to the objective function values corresponding to the initial water surface rate configuration schemes, the historical optimal water surface rate configuration scheme and the current global optimal water surface rate configuration scheme are determined;

[0038] Determine the update speed based on the historical optimal water surface rate configuration plan and the current global optimal water surface rate configuration plan;

[0039] According to the update speed, each updated water surface rate configuration scheme is obtained; each updated water surface rate configuration scheme satisfies the target constraint;

[0040] Calculate the objective function value corresponding to each updated water surface rate configuration scheme;

[0041] Determine the updated water surface rate configuration scheme whose objective function value is greater than the preset function value as the first candidate water surface rate configuration scheme;

[0042] If there is no updated water surface ratio configuration scheme whose objective function value is greater than the preset function value, the updated water surface ratio configuration scheme will continue to be updated until the updated water surface ratio configuration scheme whose objective function value is greater than the preset function value is determined as the first candidate water surface ratio configuration scheme.

[0043] The method for determining a water surface ratio configuration scheme provided in an embodiment of the present application generates an initial water surface ratio configuration scheme based on target constraints, fundamentally ensuring the feasibility of the scheme in practical applications. This avoids the situation where the generated scheme cannot be implemented due to violation of actual constraints. Based on feature representation, each initial water surface ratio configuration scheme is substituted into the objective function of the sub-water surface ratio configuration scheme model, and the objective function value corresponding to each initial water surface ratio configuration scheme is calculated, thereby enabling the advantages and disadvantages of each initial water surface ratio configuration scheme to be evaluated from multiple perspectives. When evaluating a scheme, not only can its effectiveness in flood control and drainage be understood, but also its impact on water resource utilization and water environment capacity can be understood, thereby achieving multi-objective optimization of the water surface ratio configuration scheme, making the generated scheme more in line with the comprehensive needs of sustainable development in the water network area. Then, based on the objective function value, the historical optimal and current global optimal water surface ratio configuration schemes are determined, and the update speed is determined accordingly, and the updated configuration schemes are continuously obtained. The updated scheme with an objective function value greater than the preset function value is determined as the first candidate water surface ratio configuration scheme. This screening mechanism can gradually eliminate undesirable schemes and retain and optimize more advantageous schemes. If there is no solution that meets the preset conditions, it will be continuously updated to ensure that the final first candidate water surface ratio configuration solution performs well under the multi-objective comprehensive evaluation, thereby improving the quality and practicality of the first candidate water surface ratio configuration solution. In the process of continuous updating and screening, the first candidate water surface ratio configuration solution can better adapt to the complex situation of the water network area. Since each update is based on the previous optimal solution and takes into account the comprehensive influence of multiple objective functions, the generated solution can more accurately meet the actual needs. For a water network area that has both flood control needs and water resource shortages, this mechanism can find a first candidate water surface ratio configuration solution that achieves a good balance between flood control and water resource utilization through multiple iterative optimizations.

[0044] In an optional embodiment, the target water network area includes multiple water bodies, and the objective function includes:

[0045] in, is the flood control and drainage sub-function; The water body in the target water network area i area; For water bodies The flood storage capacity is as follows: ;in For water bodies The water level, is the flood storage coefficient;

[0046] and / or,

[0047]

[0048] in, is the water resource utilization sub-function, For water bodies The available water volume is calculated as follows: ; Where P is the water body The precipitation of water body The evaporation rate is , β is the regional hydrological conversion coefficient; For water bodies Water use efficiency is defined as: ;in, For agricultural irrigation needs;

[0049] and / or,

[0050]

[0051] in, is the water environment capacity subfunction; Pi is the water body The pollution load is usually related to land use and emissions; Vi is the water The environmental capacity is calculated as follows: ; Among them, γ is the comprehensive degradation coefficient, which is related to the water The dilution capacity of is the area of water body i, is the water level of water body i, The standard concentration for water quality.

[0052] The method for determining a water surface ratio configuration scheme provided in the embodiments of the present application has an objective function that includes at least one of the following: a flood control and drainage sub-function, a water resource utilization sub-function, and a water environment capacity sub-function. Different sub-functions represent key needs in different aspects of the water network area, covering sub-functions such as flood control and drainage, water resource utilization, and water environment capacity. This can comprehensively consider the functions of the water network in flood control, water supply, and ecology, avoiding the one-sided pursuit of a single goal while neglecting other aspects. For example, when determining the water surface ratio, if only water resource utilization is considered, increasing the water surface area to store water resources may increase the risk of flood disasters during flood season due to neglecting flood control and drainage. However, by comprehensively considering multiple sub-functions, it is possible to comprehensively balance and formulate a water surface ratio configuration scheme that better meets the overall interests of the region. In addition, each sub-function quantitatively evaluates the water surface ratio configuration scheme from different perspectives, providing a basis for multi-objective optimization. By calculating the values of each scheme under different sub-functions, it is possible to clearly understand the performance of the scheme on each objective. Then, through optimization algorithms and other means, the water surface ratio configuration can be adjusted to seek a balance between multiple objectives. The flood control and drainage subfunction calculates regional flood storage capacity and flood risk reduction for different water surface ratios. The water resource utilization subfunction assesses water resource utilization efficiency and the degree to which water demand is met. Based on this, a water surface ratio configuration is identified that optimally achieves multiple objectives, thereby achieving the coordinated development of water resources, the ecological environment, and the socioeconomic situation. Furthermore, the primary challenges and needs faced by water networks vary across regions. Selecting appropriate subfunction combinations to construct the objective function can enhance the adaptability of the solution to specific regions. In flood-prone areas, the flood control and drainage subfunction is prioritized, with appropriate consideration given to other subfunctions, to ensure that the solution effectively mitigates floods and protects people's lives and property. In water-scarce regions, the water resource utilization subfunction is prioritized, optimizing the water surface ratio configuration to improve water resource utilization efficiency. This tailored approach makes the water surface ratio configuration more scientific and practical, effectively addressing regional water challenges.

[0053] In an optional embodiment, the target water network area includes multiple water bodies, and the target constraints include a water surface ratio range constraint, a water quality standard constraint, a water level and flood storage capacity constraint, and a land use constraint. The water surface ratio range constraint is:

[0054] ;

[0055] in, is the total area of the target water network; For water bodies Water surface rate; is the upper limit of the water surface rate, is the lower limit of the water surface rate;

[0056] The water quality standards are constrained by:

[0057] , ;

[0058] in, For water bodies of dissolved oxygen, For water bodies Chemical oxygen demand, For water bodies Total nitrogen, For water bodies of total phosphorus; Chemical oxygen demand for environmental standards, Total nitrogen for environmental standards, is the environmental standard value of total phosphorus;

[0059] The water level and flood storage capacity constraints are: ;

[0060] in, For water bodies The water level, is the maximum allowable water level in the target water network area;

[0061] The land use constraints are:

[0062] ;

[0063] in, is the total area of the channel; is the total area of the pond; is the total area of wetlands; is the total area of farmland, is the total area of the target water network area.

[0064] The method for determining the water surface ratio configuration scheme provided in the embodiment of the present application has target constraints including at least one of a water surface ratio range constraint, a water quality standard constraint, a water level and flood storage capacity constraint, and a land use constraint. These constraints are set based on the actual geographical environment, resource conditions, and planning requirements to ensure that the generated water surface ratio configuration scheme is feasible in reality. The water surface ratio range constraint determines the upper and lower limits based on the regional topography and planning to avoid a water surface ratio that is too high and occupies too much land resources and affects other uses, or is too low and cannot meet the functional requirements of the water network; the land use constraint coordinates the water surface ratio configuration with other land use types such as farmland and wetlands to ensure the rational allocation and efficient use of regional land resources. Water quality standard constraints and water level and flood storage capacity constraints are crucial to protecting the ecological environment and ensuring water quality safety. Water quality standard constraints, by setting limits for pollutants such as dissolved oxygen and chemical oxygen demand, ensure that water quality meets ecological and user requirements, maintaining the stability and health of aquatic ecosystems. Water level and flood storage capacity constraints ensure that water levels remain within safe ranges, preventing damage to the ecological environment and human activities caused by floods. At the same time, they safeguard the water body's flood storage capacity, playing a role in regulating and storing floods when they occur, thereby reducing flood risks. Furthermore, the comprehensive consideration of these constraints contributes to achieving sustainable regional development. Reasonable water surface ratio allocation meets current flood control, water resource utilization, and ecological protection needs, while also taking into account future development needs. Through water level and flood storage capacity constraints and water surface ratio range constraints, the layout of the water network is optimized, water resource utilization efficiency is improved, and long-term stable regional development is guaranteed. Land use constraints coordinate the relationship between the water network and other land uses, promoting the coordinated development of the regional economy, society, and environment.

[0065] In an optional implementation, the first candidate water surface rate configuration schemes are fused to generate multiple first fused water surface rate configuration schemes, including:

[0066] Calculating the similarity between the first candidate water surface rate configuration schemes;

[0067] For each first candidate water surface rate configuration scheme whose similarity is greater than a preset similarity threshold, only one is retained to obtain multiple backup water surface rate configuration schemes;

[0068] The backup water surface rate configuration plans are cross-combined to generate first fused water surface rate configuration plans.

[0069] The method for determining water surface ratio configuration schemes provided in the embodiments of the present application calculates the similarity between each first candidate water surface ratio configuration scheme and retains only one scheme with a similarity greater than a preset threshold, effectively avoiding scheme redundancy. Among the numerous candidate schemes, some may be essentially similar and contribute similarly to the ultimate goal. Without screening, subsequent processing of these similar schemes would increase the computational workload, wasting computing resources and time. By removing redundant schemes and retaining representative ones, unnecessary computational burden is reduced, improving the efficiency of the entire scheme optimization process and enabling subsequent analysis and processing to focus more on differentiated schemes. While removing similar schemes, multiple different alternative water surface ratio configuration schemes are retained, ensuring scheme diversity. These different schemes each have unique advantages and characteristics, encompassing different water surface ratio configuration approaches and trade-offs between multiple objectives. One scheme may excel in flood control and drainage, while another may be more advantageous in water resource utilization. Retaining these diverse schemes provides rich material for subsequent cross-combination, facilitating the generation of a more comprehensive and high-quality first integrated water surface ratio configuration scheme. Then, cross-combining the alternative water surface ratio configuration schemes can integrate the advantages of different schemes to create a more comprehensive and innovative first-level integrated water surface ratio configuration scheme. This cross-combination process combines the characteristics and advantages of different schemes, potentially generating new configuration ideas and models. By cross-combining two schemes that excel in flood control and water resource utilization, the resulting scheme may achieve a good balance between these two aspects, thereby improving the scheme's overall performance across multiple objectives and better meeting the requirements of multi-objective optimization of water surface ratio configuration in practical applications. This makes the generated multiple first-level integrated water surface ratio configuration schemes more adaptable and better able to meet the complex and changing needs of the target water network area. By integrating the characteristics of different schemes, these integrated schemes can cover a wider range of situations and conditions. When facing the operational needs of the water network in different seasons and climate conditions, the integrated scheme may have better adjustment capabilities and adaptability, providing more high-quality options for the scientific planning and effective management of water network areas.

[0070] In an optional embodiment, each current optimal water surface ratio configuration scheme is identified, and a target water surface ratio configuration scheme corresponding to the target water network area is output, including:

[0071] Input each current optimal water surface rate configuration scheme into each sub-water surface rate configuration scheme model respectively;

[0072] Each sub-water surface rate configuration scheme model evaluates the current optimal water surface rate configuration scheme and votes on each current optimal water surface rate configuration scheme based on the evaluation results;

[0073] The current optimal water surface rate configuration plan with the most votes is determined as the target water surface rate configuration plan.

[0074] The method for determining a water surface ratio configuration scheme provided in the embodiment of the present application inputs each current optimal water surface ratio configuration scheme into each sub-water surface ratio configuration scheme model respectively. This allows the current optimal water surface ratio configuration scheme to be examined from multiple professional perspectives. Each sub-water surface ratio configuration scheme model evaluates the performance of each current optimal water surface ratio configuration scheme in terms of flood control and drainage, water resource utilization, water environment capacity, etc. based on its own algorithm characteristics, and votes on each current optimal water surface ratio configuration scheme based on the evaluation results. The target scheme is determined by combining these evaluation results, which can fully integrate the advantages of different models, avoid the limitations of a single model, and make the final target water surface ratio configuration scheme more scientific and reasonable. In addition, multiple sub-water surface ratio configuration scheme models independently evaluate and vote, which can reduce the risk of wrong decisions due to errors or limitations of individual sub-water surface ratio configuration scheme models. If only one model is relied upon to determine the scheme, the sub-water surface ratio configuration scheme model may give unreasonable results due to factors such as data anomalies and algorithm defects. Voting on multiple sub-water surface ratio configuration scheme models is similar to group decision-making and can complement and verify each other. Even if deviations occur in the evaluation of individual models, the correct evaluation of other models can ensure the reliability of the final decision, improve the credibility of the water surface ratio configuration plan, and provide a more reliable basis for practical application in the target water network area.

[0075] In a second aspect, the present invention provides a device for determining a water surface rate configuration scheme, the device comprising:

[0076] An acquisition module is used to acquire regional meteorological data, hydrological data, water quality data, and underlying surface factor data corresponding to the target water network area; the underlying surface factor data includes at least one of regional topographic data, regional soil type and property data, and regional vegetation type and coverage data;

[0077] An input module is used to input regional meteorological data, hydrological data, water quality data, and underlying surface factor data into a preset water surface ratio configuration scheme model, and output a target water surface ratio configuration scheme corresponding to the target water network area;

[0078] The configuration module is used to configure the water surface rate corresponding to the target water network area based on the target water surface rate configuration plan.

[0079] The water surface ratio configuration scheme determination device provided in the embodiment of the present application obtains regional meteorological data, hydrological data, water quality data, and underlying surface factor data corresponding to the target water network area, providing a comprehensive and accurate basis for the water surface ratio configuration scheme. This makes the target water surface ratio configuration scheme output by the preset water surface ratio configuration scheme model more consistent with actual conditions, improving the scientificity and accuracy of the configuration scheme. Then, the regional meteorological data, hydrological data, water quality data, and underlying surface factor data are input into the preset water surface ratio configuration scheme model, and the target water surface ratio configuration scheme corresponding to the target water network area is output, ensuring the accuracy and scientificity of the output target water surface ratio configuration scheme corresponding to the target water network area. The water surface ratio corresponding to the target water network area is configured based on the target water surface ratio configuration scheme. A scientific and reasonable water surface ratio configuration scheme helps balance the supply and demand of water resources and ensure the economic development and ecological stability of the target water network area. By optimizing the water surface ratio configuration, it can not only meet the current demand for water resources, but also protect water resources and the ecological environment, lay the foundation for the long-term sustainable development of the region, and achieve coordinated development of the economy, society, and environment. This achieves a reasonable water surface ratio configuration for the target water network area.

[0080] In a third aspect, the present invention provides an electronic device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the water surface rate configuration scheme determination method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0081] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method for determining a water surface ratio configuration scheme according to the first aspect or any corresponding embodiment thereof.

[0082] In a fifth aspect, the present invention provides a computer program product comprising computer instructions for causing a computer to execute the method for determining a water surface ratio configuration scheme according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0084] Figure 1 is a flow chart of a method for determining a water surface ratio configuration scheme according to an embodiment of the present invention;

[0085] Figure 2 is a flow chart of another method for determining a water surface ratio configuration scheme according to an embodiment of the present invention;

[0086] Figure 3 is a structural block diagram of a device for determining a water surface ratio configuration scheme according to an embodiment of the present invention;

[0087] Figure 4 FIG. 4 is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0088] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0089] Water networks are widely distributed around the world and serve as the core vehicles for water storage, allocation, and ecological services. In cities, water networks not only supply water for domestic and industrial use but also play a vital role in regulating urban microclimates and beautifying the environment. In rural areas and natural ecosystems, water networks support agricultural irrigation, maintain rich biodiversity, and are crucial for ecological balance. However, with global population growth, accelerated urbanization, and the impact of climate change, water networks face numerous challenges, including water shortages, the threat of flooding, and ecological degradation.

[0090] However, traditional water surface ratio configuration technology relies primarily on empirical formulas and simple model simulations, making it difficult to accurately analyze and predict complex water network structures and diverse boundary conditions. The lack of support from advanced information technology and data analysis methods leads to significant limitations in data collection, processing, and solution optimization, making it unable to meet the high-precision requirements of modern water network management.

[0091] In summary, how to configure the water surface ratio in the water network area in a scientific, reasonable, comprehensive and dynamically adaptable manner has become a key issue that needs to be urgently addressed in the current fields of water conservancy engineering, ecological environment and urban planning. It has extremely important practical significance for ensuring the sustainable use of water resources, maintaining ecological security and promoting sustainable social and economic development.

[0092] It should be noted that the method for determining the water surface ratio configuration scheme provided in the embodiment of the present application, its execution subject can be a device for determining the water surface ratio configuration scheme, and the device for determining the water surface ratio configuration scheme can be implemented as part or all of an electronic device through software, hardware, or a combination of software and hardware, wherein the electronic device can be a server or a terminal, wherein the server in the embodiment of the present application can be a single server or a server cluster composed of multiple servers, and the terminal in the embodiment of the present application can be a smart phone, a personal computer, a tablet computer, a wearable device, an intelligent robot, and other intelligent hardware devices. In the following method embodiments, the execution subject is an electronic device as an example for explanation.

[0093] According to an embodiment of the present invention, an embodiment of a method for determining a water surface rate configuration scheme is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0094] In this embodiment, a method for determining a water surface ratio configuration scheme is provided, which can be used for the above-mentioned electronic device. Figure 1 : is a flow chart of a method for determining a water surface rate configuration scheme according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0095] Step S101: Acquire regional meteorological data, hydrological data, water quality data, and underlying surface factor data corresponding to the target water network area.

[0096] The underlying surface factor data includes at least one of regional topography and landform data, regional soil type and property data, and regional vegetation type and coverage rate data.

[0097] Specifically, the electronic device can receive regional meteorological data, hydrological data, water quality data and underlying surface factor data corresponding to the target water network area input by the user, and can also receive regional meteorological data, hydrological data, water quality data and underlying surface factor data corresponding to the target water network area sent by other devices.

[0098] Regional meteorological data can include precipitation, temperature, evaporation, and wind speed for the target water network area. These climatic factors directly influence the dynamic adjustment of the water surface ratio. Hydrological data includes river flow, water level fluctuations, and runoff for the target water network area. Water quality data includes indicators such as dissolved oxygen, chemical oxygen demand, total nitrogen, and total phosphorus for the target water network area. Regional topography and geomorphology data are key factors influencing the shape of the water network and the movement of water flows. Different terrain types, such as mountains, plains, and hills, determine the direction, speed, and convergence of water flows. In mountainous areas, the terrain is rugged, rivers have large drop heights, and water flows rapidly, which easily lead to concentrated runoff and impose higher requirements for flood control and drainage. In plains, on the other hand, the terrain is flat and water flows relatively slowly. Changes in the water surface ratio have a more significant impact on flood regulation and water resource storage. Furthermore, topography and geomorphology influence the distribution of precipitation. Windward slopes of mountains tend to receive more precipitation, while leeward slopes are relatively dry. Obtaining regional topographic and geomorphological data can better understand the formation and evolution of water networks, providing a geographic basis for rationally planning water surface ratios. Regional soil type and property data show that soil type and properties significantly influence water infiltration, evaporation, and storage. Different soil types, such as sand, loam, and clay, vary significantly in porosity, permeability, and water-holding capacity. Sandy soils are highly permeable, leading to water infiltration and loss, hindering the long-term preservation of surface water. Clay, on the other hand, has poor permeability, causing surface water to accumulate after rainfall, potentially increasing localized water surface ratios. Understanding soil properties helps analyze water movement and storage within the soil, enabling rational planning of water surface ratios and improving water resource utilization efficiency. In agricultural irrigation areas, rationally allocating water surface ratios based on soil type and properties can better meet crop water requirements and reduce water waste. Regional vegetation type and cover data show that vegetation plays a crucial regulatory role in the ecosystem within water network areas. Vegetation influences the water cycle through transpiration. Dense vegetation can increase air humidity, intercept rainfall, reduce surface runoff, and increase soil moisture. In areas with high forest cover, some rainfall is intercepted by vegetation and released slowly, reducing the velocity and volume of surface runoff and helping to maintain a stable water surface ratio. In contrast, in areas with sparse vegetation, surface runoff is high, which can easily lead to soil erosion and fluctuations in water surface ratio. Furthermore, vegetation can improve soil structure and enhance its water retention capacity. Obtaining data on regional vegetation types and coverage can assess the regulatory role of vegetation on water networks and provide an ecological basis for optimizing water surface ratio allocation.

[0099] The embodiment of the present application does not specifically limit the manner in which the electronic device obtains regional meteorological data, hydrological data, water quality data, and underlying surface factor data corresponding to the target water network area.

[0100] Step S102: input regional meteorological data, hydrological data, water quality data and underlying surface factor data into a preset water surface ratio configuration scheme model, and output a target water surface ratio configuration scheme corresponding to the target water network area.

[0101] Specifically, the electronic device can integrate regional meteorological data, hydrological data, water quality data, and underlying surface factor data, and then input the integrated data into a preset water surface ratio configuration scheme model. The preset water surface ratio configuration scheme model is constructed based on specific algorithms and principles, and is designed to calculate the optimal water surface ratio configuration scheme based on the input data.

[0102] After analyzing and calculating the input data, the pre-set water surface ratio allocation model ultimately outputs a target water surface ratio allocation plan for the target water network area. This plan specifies the area, layout, and connectivity of different water bodies (such as rivers, ponds, and wetlands) within the water network area to achieve multi-objective optimization. For example, within a specific target water network area, the pre-set water surface ratio allocation model might output increasing wetland area in a specific area to improve water storage and purification capacity, while also optimizing the layout of rivers and ditches to enhance flood control and drainage capabilities. The output plan may also include an evaluation and prediction of the plan's implementation effectiveness, providing a reference for subsequent decision-making and implementation. Implementing the target plan can effectively improve the water resources in the water network area, enhance flood control and drainage capabilities, protect the aquatic ecosystem, and promote regional sustainable development.

[0103] This step will be described in detail below.

[0104] Step S103: configuring the water surface rate corresponding to the target water network area based on the target water surface rate configuration plan.

[0105] Specifically, electronic equipment can configure a plan based on the target water surface ratio to clarify the distribution of different types of water surfaces within the target water network area, such as rivers, lakes, ponds, and wetlands. For large rivers, their primary functions in the region, such as flood control and water transport, are considered to determine the location and scope of river channel widening or regulation. For lakes and ponds, their size and shape are rationally planned based on the surrounding terrain and water demand. For wetlands, appropriate low-lying areas or floodplains are selected for planning to maximize their ecological purification and water storage functions.

[0106] Next, the connectivity between the various water bodies within the target water network area is analyzed. According to the configuration plan, measures such as ditches, sluice gates, and pumping stations are implemented to optimize water connectivity. This ensures that water can flow efficiently between different water bodies, improving water resource allocation and the self-purification capacity of water bodies. For example, in areas with dense river networks but poor connectivity, disconnected rivers can be opened to form a complete water circulation system.

[0107] The method for determining a water surface ratio configuration scheme provided in the embodiments of the present application obtains regional meteorological data, hydrological data, water quality data, and underlying surface factor data corresponding to the target water network area, providing a comprehensive and accurate basis for the water surface ratio configuration scheme. This makes the target water surface ratio configuration scheme output by the preset water surface ratio configuration scheme model more consistent with actual conditions, thereby improving the scientific nature and accuracy of the configuration scheme. Then, the regional meteorological data, hydrological data, water quality data, and underlying surface factor data are input into the preset water surface ratio configuration scheme model, and the target water surface ratio configuration scheme corresponding to the target water network area is output, ensuring the accuracy and scientific nature of the target water surface ratio configuration scheme outputted for the target water network area. The water surface ratio corresponding to the target water network area is configured based on the target water surface ratio configuration scheme. A scientific and reasonable water surface ratio configuration scheme helps balance the supply and demand of water resources and ensure the economic development and ecological stability of the target water network area. By optimizing the water surface ratio configuration, it can not only meet the current demand for water resources, but also protect water resources and the ecological environment, laying the foundation for the long-term sustainable development of the region and achieving coordinated development of the economy, society, and environment. A reasonable water surface ratio configuration is achieved for the target water network area.

[0108] In this embodiment, a method for determining a water surface ratio configuration scheme is provided, which can be used for the above-mentioned electronic device. Figure 2 : is a flow chart of a method for determining a water surface rate configuration scheme according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0109] Step S201: Acquire regional meteorological data, hydrological data, water quality data, and underlying surface factor data corresponding to the target water network area.

[0110] The underlying surface factor data includes at least one of regional topography and landform data, regional soil type and property data, and regional vegetation type and coverage rate data.

[0111] For details about this step, please refer to the above description of step S101 and will not be repeated here.

[0112] Step S202: input regional meteorological data, hydrological data, water quality data and underlying surface factor data into a preset water surface ratio configuration scheme model, and output a target water surface ratio configuration scheme corresponding to the target water network area.

[0113] Specifically, the preset water surface ratio configuration scheme model includes a feature extraction model and a water surface ratio configuration scheme model. The above step S202 may include the following steps:

[0114] Step S2021: input regional meteorological data, hydrological data, water quality data, and underlying surface factor data into a feature extraction model.

[0115] Specifically, electronic devices can preprocess various data types before input. Regional meteorological data, such as precipitation, temperature, evaporation, and wind speed, may contain missing data, outliers, or inconsistent formats. To address these issues, interpolation is used to fill missing values, statistical analysis is used to identify and address outliers, and data formats are standardized to ensure data accuracy and completeness. Hydrological data, such as river flow, water level changes, and runoff, also require data cleaning and standardization to ensure comparability. Water quality data, such as dissolved oxygen, chemical oxygen demand, total nitrogen, and total phosphorus, may vary due to different sampling times, locations, and methods, and therefore require normalization. Underlying surface factor data, such as topography (which can be represented by digital elevation models), soil type and property data (such as soil texture and porosity), and vegetation type and cover data, must be digitized and formatted to enable recognition and processing by feature extraction models.

[0116] The electronic device then inputs the pre-processed regional meteorological data, hydrological data, water quality data and underlying surface factor data into the feature extraction model.

[0117] In step S2022, the feature extraction model identifies the input data and determines the data types of various data in the input data.

[0118] Among them, the input data include regional meteorological data, hydrological data, water quality data and underlying surface factor data.

[0119] Specifically, the feature extraction model identifies the input data and determines the data types of various data in the input data. The data types may include image types and numerical types.

[0120] Step S2023: setting multi-channel input according to the data types of various data.

[0121] Specifically, feature extraction models can continuously monitor the changing trends of various input data types. For example, for meteorological data, they can track the rate of change of indicators such as precipitation and temperature over time. During the rainy season, precipitation can increase dramatically over a short period of time. By analyzing precipitation data in real time, feature extraction models can capture this rapid growth trend and determine that the weight of precipitation data on the water network system has increased significantly at this time.

[0122] The feature extraction model then analyzes the changing trends of various input data types, identifies data fluctuations, and ultimately determines the data feature analysis results. For example, river flow in hydrological data can fluctuate significantly during flood seasons but remain relatively stable during dry seasons. The feature extraction model assesses the magnitude of fluctuations by calculating statistics such as the standard deviation of flow data. When flow fluctuations exceed the normal range, it indicates that the data is more critical to the current state of the water network.

[0123] Finally, the feature extraction model dynamically adjusts the computing resources of each channel based on the results of data feature analysis. When precipitation data becomes increasingly important during the rainy season, the feature extraction model allocates more computing units (such as neurons and computing cores in the neural network) to the precipitation data channel. These additional computing units can perform more complex operations on precipitation data, such as using higher-order convolution kernels for feature extraction or increasing the number of neural network layers to further explore the relationship between precipitation and water network responses.

[0124] Furthermore, the feature extraction model adjusts the data transmission bandwidth of each channel based on data feature analysis results and sets dynamic weights for different channels, enabling multi-channel input based on various data types. For example, during the dry season, evaporation has a greater impact on the water resource balance of the water network. The feature extraction model increases the bandwidth of the evaporation data channel, allowing more data to be processed through this channel per unit time. This is achieved by adjusting the buffer size for data storage and transmission and optimizing data reading and writing algorithms, ensuring that evaporation data can be processed more quickly and accurately by the model. The importance of different data types varies under different water network operating conditions. In the early stages of a flood caused by heavy rain, precipitation and river flow data are crucial for predicting flood progression. The feature extraction model increases the weight of these two data channels. During the model's computation, such as in the weighted summation layer of the neural network, the weight coefficients of these two data channels are increased, giving them a greater role in model decision-making.

[0125] Step S2024: Based on each input channel, feature extraction is performed on various types of input data, and feature representation is output.

[0126] Specifically, the above step S2024 may include:

[0127] In step a1, the image data in the input data is input as an image channel.

[0128] Image data usually comes from a variety of sources, the most common of which are satellite or aerial remote sensing images acquired through remote sensing technology. These images can show the topography, water distribution, land use, and other information of the target water network area from a macro perspective.

[0129] Specifically, the feature extraction model takes the image data in the input data as image channels and then uses deep learning technology to achieve adaptive image enhancement. Specifically, the feature extraction model automatically determines which areas need to be enhanced and what enhancement method to use based on the content and features of the input image. For example, for water bodies with fuzzy boundaries in water network areas, the feature extraction model automatically identifies and applies edge enhancement algorithms to highlight the boundaries of the water body, allowing subsequent image channel processing to more accurately capture the range and shape characteristics of the water body. This adaptive enhancement avoids the limitations of traditional fixed enhancement methods and can better adapt to image data in different scenarios.

[0130] Step a2: For the numerical data in the input data, convert the numerical data into a two-dimensional matrix form and allocate channels according to the feature dimensions.

[0131] Specifically, when numerical data exhibits time series characteristics, such as the temporal variation of precipitation in meteorological data or the fluctuation of water levels at different times in hydrological data, the feature extraction model can construct a two-dimensional matrix based on the time series. Time is used as one dimension of the matrix, and the values corresponding to different time points are used as the other dimension. For numerical data with spatial distribution characteristics, such as soil moisture and vegetation cover at different geographic locations, the feature extraction model can construct a two-dimensional matrix based on spatial coordinates. Based on longitude and latitude or other spatial coordinate systems, the spatial location is used as the row and column index of the matrix, and the values corresponding to the location are filled into the matrix elements. For soil moisture data from multiple monitoring points within a region, it is converted into a two-dimensional matrix based on the coordinates of the monitoring points. If there are multiple spatially related numerical data, such as soil moisture and soil nutrient content, they can also be integrated into a single matrix, preserving the spatial correlation information of the data. When allocating channels, channels can be divided according to spatial regions, for example, assigning data from adjacent regions to the same channel, which facilitates the model to learn spatial similarities and differences.

[0132] Furthermore, feature extraction models can also exploit the inherent relationships between numerical data and map them into a two-dimensional matrix. By calculating correlations, causal relationships, and other relationships between data, a relationship matrix is constructed. When analyzing the relationship between meteorological and hydrological data, the correlation coefficient between precipitation and river flow is calculated, and the correlation coefficient is populated into the matrix, with data types as rows and columns. For complex relationships between multiple data types, an n×n (n is the number of data types) relationship matrix is formed. This matrix can highlight the interactions between data. For channel allocation, data can be grouped according to the closeness of the relationship, with dimensions corresponding to closely related data being assigned to the same channel. This helps the model capture the synergistic variations between data.

[0133] The feature extraction model can then assign channels according to feature dimensions and adjust channel weights in real time based on the importance of the data to the target task (such as water surface rate configuration plan formulation) in different scenarios.

[0134] Optionally, the feature extraction model can also create cross-fusion channels to facilitate the exchange and integration of data from different feature dimensions. Relevant dimensions are selected from different types of numerical data and combined into new cross-fusion channels. For example, the precipitation dimension from meteorological data and the river velocity dimension from hydrological data are combined to form a new channel that reflects the impact of precipitation on water velocity. Through this cross-fusion channel, the model can learn comprehensive features across data types, uncovering information that is difficult to discover using a single data type channel, providing a richer perspective for water network analysis.

[0135] In step a3, convolution processing is performed on the data input from each input channel using multiple convolution kernels of different sizes to extract target features of different scales.

[0136] Specifically, the feature extraction model can dynamically select the convolution kernel size based on the features of the data input through each input channel. For example, when processing image data of a water network area, the feature extraction model first performs a preliminary analysis of the image to determine the scale range of the main features it contains. If the image contains both large-scale features such as large lakes and small-scale features such as small ditches, the feature extraction model adaptively selects the frequency and order of using convolution kernels of different sizes based on the proportion and importance of the features. After the large-size convolution kernel obtains the global features, it passes the global feature information to the small-size convolution kernel. The small-size convolution kernel uses this global feature information to perform more refined feature mining in the local area, thereby extracting target features of different scales.

[0137] For example, after a large-scale convolution kernel identifies the approximate extent of a water body, a small-scale convolution kernel at the water body's boundary, combined with the global position information conveyed by the large-scale convolution kernel, further extracts subtle texture and shape changes at the boundary. This fusion approach fully leverages the advantages of convolution kernels of different sizes to generate a more comprehensive and accurate feature representation.

[0138] In step a4, the target features extracted from different levels and types of data are fused according to the channel dimension to generate feature representation.

[0139] Specifically, the feature extraction model can first identify the target features extracted from data of different levels and types, and determine the hierarchical association mechanism between each target feature. Then, based on the hierarchical association mechanism between each target feature, the weight information corresponding to each target feature is determined. For local river channel detail features), the feature extraction model can adopt a cross-scale feature fusion method. First, the features of different scales are hierarchically processed, and the large-scale features are used as global features and the small-scale features as local features. Then, based on the weight information corresponding to each target feature, convolution operations or pooling operations are used to fuse features between different scales to generate feature representations. For example, through upsampling and downsampling operations, the large-scale features and small-scale features are adjusted to the same scale, and then the channel dimension is fused. The idea of skip connection can also be introduced to directly connect features of different scales, so that the fused feature representation can simultaneously contain information of different scales and more comprehensively describe the structure and function of the water network.

[0140] Step S2025 , inputting the feature representation into the water surface ratio configuration scheme model, and outputting a target water surface ratio configuration scheme corresponding to the target water network area based on the feature representation.

[0141] Specifically, the water surface ratio configuration scheme model includes multiple sub-water surface ratio configuration scheme models of different algorithms. The above step S2025 may include the following steps:

[0142] Step b1: input the feature representation into each sub-water surface rate configuration scheme model respectively.

[0143] Specifically, the electronic device may input the feature representation into each sub-water surface ratio configuration scheme model respectively.

[0144] In step b2, each sub-water surface ratio configuration scheme model identifies the feature representation and outputs a plurality of first candidate water surface ratio configuration schemes respectively.

[0145] In an optional embodiment of the present application, the above step b2 may include the following steps:

[0146] Step b21: for one of the sub-water surface ratio configuration scheme models, the sub-water surface ratio configuration scheme model identifies the feature representation.

[0147] Specifically, for one of the sub-water surface ratio configuration scheme models, the sub-water surface ratio configuration scheme model identifies the feature representation.

[0148] Step b22: Generate multiple initial water surface rate configuration plans based on the target constraint conditions in the sub-water surface rate configuration plan model.

[0149] Among them, the target constraints include water surface ratio range constraints, water quality standard constraints, water level and flood storage capacity constraints, and land use constraints.

[0150] Among them, the water surface rate range constraint is: ;

[0151] in, is the total area of the target water network; For water bodies Water surface rate; is the upper limit of the water surface rate, is the lower limit of the water surface rate;

[0152] The water quality standards are constrained by:

[0153] , ;

[0154] in, For water bodies of dissolved oxygen, For water bodies Chemical oxygen demand, For water bodies Total nitrogen, For water bodies of total phosphorus; Chemical oxygen demand for environmental standards, Total nitrogen for environmental standards, is the environmental standard value of total phosphorus;

[0155] The water level and flood storage capacity constraints are: ;

[0156] in, For water bodies The water level, is the maximum allowable water level in the target water network area;

[0157] The land use constraints are: ;

[0158] in, is the total area of the channel; is the total area of the pond; is the total area of wetlands; is the total area of farmland, is the total area of the target water network area.

[0159] Specifically, the sub-water surface ratio configuration model includes multiple agents, each focusing on a different type of objective constraint. One "hydrological agent" specifically addresses water level and flood storage capacity constraints; another "ecological agent" focuses on water quality standards and ecological protection constraints; and another "land use agent" prioritizes land use constraints and water surface ratio range constraints. These agents collaborate to generate initial water surface ratio configurations, comprehensively considering multiple constraints through information sharing and interaction. After the hydrological agent determines the minimum water surface area required to meet flood storage requirements, it passes this information to the land use agent. Based on this information, the land use agent, taking into account land resource conditions, determines a feasible water surface layout. The ecological agent then evaluates the ecological suitability of the generated preliminary plan and provides feedback to the other agents for adjustment. Together, they generate multiple initial water surface ratio configurations that meet various constraints.

[0160] Step b23: Based on the feature representation, each initial water surface ratio configuration scheme is substituted into the objective function in the sub-water surface ratio configuration scheme model to calculate the objective function value corresponding to each initial water surface ratio configuration scheme.

[0161] Among them, the objective function includes at least one of the flood control and drainage sub-function, the water resource utilization sub-function, and the water environment capacity sub-function.

[0162] Specifically, the objective function includes:

[0163] in, is the flood control and drainage sub-function; The water body in the target water network area i area; For water bodies The flood storage capacity is as follows: ;in For water bodies The water level, is the flood storage coefficient;

[0164] and / or,

[0165]

[0166] in, is the water resource utilization sub-function, For water bodies The available water volume is calculated as follows: ; Where P is the water body The precipitation of water body The evaporation rate is , β is the regional hydrological conversion coefficient; For water bodies Water use efficiency is defined as: ;in, For agricultural irrigation needs;

[0167] and / or,

[0168]

[0169] in, is the water environment capacity subfunction; Pi is the water body The pollution load is usually related to land use and emissions; Vi is the water The environmental capacity is calculated as follows: ; Among them, γ is the comprehensive degradation coefficient, which is related to the water The dilution capacity of is the area of water body i, is the water level of water body i, The standard concentration for water quality.

[0170] Specifically, the sub-water surface ratio allocation model dynamically adjusts the weights of various sub-functions in the objective function (such as the flood control and drainage sub-function, the water resource utilization sub-function, and the water environment capacity sub-function) based on the importance and relevance of each feature in the feature representation. For water network areas located in high-flood-prone areas, flood-related features (such as river channel width and terrain slope) are more prominent in the feature representation. In these cases, the weight of the flood control and drainage sub-function in the objective function is increased, resulting in a more rigorous evaluation of the flood control performance of the scheme. Through this adaptive weighting adjustment, the objective function can better align with the actual needs of the water network area and more accurately reflect the strengths and weaknesses of each initial scheme.

[0171] Then, using a multi-core processor or distributed computing platform, parallel calculations are performed on each initial water surface ratio configuration scheme. Different schemes are assigned to different computing cores or nodes, and the objective function is substituted into the calculations simultaneously to obtain the objective function value corresponding to each initial water surface ratio configuration scheme. This can significantly shorten the calculation time, especially when dealing with a large number of initial schemes. For example, in a large-scale water network area, hundreds or even thousands of initial water surface ratio configuration schemes may be generated. Through parallel calculations, the objective function value corresponding to each scheme can be quickly obtained, improving evaluation efficiency and buying more time for subsequent scheme screening and optimization.

[0172] Step b24: Determine the historical optimal water surface rate configuration scheme and the current global optimal water surface rate configuration scheme based on the objective function values corresponding to the initial water surface rate configuration schemes.

[0173] Specifically, the sub-water surface ratio configuration scheme model can introduce multi-dimensional evaluation indicators. Multi-dimensional evaluation indicators can include factors such as the implementation cost of the scheme, the long-term impact on the surrounding ecological environment, and social and economic benefits. A scheme with a high objective function value may have too high an implementation cost or have potential negative impacts on the ecological environment. By integrating these multi-dimensional indicators, each initial water surface ratio configuration scheme is scored. A comprehensive evaluation model is constructed to weightedly integrate the objective function value with other evaluation indicators to obtain a comprehensive score. The historical optimal and current global optimal schemes are determined based on the comprehensive score. This can avoid the limitations of focusing only on the objective function value and ignoring other important factors, making the selected scheme more valuable for practical application.

[0174] The sub-water surface ratio configuration model can then utilize swarm intelligence algorithms (such as the ant colony algorithm and particle swarm algorithm) to assist in determining the optimal solution. Each initial water surface ratio configuration solution is considered an individual or particle in the swarm intelligence algorithm, with the objective function value and comprehensive evaluation score serving as the individual's fitness value. Through the algorithm's iterative search, individuals continuously evolve and optimize within the solution space. In the ant colony algorithm, ants search and select between different solutions based on pheromone guidance, with the strength of the pheromone reflecting the quality of the solution. As iterations proceed, the algorithm gradually converges to a more optimal solution. The global search capabilities of the swarm intelligence algorithm can discover excellent solutions that are difficult to find using traditional methods, improving the efficiency and quality of determining the optimal solution, thereby determining both the historically optimal water surface ratio configuration solution and the current globally optimal water surface ratio configuration solution.

[0175] Step b25: Determine the update speed based on the historical optimal water surface rate configuration plan and the current global optimal water surface rate configuration plan.

[0176] Specifically, in each iteration, each initial water surface rate configuration scheme updates its speed and position based on its corresponding historical optimal water surface rate configuration scheme pbest and the current global optimal water surface rate configuration scheme gbest. The speed update formula is:

[0177]

[0178] in, is the speed of the initial water surface rate configuration scheme i at time t, w is the inertia weight, which is used to balance the global and local search capabilities. As the number of iterations increases, w can decrease linearly, so that the algorithm focuses on global search in the early stage and focuses on local optimization in the later stage; c1 and c2 are learning factors, usually set to constants; r1 and r2 are random numbers between [0,1]; pbest i is the historical optimal water surface rate configuration scheme corresponding to the initial water surface rate configuration scheme i; gbest iThe current global optimal water surface rate configuration scheme corresponding to the initial water surface rate configuration scheme i. is the position of the initial water surface rate configuration scheme i at time t.

[0179] Step b26: Obtain updated water surface rate configuration plans based on the update speed.

[0180] Among them, each updated water surface rate configuration scheme meets the target constraint.

[0181] Specifically, the position update formula is:

[0182] in, Configure the scheme for each updated water surface rate.

[0183] Step b27, calculating the objective function value corresponding to each updated water surface rate configuration scheme.

[0184] Specifically, the sub-water surface ratio configuration scheme model can calculate the objective function value corresponding to each updated water surface ratio configuration scheme based on the above objective function.

[0185] Step b28: Determine the updated water surface rate configuration scheme whose objective function value is greater than the preset function value as the first candidate water surface rate configuration scheme.

[0186] Specifically, the sub-water surface ratio configuration scheme model can compare the objective function value corresponding to each updated water surface ratio configuration scheme with a preset function value, and then determine the updated water surface ratio configuration scheme with an objective function value greater than the preset function value as the first candidate water surface ratio configuration scheme.

[0187] Step b29: If there is no updated water surface ratio configuration scheme whose objective function value is greater than the preset function value, the updated water surface ratio configuration scheme is continuously updated until the updated water surface ratio configuration scheme whose objective function value is greater than the preset function value is determined as the first candidate water surface ratio configuration scheme.

[0188] Specifically, if there is no updated water surface ratio configuration scheme whose objective function value is greater than the preset function value, the sub-water surface ratio configuration scheme model continues to update the updated water surface ratio configuration scheme until the updated water surface ratio configuration scheme whose objective function value is greater than the preset function value is determined as the first candidate water surface ratio configuration scheme.

[0189] In an optional embodiment of the present application, other sub-water surface ratio configuration scheme models may be sub-water surface ratio configuration scheme models based on a genetic algorithm (GA), sub-water surface ratio configuration scheme models based on a simulated annealing algorithm (SA), or sub-water surface ratio configuration scheme models based on other algorithms. The embodiments of the present application do not specifically limit other sub-water surface ratio configuration scheme models.

[0190] The Genetic Algorithm (GA) is an optimization algorithm based on natural selection and genetic mechanisms. It simulates the biological evolutionary process through operations such as encoding, selection, crossover, and mutation to find the optimal solution. In the water surface ratio configuration problem, the water surface ratio configuration is encoded as chromosomes. By selecting chromosomes with high fitness and performing crossover and mutation, new configurations are continuously generated, gradually evolving towards the optimal solution.

[0191] A simulated annealing (SA) algorithm is used to optimize the sub-water surface ratio configuration. SA, derived from the simulation of the solid annealing process, controls the temperature parameter to accept a less favorable solution with a certain probability, thereby escaping the local optimum. In the water surface ratio optimization, starting from an initial water surface ratio configuration, a new solution is randomly searched in the solution space. If the new solution has a better objective function value, it is accepted; otherwise, a less favorable solution is accepted with a certain probability based on the current temperature and the Metropolis criterion. As the temperature gradually decreases, the algorithm converges to the global optimal solution.

[0192] Step b3: fuse the first candidate water surface rate configuration schemes to generate multiple first fused water surface rate configuration schemes.

[0193] Specifically, the above step b3 may include the following steps:

[0194] Step b31: Calculate the similarity between the first candidate water surface rate configuration schemes.

[0195] Specifically, the water surface ratio configuration scheme model represents each first-tier candidate water surface ratio configuration scheme as a graph structure, where nodes represent individual elements in the water network system (such as rivers, lakes, pumping stations, etc.), and edges represent relationships between elements (such as water flow direction, connectivity, etc.). These graph structures are learned and analyzed using graph neural networks (GNNs). Through the GNN's message passing mechanism, nodes can obtain information about their neighboring nodes, thereby learning the structural characteristics of the entire graph. The water surface ratio configuration scheme model then calculates the similarity between the graph structures of different schemes by comparing feature vectors extracted by the graph neural network or using a graph matching algorithm to measure the similarity of the graph structures. This approach can capture the complex relationships between elements in the scheme and has unique advantages for evaluating the overall layout and functional similarity of water surface ratio configuration schemes in the water network system.

[0196] In step b32, only one of the first candidate water surface rate configuration schemes whose similarity is greater than a preset similarity threshold is retained to obtain multiple backup water surface rate configuration schemes.

[0197] Specifically, for each first candidate water surface ratio configuration scheme whose similarity is greater than a preset similarity threshold, the water surface ratio configuration scheme model retains only one of them, thereby obtaining a plurality of backup water surface ratio configuration schemes.

[0198] Step b33: Cross-combining the standby water surface rate configuration plans to generate first fused water surface rate configuration plans.

[0199] Specifically, the water surface ratio allocation model breaks down each alternative water surface ratio allocation plan into different functional modules of the water network system, such as the flood control module (including dam design and flood storage area planning), the water resource allocation module (water source protection and water channel layout), the ecological restoration module (wetland construction and aquatic plant cultivation), and the water purification module (wastewater treatment facilities and ecological pond installation). The model then determines the importance of each functional module based on the current needs and future development trends of the water network region. Based on this weighting, the alternative water surface ratio allocation plans are cross-combined to generate the first fused water surface ratio allocation plan. For example, during periods of frequent drought, the water resource allocation module is given a relatively high weight; in areas with greater flood risk, the flood control module is more critical. When combining the plans, the corresponding modules of each plan are weighted and integrated according to their weightings. For the water resources allocation module, if the weight of the relevant part of Plan A is 0.6 and that of Plan B is 0.4, when generating the module of the fusion plan, both are comprehensively considered and combined according to the weight distribution ratio to make the generated plan more in line with actual needs.

[0200] Step b4: input each first fused water surface ratio configuration scheme into each sub-water surface ratio configuration scheme model respectively.

[0201] Specifically, each first fused water surface ratio configuration scheme is input again into each sub-water surface ratio configuration scheme model respectively.

[0202] In step b5, each sub-water surface ratio configuration scheme model evaluates and updates each first fused water surface ratio configuration scheme, and outputs a plurality of second candidate water surface ratio configuration schemes.

[0203] Specifically, each sub-water surface rate configuration scheme model evaluates and updates each first fused water surface rate configuration scheme, and the process of outputting multiple second candidate water surface rate configuration schemes can be referred to the above process of each sub-water surface rate configuration scheme model identifying the feature representation and outputting multiple first candidate water surface rate configuration schemes respectively, which will not be repeated here.

[0204] Step b6: Repeat this process until a preset number of times, and each sub-water surface ratio configuration scheme model outputs its corresponding current optimal water surface ratio configuration scheme.

[0205] Specifically, the water surface ratio configuration scheme model can dynamically adjust the number of loops and loop strategies based on the degree of optimization of the current optimal water surface ratio configuration scheme. In the early stages of the loop, if the water surface ratio configuration scheme model finds that the optimization speed of the scheme is fast and the objective function value increases rapidly, then the number of subsequent loops can be appropriately reduced to avoid over-optimization and waste of computing resources. On the contrary, if the improvement of the scheme is slow, increase the number of loops or adjust the parameters and operation methods of the model in the loop. It is also possible to set an adaptive loop strategy for each sub-water surface ratio configuration scheme model based on the characteristics of different sub-water surface ratio configuration scheme models. For example, for a model based on deep learning, when its convergence speed slows down, adjust the learning rate or increase the amount of training data to speed up the optimization process of the scheme.

[0206] Finally, based on the adjusted number of cycles and cycle strategies, each sub-water surface rate configuration scheme model outputs its corresponding current optimal water surface rate configuration scheme.

[0207] Step b7: Identify each current optimal water surface ratio configuration scheme and output the target water surface ratio configuration scheme corresponding to the target water network area.

[0208] Specifically, the above step b7 may include the following steps:

[0209] Step b71: input each current optimal water surface ratio configuration scheme into each sub-water surface ratio configuration scheme model respectively.

[0210] Specifically, each current optimal water surface ratio configuration scheme is input into each sub-water surface ratio configuration scheme model respectively.

[0211] In step b72, each sub-water surface ratio configuration scheme model evaluates the current optimal water surface ratio configuration scheme, and votes on each current optimal water surface ratio configuration scheme based on the evaluation result.

[0212] Specifically, each sub-water surface ratio configuration scheme model can evaluate the current optimal water surface ratio configuration scheme based on the above-mentioned objective function and the knowledge graph in the water network field to obtain an evaluation result. Among them, the knowledge graph contains rich professional knowledge, practical experience and various cases. When evaluating the current optimal water surface ratio configuration scheme, the model will refer to the relevant information in the knowledge graph for intelligent reasoning and judgment. If a scheme involves the construction of a specific water conservancy facility, the knowledge graph can provide the operating results and success and failure cases of the facility under different geographical environments and climatic conditions, helping the model to more accurately evaluate the feasibility and potential risks of the scheme. At the same time, the knowledge graph can also analyze the relationship between the various elements in the scheme to determine whether there are potential conflicts or synergistic effects, thereby providing more scientific evaluation results.

[0213] Then, vote on the current optimal water surface rate configuration schemes based on the evaluation results.

[0214] Step b73: determine the current optimal water surface rate configuration plan with the most votes as the target water surface rate configuration plan.

[0215] Specifically, the water surface ratio configuration scheme model determines the current optimal water surface ratio configuration scheme with the most votes as the target water surface ratio configuration scheme.

[0216] Step S203: configuring the water surface rate corresponding to the target water network area based on the target water surface rate configuration plan.

[0217] For details about this step, please refer to the above description of step S103 and will not be repeated here.

[0218] The method for determining the water surface ratio configuration scheme provided in the embodiment of the present application inputs regional meteorological data, hydrological data, water quality data, and underlying surface factor data into a feature extraction model. The feature extraction model identifies the input data and determines the data types of various data in the input data, thereby ensuring the accuracy of the data types of various data in the determined input data. Then, multi-channel input is set according to the data types of various data. Thus, the feature extraction model can perform targeted processing based on the characteristics of different types of data. The time series data such as precipitation and temperature in meteorological data have significant differences in characteristics from the flow and water level change data in hydrological data and water quality data. Multi-channel input allows the feature extraction model to adopt processing methods suitable for each data type respectively, avoiding the loss of key information due to unified processing, ensuring the effective use of data, and laying the foundation for subsequent accurate feature extraction. Directly inputting image data as image channels completely preserves the spatial information and visual features of the image. For example, in remote sensing images of water network areas, the shape, location, and range of water bodies can be intuitively presented, providing an intuitive geographic information basis for subsequent analysis. Converting numerical data into a two-dimensional matrix and assigning channels according to feature dimensions can structure abstract numerical information, facilitating model recognition and processing. For example, flow and water level values in hydrological data can be organized according to feature dimensions such as time series or spatial distribution after conversion, enabling the model to better capture relationships and changing trends between values. Then, convolution processing is performed on each input channel data using multiple convolution kernels of different sizes to extract target features at different scales. Small convolution kernels focus on local details and, when processing image data, can accurately identify subtle features such as water body edges and small ditches. Large convolution kernels focus on global information, capturing the macroscopic layout and structure of the entire water network. When processing numerical data, small convolution kernels can capture short-term fluctuations and local changes in the data, while large convolution kernels are used to analyze long-term trends and overall patterns. This multi-scale feature extraction approach comprehensively encompasses various data features, enhancing the depth and breadth of data understanding. Finally, target features extracted from data at different levels and types are fused according to the channel dimension, generating a feature representation that integrates the strengths of multiple data sources. The spatial features of image data and the quantitative features of numerical data complement each other, providing the model with richer and more comprehensive information. When determining the water surface ratio configuration plan, the fused features can not only reflect the actual geographical form of the water network, but also reflect the changes in related hydrological, meteorological and other data, making the water surface ratio configuration plan model decision more scientific and accurate. In addition, the rich and comprehensive feature representation helps to improve the performance of the water surface ratio configuration plan model in subsequent tasks. The water surface ratio configuration plan model is based on the fused feature representation for analysis, which can more accurately capture the complex relationships and potential laws between data, improve prediction accuracy and evaluation reliability. At the same time, the introduction of multi-scale features enhances the model's adaptability to different situations, allowing it to maintain good performance when faced with complex and changing data.

[0219] The water surface ratio allocation model then comprises multiple sub-water surface ratio allocation models based on different algorithms. Each sub-water surface ratio allocation model processes the feature representation based on different algorithmic principles and optimization strategies. For each sub-water surface ratio allocation model, an initial water surface ratio allocation plan is generated based on the objective constraints, fundamentally ensuring the feasibility of the plan in practical applications. This prevents the generated plan from being unimplementable due to violating practical constraints. Based on the feature representation, each initial water surface ratio allocation plan is substituted into the objective function of the sub-water surface ratio allocation model, and the corresponding objective function value is calculated for each initial water surface ratio allocation plan. This allows for the evaluation of each initial water surface ratio allocation plan from multiple perspectives. When evaluating a plan, not only is its effectiveness in flood control and drainage understood, but its impact on water resource utilization and water environmental capacity is also understood. This enables multi-objective optimization of the water surface ratio allocation plan, ensuring that the generated plan better meets the comprehensive requirements of sustainable development in the water network region. The historically optimal and currently globally optimal water surface ratio allocation plans are then determined based on the objective function values, and the update rate is determined accordingly to continuously generate updated allocation plans. The updated solution with an objective function value greater than the preset function value is determined as the first candidate water surface ratio configuration solution. This screening mechanism can gradually eliminate undesirable solutions and retain and optimize more advantageous solutions. If there is no solution that meets the preset conditions, it will be continuously updated to ensure that the final first candidate water surface ratio configuration solution performs well under the multi-objective comprehensive evaluation, thereby improving the quality and practicality of the first candidate water surface ratio configuration solution. In the process of continuous updating and screening, the first candidate water surface ratio configuration solution can better adapt to the complex situation of the water network area. Since each update is based on the previous optimal solution and takes into account the comprehensive impact of multiple objective functions, the generated solution can more accurately meet actual needs. For a water network area that has both flood control needs and water resource shortages, this mechanism can find a first candidate water surface ratio configuration solution that achieves a good balance between flood control and water resource utilization through multiple iterative optimizations.

[0220] Next, the similarity between each first-tier candidate water surface ratio configuration scheme is calculated. Only one scheme with a similarity greater than a preset threshold is retained, effectively avoiding scheme redundancy. Among the numerous candidate schemes, some may be essentially similar and contribute similarly to the ultimate goal. Without screening, subsequent processing of these similar schemes would increase the computational workload, wasting computing resources and time. By removing redundant schemes and retaining representative ones, unnecessary computational burden is reduced, improving the efficiency of the entire scheme optimization process and allowing subsequent analysis and processing to focus more on differentiated schemes. While removing similar schemes, multiple alternative water surface ratio configuration schemes are retained, ensuring scheme diversity. These different schemes each have unique advantages and characteristics, encompassing different water surface ratio configuration approaches and multi-objective trade-offs. One scheme may excel in flood control and drainage, while another may be more advantageous in water resource utilization. Retaining these diverse schemes provides rich material for subsequent cross-combination, facilitating the generation of a more comprehensive and high-quality first-tier integrated water surface ratio configuration scheme. Then, cross-combining the alternative water surface ratio configuration schemes can integrate the advantages of different schemes to create a more comprehensive and innovative first-level integrated water surface ratio configuration scheme. This cross-combination process combines the characteristics and advantages of different schemes, potentially generating new configuration ideas and models. By cross-combining two schemes that excel in flood control and water resource utilization, the resulting scheme may achieve a good balance between these two aspects, thereby improving the scheme's overall performance across multiple objectives and better meeting the requirements of multi-objective optimization of water surface ratio configuration in practical applications. This makes the generated multiple first-level integrated water surface ratio configuration schemes more adaptable and better able to meet the complex and changing needs of the target water network area. By integrating the characteristics of different schemes, these integrated schemes can cover a wider range of situations and conditions. When facing the operational needs of the water network in different seasons and climate conditions, the integrated scheme may have better adjustment capabilities and adaptability, providing more high-quality options for the scientific planning and effective management of water network areas.

[0221] The first fused water surface ratio allocation scheme is then re-entered into each sub-water surface ratio allocation scheme model for evaluation and update, outputting a second candidate water surface ratio allocation scheme, and repeating this process. Each iteration optimizes and improves the previous scheme. The sub-water surface ratio allocation scheme model re-evaluates and adjusts the fused scheme based on its own algorithm, gradually exploring the scheme's potential and continuously moving it closer to the optimal solution. After a preset number of iterations, each sub-water surface ratio allocation scheme model outputs its corresponding current optimal water surface ratio allocation scheme. Each current optimal water surface ratio allocation scheme is then input into each sub-water surface ratio allocation scheme model. This allows for a multi-disciplinary perspective on each current optimal water surface ratio allocation scheme. Based on its own algorithmic characteristics, each sub-water surface ratio allocation scheme model evaluates the performance of each current optimal water surface ratio allocation scheme in terms of flood control and drainage, water resource utilization, and water environment capacity. Based on the evaluation results, the model votes on each current optimal water surface ratio allocation scheme. The target solution is determined by integrating these evaluation results. This fully integrates the strengths of different models, avoids the limitations of a single model, and makes the final target water surface ratio allocation scheme more scientific and reasonable. Furthermore, independently evaluating and voting on multiple sub-rate allocation models reduces the risk of incorrect decisions due to errors or limitations in individual models. Relying solely on a single model to determine the solution could lead to illogical results due to data anomalies, algorithmic flaws, and other factors. Voting on multiple sub-rate allocation models, however, is similar to group decision-making, complementing and validating each other. Even if individual models exhibit deviations in their evaluation, the correct evaluations of the other models ensure the reliability of the final decision, enhancing the credibility of the rate allocation solution and providing a more reliable basis for practical application in the target water network area.

[0222] This embodiment also provides a device for determining a water surface ratio configuration scheme, which is used to implement the above-mentioned embodiments and preferred embodiments. Details already described will not be repeated here. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. While the devices described in the following embodiments are preferably implemented using software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0223] This embodiment provides a device for determining a water surface ratio configuration scheme, such as Figure 3 As shown, including:

[0224] An acquisition module 301 is configured to acquire regional meteorological data, hydrological data, water quality data, and underlying surface factor data corresponding to a target water network area; the underlying surface factor data includes at least one of regional topographic data, regional soil type and property data, and regional vegetation type and coverage data;

[0225] Input module 302, for inputting regional meteorological data, hydrological data, water quality data and underlying surface factor data into a preset water surface ratio configuration scheme model, and outputting a target water surface ratio configuration scheme corresponding to the target water network area;

[0226] The configuration module 303 is used to configure the water surface rate corresponding to the target water network area based on the target water surface rate configuration plan.

[0227] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0228] The water surface rate configuration scheme determination device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0229] An embodiment of the present invention further provides an electronic device having the above Figure 3 The water surface rate configuration scheme determination device shown.

[0230] See also Figure 4 , Figure 4 is a structural diagram of an electronic device provided by an optional embodiment of the present invention, such as Figure 4 As shown, the electronic device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple electronic devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 4 A processor 10 is taken as an example.

[0231] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0232] The memory 20 stores instructions that can be executed by at least one processor 10, so as to enable at least one processor 10 to execute the method shown in the above embodiment.

[0233] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and applications required for at least one function; the data storage area may store data created based on the use of the electronic device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0234] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0235] The electronic device further includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 4 The bus connection is taken as an example.

[0236] The input device 30 can receive input digital or character information and generate key signal input related to user settings and function control of the electronic device. Examples include a touch screen, keypad, mouse, trackpad, touchpad, pointer, one or more mouse buttons, trackball, joystick, etc. The output device 40 may include a display device, auxiliary lighting devices (e.g., LEDs), and tactile feedback devices (e.g., vibration motors). Such display devices include, but are not limited to, liquid crystal displays, light emitting diodes, monitors, and plasma displays. In some optional embodiments, the display device may be a touch screen.

[0237] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for determining a water surface ratio configuration scheme, characterized in that: Methods include: Obtain regional meteorological data, hydrological data, water quality data and underlying surface factor data corresponding to the target water network area; The underlying surface factor data includes at least one of regional topography and landform data, regional soil type and property data, and regional vegetation type and coverage rate data; Inputting regional meteorological data, hydrological data, water quality data and underlying surface factor data into a feature extraction model in a preset water surface ratio configuration scheme model; Identify input data, determine the data types of various data in the input data, and continuously monitor the changing trends of various types of input data; the input data include regional meteorological data, hydrological data, water quality data, and underlying surface factor data; Analyze the changing trends of various input data and determine the data feature analysis results; According to the results of data feature analysis, multi-channel input is set up, and the computing resources, data transmission bandwidth and dynamic weight of each channel are dynamically adjusted; the multi-channel includes single data type channels and cross-fusion channels. Single data type channels are used to extract features from a single type of data, while cross-fusion channels are used to extract features from different types of data; Based on each input channel, feature extraction is performed on various types of input data, and feature representation is output; Inputting the feature representations into each sub-water surface ratio configuration scheme model, identifying the feature representations, and outputting a plurality of first candidate water surface ratio configuration schemes respectively; each sub-water surface ratio configuration scheme model corresponds to a different algorithm; fusing the first candidate water surface rate configuration schemes to generate a plurality of first fused water surface rate configuration schemes; Input each first fused water surface rate configuration scheme into each sub-water surface rate configuration scheme model, evaluate and update each first fused water surface rate configuration scheme, and output multiple second candidate water surface rate configuration schemes; After a preset number of times, each sub-water surface rate configuration scheme model outputs its corresponding current optimal water surface rate configuration scheme; Identify the current optimal water surface ratio configuration schemes and output the target water surface ratio configuration scheme corresponding to the target water network area; Configure the water surface rate corresponding to the target water network area based on the target water surface rate configuration plan; Among them, each sub-water surface rate configuration scheme model identifies the feature representation and outputs multiple first candidate water surface rate configuration schemes, including: For one of the sub-water surface rate configuration scheme models, the sub-water surface rate configuration scheme model identifies the feature representation; Each agent in the sub-water surface ratio configuration scheme model generates multiple initial water surface ratio configuration schemes based on the target constraints in the sub-water surface ratio configuration scheme model; the target constraints include at least one of the following: water surface ratio range constraints, water quality standard constraints, water level and flood storage capacity constraints, and land use constraints; each agent focuses on different types of target constraints; Based on the feature representation, each initial water surface ratio configuration scheme is substituted into the objective function of the sub-water surface ratio configuration scheme model. According to the importance and relevance of each feature in the feature representation, the weight of each sub-function in the objective function is dynamically adjusted to calculate the objective function value corresponding to each initial water surface ratio configuration scheme; wherein the objective function includes at least one sub-function of the flood control and drainage sub-function, the water resource utilization sub-function, and the water environment capacity sub-function; According to the objective function values corresponding to the initial water surface rate configuration schemes, the historical optimal water surface rate configuration scheme and the current global optimal water surface rate configuration scheme are determined; The update speed is determined based on the historical optimal water surface rate configuration plan and the current global optimal water surface rate configuration plan; the speed update formula is: ;in, is the velocity of the initial surface rate configuration scheme i at time t; w is the inertia weight; c1 and c2 are learning factors; r1 and r2 are random numbers; pbest i is the historical optimal water surface rate configuration scheme corresponding to the initial water surface rate configuration scheme i; gbest i The current global optimal water surface rate configuration scheme corresponding to the initial water surface rate configuration scheme i; is the position of the initial water surface rate configuration scheme i at time t; According to the update speed, each updated water surface rate configuration scheme is obtained; each updated water surface rate configuration scheme satisfies the target constraint; Calculate the objective function value corresponding to each updated water surface rate configuration scheme; Determine the updated water surface rate configuration scheme whose objective function value is greater than the preset function value as the first candidate water surface rate configuration scheme; If there is no updated water surface ratio configuration scheme whose objective function value is greater than the preset function value, the updated water surface ratio configuration scheme will continue to be updated until the updated water surface ratio configuration scheme whose objective function value is greater than the preset function value is determined as the first candidate water surface ratio configuration scheme.

2. The method according to claim 1, characterized in that The feature extraction of various types of input data based on each input channel and output of feature representation include: The image data in the input data is input as an image channel; For the numerical data in the input data, convert the numerical data into a two-dimensional matrix form and allocate channels according to feature dimensions; Use multiple convolution kernels of different sizes to perform convolution processing on the data input from each input channel to extract target features of different scales; The target features extracted from different levels and different types of data are fused according to the channel dimension to generate the feature representation.

3. The method according to claim 1, characterized in that The target water network area includes multiple water bodies, and the objective function includes: ， in, is the flood control and drainage sub-function, The water body in the target water network area i area; For water bodies The flood storage capacity is as follows: ;in For water bodies The water level, is the flood storage coefficient; and / or, ， in, is the water resource utilization sub-function, For water bodies The available water volume is calculated as follows: ; Where P is water The precipitation of water body The evaporation amount, β is the regional hydrological conversion coefficient; For water bodies Water use efficiency is defined as: ;in, For agricultural irrigation needs; and / or, ， in, is the water environment capacity subfunction; Pi is the water body The pollution load is usually related to land use and emissions; Vi is the water The environmental capacity is calculated as follows: ， Among them, γ is the comprehensive degradation coefficient, which is related to the water The dilution capacity of is the area of water body i, is the water level of water body i, The standard concentration for water quality.

4. The method according to claim 1, wherein The target water network area includes multiple water bodies, and the target constraint conditions include the water surface ratio range constraint, the water quality standard constraint, the water level and flood storage capacity constraint, and the land use constraint. The water surface ratio range constraint is: ； in, is the total area of the target water network area; For water bodies Water surface rate; The upper limit of the water surface rate, is the lower limit of the water surface rate; The water quality standard constraints are: ， ； in, For water bodies of dissolved oxygen, For water bodies Chemical oxygen demand, For water bodies Total nitrogen, For water bodies of total phosphorus; Chemical oxygen demand for environmental standards, Total nitrogen for environmental standards, is the environmental standard value of total phosphorus; The water level and flood storage capacity constraints are: ； in, For water bodies The water level, is the maximum allowable water level of the target water network area; The land use constraints are: ； in: is the total area of the channel; is the total area of the pond; is the total area of wetlands; is the total area of farmland, is the total area of the target water network area.

5. The method according to claim 1, wherein The fusing of the first candidate water surface rate configuration schemes to generate a plurality of first fused water surface rate configuration schemes includes: Calculating the similarity between the first candidate water surface rate configuration schemes; For each of the first candidate water surface rate configuration schemes whose similarity is greater than a preset similarity threshold, only one is retained to obtain multiple backup water surface rate configuration schemes; The standby water surface rate configuration schemes are cross-combined to generate the first fused water surface rate configuration schemes.

6. The method according to claim 1, characterized in that Identifying each of the current optimal water surface ratio configuration schemes and outputting the target water surface ratio configuration scheme corresponding to the target water network area includes: Inputting each of the current optimal water surface rate configuration schemes into each of the sub-water surface rate configuration scheme models respectively; Each of the sub-water surface rate configuration scheme models evaluates the current optimal water surface rate configuration scheme, and votes on each of the current optimal water surface rate configuration schemes according to the evaluation results; The current optimal water surface rate configuration scheme with the most votes is determined as the target water surface rate configuration scheme.

7. A device for determining a water surface ratio configuration scheme, characterized in that: The device includes: An acquisition module is used to obtain regional meteorological data, hydrological data, water quality data and underlying surface factor data corresponding to the target water network area; The underlying surface factor data includes at least one of regional topography and landform data, regional soil type and property data, and regional vegetation type and coverage rate data; An input module, used to input regional meteorological data, hydrological data, water quality data and underlying surface factor data into a feature extraction model in a preset water surface ratio configuration scheme model; Identify input data, determine the data types of various data in the input data, and continuously monitor the changing trends of various types of input data; the input data include regional meteorological data, hydrological data, water quality data, and underlying surface factor data; Analyze the changing trends of various input data and determine the results of data feature analysis; according to the results of data feature analysis, set up multi-channel input and dynamically adjust the computing resources, data transmission bandwidth and dynamic weight of each channel; among them, the multi-channel includes single data type channel and cross fusion channel, the single data type channel is used to extract features of a single type of data, and the cross fusion channel is used to extract features of different types of data; based on each input channel, extract features of various types of input data and output feature representation; input the feature representation into each sub-water surface rate configuration scheme model respectively, identify the feature representation, and output multiple First candidate water surface rate configuration schemes; each sub-water surface rate configuration scheme model corresponds to a different algorithm; each first candidate water surface rate configuration scheme is fused to generate multiple first fused water surface rate configuration schemes; each first fused water surface rate configuration scheme is input into each sub-water surface rate configuration scheme model, each first fused water surface rate configuration scheme is evaluated and updated, and multiple second candidate water surface rate configuration schemes are output; after the preset number of times, each sub-water surface rate configuration scheme model outputs its corresponding current optimal water surface rate configuration scheme; each current optimal water surface rate configuration scheme is identified, and the target water surface rate configuration scheme corresponding to the target water network area is output; wherein, each sub-water surface rate configuration scheme model outputs the target water surface rate configuration scheme corresponding to the target water network area; wherein, each sub-water surface rate configuration scheme The rate configuration scheme model identifies the feature representation and outputs multiple first candidate water surface rate configuration schemes respectively, including: for one of the sub-water surface rate configuration scheme models, the sub-water surface rate configuration scheme model identifies the feature representation; each intelligent agent in the sub-water surface rate configuration scheme model generates multiple initial water surface rate configuration schemes based on the target constraint conditions in the sub-water surface rate configuration scheme model; the target constraint conditions include at least one of the water surface rate range constraint, water quality standard constraint, water level and flood storage capacity constraint and land use constraint; each intelligent agent focuses on different types of target constraints; based on the feature representation, each initial water surface rate configuration scheme is substituted into The objective function in the sub-water surface ratio configuration scheme model is dynamically adjusted according to the importance and relevance of each feature in the feature representation, and the objective function value corresponding to each initial water surface ratio configuration scheme is calculated; wherein the objective function includes at least one sub-function among the flood control and drainage sub-function, the water resource utilization sub-function, and the water environment capacity sub-function; according to the objective function value corresponding to each initial water surface ratio configuration scheme, the historical optimal water surface ratio configuration scheme and the current global optimal water surface ratio configuration scheme are determined; according to the historical optimal water surface ratio configuration scheme and the current global optimal water surface ratio configuration scheme, the update speed is determined; wherein the speed update formula is: ;in, is the velocity of the initial surface rate configuration scheme i at time t; w is the inertia weight; c1 and c2 are learning factors; r1 and r2 are random numbers; pbest i is the historical optimal water surface rate configuration scheme corresponding to the initial water surface rate configuration scheme i; gbest i The current global optimal water surface rate configuration scheme corresponding to the initial water surface rate configuration scheme i; is the position of the initial water surface rate configuration scheme i at time t; according to the update speed, each updated water surface rate configuration scheme is obtained; each updated water surface rate configuration scheme satisfies the target constraint; the objective function value corresponding to each updated water surface rate configuration scheme is calculated; the updated water surface rate configuration scheme whose objective function value is greater than the preset function value is determined as the first candidate water surface rate configuration scheme; if there is no updated water surface rate configuration scheme whose objective function value is greater than the preset function value, the updated water surface rate configuration scheme is continuously updated until the updated water surface rate configuration scheme whose objective function value is greater than the preset function value is determined as the first candidate water surface rate configuration scheme; The configuration module is used to configure the water surface rate corresponding to the target water network area based on the target water surface rate configuration plan.

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