Karst mountainous area water resource management method and system based on intelligent algorithm

Abstract

The application discloses a karst mountainous area water resource management method and system based on an intelligent algorithm, and belongs to the technical field of water resource management.The application realizes accurate prediction and efficient management of karst mountainous area water resources by integrating a three-dimensional model, an intelligent algorithm and multi-objective optimization, can comprehensively consider multidimensional information such as terrain, water area, historical data and the like, predicts by using the intelligent algorithm, and optimizes water resource distribution by using a multi-objective function, so that the water quantity balance, social economy and other constraint conditions are met while the ecological benefits, water shortage and water resource utilization benefits are met, and therefore the scientificity and sustainability of water resource management are improved.

Description

Technical Field

[0001] This invention relates to the field of water resource management technology, and more specifically to a method and system for water resource management in karst mountainous areas based on intelligent algorithms. Background Technology

[0002] Karst mountain areas, as a unique natural geographical environment on Earth, exhibit significant regional characteristics and complexities in their water resources. Research on water resource management in karst mountain areas is not only an urgent need to resolve regional water supply and demand imbalances and protect the ecological environment, but also an important pathway to promote scientific and technological progress and sustainable economic and social development.

[0003] However, the unique geological structure of karst mountain areas results in extremely uneven spatial and temporal distribution of water resources, making it difficult for traditional management methods to accurately capture these dynamic changes. Furthermore, existing monitoring methods are often limited by geographical location and technological conditions, hindering comprehensive and real-time perception of water resources and leading to incomplete and untimely data collection. In terms of forecasting, the complexity of hydrogeological conditions in karst mountain areas makes it difficult for traditional forecasting models to accurately reflect the changing patterns of water resources, resulting in limited forecast accuracy. Finally, in the scheduling phase, the lack of efficient decision support systems and intelligent scheduling algorithms often leads to unscientific and irrational allocation and utilization of water resources, resulting in resource waste.

[0004] Therefore, proposing a water resource management method and system based on intelligent algorithms to improve the utilization efficiency and management level of water resources in karst mountainous areas is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, the present invention provides a method and system for water resource management in karst mountainous areas based on intelligent algorithms. It uses three-dimensional digital twin technology to create a virtual water resource management model that reflects the status and changes of water resources in real time, helping decision-makers to manage more effectively.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] On the one hand, this invention discloses a water resource management method for karst mountain areas based on intelligent algorithms, comprising the following steps:

[0008] A terrain model is constructed based on the terrain data of the target karst mountain area, and the terrain model is combined with the water area model of the target karst mountain area to obtain a three-dimensional model of the karst mountain area.

[0009] A water resources management model is constructed and integrated into the 3D model of the karst mountain area;

[0010] Historical hydrological and meteorological data of the target karst mountain area are obtained and filtered to determine the factors affecting usable water resources.

[0011] The water resources management model is trained using historical data on available water resources and factors influencing historical available water resources to obtain an optimized prediction model.

[0012] Real-time data on available water resources and influencing factors of available water resources in the target karst mountain area are obtained, and available water resources are output according to the optimized prediction model.

[0013] The available water resources are combined with a multi-objective function to output the optimal water resource management plan.

[0014] Preferably, the water resource management model is constructed based on the MGRU model.

[0015] Preferably, a water resources management model is trained using historical data on available water resources and factors influencing historical available water resources, including:

[0016] Wavelet analysis and coefficient reconstruction were performed on the historical available water resources to obtain available water resources data components.

[0017] Phase space reconstruction is performed by combining the available water resources data components and the data on factors influencing available water resources to construct a training set;

[0018] The optimal hyperparameters of the MGRU model are found using the particle swarm optimization algorithm.

[0019] Based on the optimal hyperparameters, the MGRU model is trained using the training set to obtain an optimized prediction model.

[0020] Preferably, the multi-objective function L is constructed based on ecological benefits, water shortage, and water resource utilization benefits, as shown in the following formula:

[0021] L=min(-λ1L1+λ2L2-λ3L3)

[0022] In the formula, L1 is the ecological benefit objective function, L2 is the water shortage objective function, L3 is the water resource utilization benefit objective function, and λ1, λ2, and λ3 are the weight coefficients of the ecological benefit objective function, the water shortage objective function, and the water resource utilization benefit objective function, respectively.

[0023] Preferably, the ecological benefit objective function L1 is formulated as follows:

[0024]

[0025] In the formula, n is the total number of ecological areas within the target karst mountain area; Let be the minimum water requirement for the i-th ecological region; W represents the actual water volume obtained in the i-th ecological region. i Let be the weight of the i-th ecological region.

[0026] Preferably, the objective function L2 for water shortage is:

[0027]

[0028] In the formula, P represents the number of water use types, which in this embodiment includes industrial water use, agricultural water use, and domestic water use, Q represents the number of regions for the p-th water use type, and D represents the number of regions for the p-th water use type. p,q S represents the water demand of the q-th region for the p-th water use type. p,q This represents the actual water consumption of the q-th region for the p-th water use type.

[0029] Preferably, the objective function L3 for water resource utilization benefits is:

[0030]

[0031] In the formula, M represents the number of water resource management schemes, and R... m C represents the economic benefit of the m-th water resource management scheme. m Let m be the cost of the m-th water resource management scheme.

[0032] Preferably, under the condition of satisfying the constraints, the optimal water resource management scheme is output by solving the multi-objective function using an optimization algorithm;

[0033] The constraints include water balance constraints, ecological benefit constraints, water demand constraints, water resource utilization efficiency constraints, and socio-economic constraints.

[0034] On the other hand, this invention proposes a water resource management system for karst mountain areas based on intelligent algorithms, used to implement the aforementioned water resource management method for karst mountain areas based on intelligent algorithms. The system includes:

[0035] The 3D model building module is used to build a terrain model based on the terrain data of the target karst mountain area. The terrain model is combined with the water area model of the target karst mountain area to obtain a 3D model of the karst mountain area.

[0036] The model integration module is used to construct a water resources management model and integrate the water resources management model into the 3D model of the karst mountain area;

[0037] The influencing factor screening module is used to acquire historical hydrological and meteorological data of the target karst mountain area and screen the historical hydrological and meteorological data to determine the influencing factors of usable water resources.

[0038] The model training module is used to train the water resources management model using historical data on available water resources and factors influencing historical available water resources, so as to obtain an optimized prediction model.

[0039] The water resources availability output module is used to acquire in real time the water resources availability and the data of factors influencing available water resources in the target karst mountain area, and output the water resources availability according to the optimized prediction model.

[0040] The management scheme output module is used to output the optimal water resource management scheme based on the available water resources and a multi-objective function.

[0041] As can be seen from the above technical solution, compared with the prior art, this invention discloses a method and system for water resource management in karst mountainous areas based on intelligent algorithms. By constructing a three-dimensional terrain and water area model, combining historical hydrological and meteorological data, and applying optimization algorithms to solve multi-objective functions, it achieves accurate prediction of available water resources and outputs the optimal management plan. This invention not only improves the scientific nature and efficiency of water resource management, but also comprehensively considers ecological, economic, and social factors, effectively addressing the unique water resource challenges in karst mountainous areas, and ultimately promoting sustainable development and ecological protection. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0043] Figure 1 This is a schematic diagram of the method flow provided by the present invention;

[0044] Figure 2 This is a schematic diagram of the system architecture provided by the present invention. Detailed Implementation

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] This invention discloses a water resource management method for karst mountain areas based on intelligent algorithms, with reference to... Figure 1 The method includes the following steps:

[0047] S1. Construct a terrain model based on the terrain data of the target karst mountain area. Combine the terrain model with the water area model of the target karst mountain area to obtain a three-dimensional model of the karst mountain area.

[0048] Topographic data of the target karst mountain area, including contour lines and point cloud data, is obtained through methods such as drone aerial surveying and ground surveying.

[0049] Use professional 3D modeling software, such as SketchUp and 3DMAX, to build a terrain model based on the terrain data.

[0050] Meanwhile, information on water bodies (rivers, lakes, reservoirs, etc.) in the mountainous area was obtained through field surveys and remote sensing image interpretation, including the location, shape, and size of the water bodies. A water body model was constructed using GIS software, and the terrain model and water body model were merged using the fusion function of GIS software or 3D modeling software to obtain a 3D model of the karst mountainous area.

[0051] S2. Construct a water resources management model and integrate it into a 3D model of the karst mountain area. Specifically, the water resources management model is constructed based on the MGRU model.

[0052] In this embodiment, the model structure is determined to be a three-layer MGRU network, with each layer containing 64 neurons. The loss function is the mean squared error (MSE) function.

[0053] The water resources management model was integrated into a 3D model of the karst mountain area using a software development kit (SDK). Within the 3D model, the model's operational results and predictions can be displayed through a visual interface.

[0054] S3. Obtain historical hydrological and meteorological data of the target karst mountain area and screen the historical hydrological and meteorological data to determine the factors affecting usable water resources.

[0055] Historical hydrological and meteorological data for the target karst mountain area were collected, including rainfall, evaporation, temperature, humidity, river flow, and groundwater level. This data can be obtained from meteorological stations, hydrological stations, geological departments, and can also be collected using remote sensing technology and data mining methods.

[0056] The collected historical hydrological and meteorological data are processed to remove outliers and noise, ensuring the accuracy and reliability of the data.

[0057] This embodiment performs correlation coefficient analysis on the above-mentioned historical hydrological and meteorological data to determine the influencing factors of usable water resources.

[0058] S4. Train a water resources management model using historical data on available water resources and factors influencing historical available water resources, to obtain an optimized prediction model, including:

[0059] S41. Perform wavelet analysis and coefficient reconstruction on the historical available water resources to obtain available water resource data components.

[0060] Wavelet analysis was performed on historical water resource availability, using the Daubechies wavelet basis function to decompose it into components of different frequencies. Wavelet analysis can effectively extract features from time series data and remove noise and trends.

[0061] Then, the wavelet coefficients are reconstructed to obtain the available water resource data components. These data components can better reflect the changing characteristics of available water resources.

[0062] S42. Combine the available water resources data components and the data on factors influencing available water resources to reconstruct the phase space and construct a training set.

[0063] Phase space reconstruction is performed on available water resource data components and influencing factor data, and used as input for the MGRU model. Phase space reconstruction can reveal the intrinsic structure and dynamic characteristics of time series data.

[0064] A training set is constructed based on the data after phase space reconstruction, which is used to train the water resource management model.

[0065] S43. Use the particle swarm optimization algorithm to find the optimal hyperparameters of the MGRU model.

[0066] Determine the range of hyperparameters for the MGRU model, such as learning rate, number of hidden layer neurons, batch size, etc.

[0067] Particle Swarm Optimization (PSO) is used to find the optimal combination of hyperparameters within a given range. PSO is a swarm intelligence-based optimization algorithm that finds the optimal solution by simulating the foraging behavior of a flock of birds.

[0068] The performance of the MGRU model under different hyperparameter combinations is evaluated, and the hyperparameter combination with the best performance is selected as the optimal hyperparameter.

[0069] S44. Based on the optimal hyperparameters, train the MGRU model using the training set to obtain the optimized prediction model.

[0070] The MGRU model is initialized using the optimal hyperparameters.

[0071] The constructed training set is input into the MGRU model for training. During training, the backpropagation algorithm is used to adjust the model's weights and biases, enabling the model to better fit the training data.

[0072] The training process is iterated continuously until the model converges or reaches the preset number of training iterations.

[0073] The optimized water resources management prediction model was finally obtained.

[0074] S5. Real-time acquisition of available water resources and influencing factors in the target karst mountain area, and output of available water resources based on the optimized prediction model.

[0075] S6. The optimal water resource management plan is output by combining the available water resources with a multi-objective function.

[0076] A multi-objective function L is constructed based on ecological benefits, water shortage, and water resource utilization benefits, as shown in the following formula:

[0077] L=min(-λ1L1+λ2L2-λ3L3)

[0078] In the formula, L1 is the ecological benefit objective function, L2 is the water shortage objective function, L3 is the water resource utilization benefit objective function, and λ1, λ2, and λ3 are the weight coefficients of the ecological benefit objective function, the water shortage objective function, and the water resource utilization benefit objective function, respectively.

[0079] The ecological benefit objective function L1 formula is as follows:

[0080]

[0081] In the formula, n is the total number of ecological areas within the target karst mountain area; Let be the minimum water requirement for the i-th ecological region; W represents the actual water volume obtained in the i-th ecological region. i Let be the weight of the i-th ecological region.

[0082] The objective function L2 for water shortage is:

[0083]

[0084] In the formula, P represents the number of water use types, Q represents the number of regions with the p-th water use type, and D... p,q S represents the water demand of the q-th region for the p-th water use type. p,q This represents the actual water consumption of the q-th region for the p-th water use type.

[0085] The objective function L3 for water resource utilization benefits is:

[0086]

[0087] In the formula, M represents the number of water resource management schemes, and R... m C represents the economic benefit of the m-th water resource management scheme. m Let m be the cost of the m-th water resource management scheme.

[0088] Under the condition of satisfying the constraints, optimization algorithms, such as genetic algorithms, are used to solve the multi-objective function and output the optimal water resource management scheme.

[0089] The constraints include:

[0090] Water balance constraint: HEO≥0; where H is the amount of water resources available output by the optimized prediction model, E is the evaporation rate, and O is the total water demand, including water demand in ecological areas and water demand from different types of industries such as industry, agriculture, and domestic use.

[0091] Ecological benefit constraint: L1≥E min

[0092] Among them, E min This is the minimum threshold for ecological benefits, in order to ensure the sustainability of the ecological environment.

[0093] Water demand constraint: D≤D max

[0094] Among them, D max The target is the maximum water shortage that the karst mountainous area can withstand, ensuring that the basic water needs of various sectors are met.

[0095] Water resource utilization efficiency constraints:

[0096] Where W represents the water consumption in the region; I represents the rainfall; R in U represents the amount of water flowing into the area. th This serves as the lower limit for water resource utilization efficiency, thereby ensuring the effective use of water resources.

[0097] Socioeconomic constraints: C total ≤B.

[0098] In the formula, C total B represents the total cost of the water resource management plan; B represents the maximum affordable cost budget.

[0099] On the other hand, this invention proposes a water resource management system for karst mountain areas based on intelligent algorithms, used to implement the aforementioned water resource management method for karst mountain areas based on intelligent algorithms, such as... Figure 2 As shown, the system includes:

[0100] The 3D model building module is used to build a terrain model based on the terrain data of the target karst mountain area. The terrain model is combined with the water area model of the target karst mountain area to obtain a 3D model of the karst mountain area.

[0101] The model integration module is used to build a water resources management model and integrate it into a 3D model of karst mountainous areas.

[0102] The influencing factor screening module is used to obtain historical hydrological and meteorological data of the target karst mountain area and screen the historical hydrological and meteorological data to determine the influencing factors of usable water resources.

[0103] The model training module is used to train a water resources management model using historical data on available water resources and factors influencing historical available water resources, and to obtain an optimized prediction model.

[0104] The water resources availability output module is used to acquire data on the availability of water resources and the influencing factors of available water resources in the target karst mountain area in real time, and output the availability of water resources based on the optimized prediction model.

[0105] The management plan output module is used to output the optimal water resource management plan based on the available water resources and a multi-objective function.

[0106] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0107] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A water resource management method based on intelligent algorithms in karst mountain areas, characterized in that, Includes the following steps: A terrain model is constructed based on the terrain data of the target karst mountain area, and the terrain model is combined with the water area model of the target karst mountain area to obtain a three-dimensional model of the karst mountain area. A water resources management model is constructed and integrated into the 3D model of the karst mountain area; the water resources management model is constructed based on the MGRU model. Historical hydrological and meteorological data of the target karst mountain area are obtained and filtered to determine the factors affecting usable water resources. The water resources management model is trained using historical data on available water resources and factors influencing these resources to obtain an optimized prediction model, including: Wavelet analysis and coefficient reconstruction were performed on the historical available water resources to obtain available water resources data components. Phase space reconstruction is performed by combining the available water resources data components and the data on factors influencing available water resources to construct a training set; The optimal hyperparameters of the MGRU model are found using the particle swarm optimization algorithm. Based on the optimal hyperparameters, the MGRU model is trained using the training set to obtain an optimized prediction model; Real-time data on available water resources and influencing factors of available water resources in the target karst mountain area are obtained, and available water resources are output according to the optimized prediction model. The optimal water resource management plan is output by combining the available water resources with a multi-objective function; wherein the multi-objective function is constructed based on ecological benefits, water scarcity, and water resource utilization benefits. L The formula is as follows: In the formula, For the ecological benefit objective function, Let the objective function be the amount of water shortage. The objective function for water resource utilization efficiency is... , , These are the weighting coefficients for the ecological benefit objective function, the water shortage objective function, and the water resource utilization benefit objective function, respectively.

2. The method for water resource management in karst mountainous areas based on intelligent algorithms according to claim 1, characterized in that, The ecological benefit objective function The formula is as follows: ； In the formula, n This represents the total number of ecological areas within the target karst mountainous region. For the first i Minimum water requirement for each ecological zone; For the first i The actual amount of water obtained in each ecological area; For the first i The weight of each ecological region.

3. The method for water resource management in karst mountainous areas based on intelligent algorithms according to claim 1, characterized in that, The objective function for water shortage for: ； In the formula, P represents the number of water use types, and Q represents the number of water use types. p Number of areas with different water use types For the first p Type of water use q Water demand in each region For the first p Type of water use q The actual amount of water obtained in each region.

4. The method for water resource management in karst mountainous areas based on intelligent algorithms according to claim 1, characterized in that, The objective function for water resource utilization benefits for: ； In the formula, M represents the number of water resource management schemes. For the first m The economic benefits of water resource management programs; For the first m The cost of a water resource management plan.

5. A method for water resource management in karst mountainous areas based on intelligent algorithms according to claim 1, characterized in that, Under the condition of satisfying the constraints, the optimal water resource management scheme is output by solving the multi-objective function using an optimization algorithm. The constraints include water balance constraints, ecological benefit constraints, water demand constraints, water resource utilization efficiency constraints, and socio-economic constraints.

6. A water resource management system for karst mountain areas based on intelligent algorithms, characterized in that, include: The 3D model building module is used to build a terrain model based on the terrain data of the target karst mountain area. The terrain model is combined with the water area model of the target karst mountain area to obtain a 3D model of the karst mountain area. The model integration module is used to construct a water resources management model and integrate the water resources management model into the 3D model of the karst mountain area; the water resources management model is constructed based on the MGRU model. The influencing factor screening module is used to acquire historical hydrological and meteorological data of the target karst mountain area and screen the historical hydrological and meteorological data to determine the influencing factors of usable water resources. The model training module is used to train the water resource management model using historical data on available water resources and factors influencing these resources, to obtain an optimized prediction model, including: Wavelet analysis and coefficient reconstruction were performed on the historical available water resources to obtain available water resources data components. Phase space reconstruction is performed by combining the available water resources data components and the data on factors influencing available water resources to construct a training set; The optimal hyperparameters of the MGRU model are found using the particle swarm optimization algorithm. Based on the optimal hyperparameters, the MGRU model is trained using the training set to obtain an optimized prediction model; The water resources availability output module is used to acquire in real time the water resources availability and the data of factors influencing available water resources in the target karst mountain area, and output the water resources availability according to the optimized prediction model. The management scheme output module is used to output the optimal water resource management scheme based on the available water resources and a multi-objective function; wherein, the multi-objective function is constructed based on ecological benefits, water shortage, and water resource utilization benefits. L The formula is as follows: In the formula, For the ecological benefit objective function, Let the objective function be the amount of water shortage. The objective function for water resource utilization efficiency is... , , These are the weighting coefficients for the ecological benefit objective function, the water shortage objective function, and the water resource utilization benefit objective function, respectively.

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