Water conservancy planning method and system based on GIS

Abstract

The invention relates to the technical field of water conservancy planning, in particular to a GIS-based water conservancy planning method and system, and the method comprises the steps: obtaining GIS basic geographic data, remote sensing data, hydrological monitoring data and social economic data; constructing a multi-source data fusion matrix based on the GIS basic geographic data, the remote sensing data, the hydrological monitoring data and the social economic data; hydrological features are extracted according to the multi-source data fusion matrix; based on the hydrological characteristics, establishing a water resource system dynamic model; generating a multi-target planning scheme according to the dynamic model of the water resource system; according to the multi-source data fusion method provided by the invention, multi-source heterogeneous data of GIS, remote sensing, hydrological monitoring, social economy and the like can be effectively integrated, and the data basis of water conservancy planning is greatly enriched. Therefore, the planning accuracy is improved, the spatial-temporal scale of planning is expanded, and the planning scheme is more comprehensive and reliable.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy planning, and more specifically, to a water conservancy planning method and system based on GIS. Background Art

[0002] With the increasing impact of global climate change and human activities, water resources management is facing unprecedented challenges. Traditional water conservancy planning methods mainly rely on empirical models and single hydrological analysis, making it difficult to cope with the current complex and changing water resources environment. In recent years, with the development of Geographic Information System (GIS) technology, water conservancy planning methods based on GIS have gradually become a research hotspot.

[0003] Currently, the closest prior art mainly uses the method of combining GIS with hydrological models for water conservancy planning. This method has made certain progress compared with traditional methods, being able to better process spatial data and improve the accuracy of planning. However, this method still has some significant technical problems. First, the existing method has deficiencies in multi-source data fusion and is difficult to make full use of the massive heterogeneous data brought by emerging technologies such as remote sensing and the Internet of Things. Second, most existing hydrological models are deterministic models based on simplified assumptions and are difficult to accurately describe the complex non-linear dynamic characteristics of the water resources system. Third, the existing method is still insufficient in multi-objective optimization and is difficult to effectively balance multiple objectives such as economic development, ecological protection, and flood control and disaster reduction. Finally, the existing method lacks an intelligent decision-making support mechanism and is difficult to cope with emergencies and extreme climate conditions.

[0004] These technical problems seriously restrict the scientific nature and effectiveness of water conservancy planning, and there is an urgent need for a new water conservancy planning method that can comprehensively utilize multi-source data, accurately simulate the dynamics of water resources, achieve multi-objective optimization, and provide intelligent decision-making support. Summary of the Invention

[0005] The water conservancy planning method and system based on GIS proposed by the present invention aim to solve the above technical problems. Specifically, the core technical problems to be solved by the present invention include: effective fusion of multi-source heterogeneous data, accurate modeling of complex water resources systems, multi-objective optimization of water conservancy planning, and intelligent decision-making support.

[0006] The present invention provides a water conservancy planning method based on GIS, including:

[0007] An acquisition step, including:

[0008] Acquiring GIS basic geographic data, remote sensing data, hydrological monitoring data, and social and economic data;

[0009] A processing step, including:

[0010] Construct a multi-source data fusion matrix based on the GIS basic geographic data, remote sensing data, hydrological monitoring data, and socioeconomic data;

[0011] Extract hydrological features according to the multi-source data fusion matrix;

[0012] Establish a dynamic model of the water resources system based on the hydrological features;

[0013] Generate a multi-objective planning scheme according to the dynamic model of the water resources system;

[0014] The output step includes:

[0015] Output the optimal water conservancy planning scheme and decision support information.

[0016] Preferably, the construction of the multi-source data fusion matrix specifically includes:

[0017] Use the random matrix theory to perform dimensionality reduction processing on the GIS basic geographic data, remote sensing data, hydrological monitoring data, and socioeconomic data;

[0018] Estimate the data covariance matrix based on the Wishart distribution;

[0019] Generate a multi-source data fusion matrix according to the covariance matrix.

[0020] Preferably, the extraction of hydrological features specifically includes:

[0021] Construct a multi-scale simplicial complex based on the topological persistent homology theory;

[0022] Use the multi-scale simplicial complex to calculate the persistent homology group;

[0023] Extract hydrological features through a graph neural network according to the persistent homology group.

[0024] Preferably, the establishment of the dynamic model of the water resources system specifically includes:

[0025] Construct a non-linear dynamics model of water resources based on the Lorenz system;

[0026] Use the Lyapunov exponent to analyze the stability of the non-linear dynamics model of water resources;

[0027] Introduce a random perturbation term to simulate the uncertainty of the water resources system.

[0028] Preferably, the generation of the multi-objective planning scheme specifically includes:

[0029] Define a group of water conservancy planning objective functions based on group theory;

[0030] Construct a group action to reduce the complexity of the planning problem;

[0031] Utilize the quotient space to achieve multi-objective optimization.

[0032] Preferably, the output of the optimal water conservancy planning scheme and decision support information specifically includes:

[0033] Based on the quantum computing theory, construct decision-making qubits;

[0034] Design quantum gate operations to achieve the conversion of decision-making states;

[0035] Utilize quantum entanglement to simulate decision-making correlation;

[0036] Output the optimal decision-making result based on quantum computing.

[0037] Preferably, it further includes:

[0038] Based on the optimal water conservancy planning scheme, generate a 4D dynamic visualization display;

[0039] Utilize virtual reality technology to construct a simulation of the water conservancy project planning scenario.

[0040] Preferably, it further includes:

[0041] Construct a knowledge graph of water conservancy projects;

[0042] Based on the knowledge graph of water conservancy projects, conduct intelligent application of empirical knowledge.

[0043] Preferably, it further includes:

[0044] Establish a real-time data feedback mechanism;

[0045] Based on the real-time data feedback mechanism, dynamically optimize the hydrological model and the planning scheme.

[0046] A GIS-based water conservancy planning system for executing the method includes:

[0047] A data acquisition module for acquiring GIS basic geographic data, remote sensing data, hydrological monitoring data, and social and economic data;

[0048] A data fusion module for constructing a multi-source data fusion matrix based on the GIS basic geographic data, remote sensing data, hydrological monitoring data, and social and economic data;

[0049] A feature extraction module for extracting hydrological features according to the multi-source data fusion matrix;

[0050] A dynamic simulation module for establishing a dynamic model of the water resources system based on the hydrological features;

[0051] A planning optimization module for generating multi-objective planning schemes based on the dynamic model of the water resource system;

[0052] A decision support module for outputting the optimal water conservancy planning scheme and decision support information.

[0053] Specifically, the beneficial effects of the present invention are mainly reflected in the following aspects:

[0054] By innovatively integrating advanced technologies such as multi-source data fusion, topological data analysis, nonlinear dynamics modeling, group theory optimization, and quantum computing, the present invention realizes the full-process intelligent water conservancy planning from data processing to decision support. This method demonstrates significant technical advantages and beneficial effects in multiple aspects:

[0055] Firstly, the multi-source data fusion method proposed in the present invention can effectively integrate multi-source heterogeneous data such as GIS, remote sensing, hydrological monitoring, and social economy, greatly enriching the data basis for water conservancy planning. This not only improves the accuracy of the planning but also expands the spatio-temporal scale of the planning, making the planning scheme more comprehensive and reliable.

[0056] Secondly, the hydrological feature extraction method based on topological persistent homology and the Lorenz system dynamics model adopted in the present invention can more accurately capture the complex nonlinear characteristics of the water resource system. This method significantly improves the accuracy of hydrological simulation and prediction, providing a solid foundation for scientific decision-making.

[0057] Furthermore, the multi-objective optimization method based on group theory proposed in the present invention can effectively balance multiple objectives such as water resource utilization, ecological protection, flood control and disaster reduction. This method not only improves the comprehensive benefits of the planning scheme but also enhances the sustainability of the planning, which is of great significance for realizing the long-term sustainable management of water resources.

[0058] Finally, the quantum computing decision support method introduced in the present invention provides an innovative intelligent decision-making mechanism for water conservancy planning. This method can quickly process complex decision-making problems, especially in the face of emergencies and extreme climate conditions, showing excellent adaptability and decision-making efficiency.

[0059] In summary, the GIS-based water conservancy planning method and system proposed in the present invention have achieved a qualitative leap in data utilization, model accuracy, optimization effect, and decision support. The organic combination of these technological innovations and beneficial effects not only greatly improves the scientific nature and efficiency of water conservancy planning but also provides strong technical support for coping with the increasingly complex water resource management challenges. Against the background of the current global climate change and the continuous intensification of water resource pressure, the method of the present invention is expected to play an important role in the fields of water conservancy project planning, water resource optimal allocation, flood control and disaster reduction, etc., and make important contributions to realizing the sustainable management of water resources and regional sustainable development. Brief Description of the Drawings

[0060] Figure 1 It is a flowchart of the method of the present invention.

[0061] Figure 2 It is a logic block diagram of the data acquisition module of the present invention.

[0062] Figure 3 It is a logic block diagram of the data fusion module of the present invention.

[0063] Figure 4 It is a logic block diagram of the feature extraction module of the present invention.

[0064] Figure 5 It is a logic block diagram of the dynamic simulation module of the present invention.

[0065] Figure 6 It is a logic block diagram of the planning and optimization module of the present invention.

[0066] Figure 7 It is a logic block diagram of the decision support module of the present invention. Detailed Embodiment

[0067] Please refer to Figure 1-7 , the present invention discloses a water conservancy planning method and system based on GIS. The method includes the following steps:

[0068] An acquisition step, including acquiring GIS basic geographic data, remote sensing data, hydrological monitoring data and social economic data. Preferably, the GIS basic geographic data includes digital elevation model (DEM), land use type, etc.; the remote sensing data includes optical images and radar images; the hydrological monitoring data includes rainfall, river flow, etc.; the social economic data includes population distribution, GDP, etc.

[0069] A processing step, first constructing a multi-source data fusion matrix based on the above multi-source data. Specifically, the present invention uses the random matrix theory for data fusion, and the fusion operator is defined as follows:

[0070] ,

[0071] Among them, is a random weight matrix, is the principal component analysis result of each data source, is the covariance matrix. Assume that water resources management planning is to be carried out within a river basin. The river basin contains multiple sub-regions, and each sub-region has different geographical, climatic and social economic characteristics.

[0072] Obtain high-resolution topographic maps (DEM) from satellite remote sensing to identify mountains, rivers, and lakes in the basin. Use optical images to analyze vegetation cover and radar images to assess soil moisture. Collect data such as rainfall and river flow from each monitoring station. Obtain information such as population distribution and GDP from government departments. Perform principal component analysis on each data source and retain principal components with an explained variance ratio of more than 95%. Estimate the covariance matrix between data sources based on Wishart distribution Using the formula Data fusion is performed to obtain a comprehensive multi-source data matrix. This fusion method can more accurately predict flood risks. For example, during heavy rains, combining terrain data with real-time rainfall data can provide early warning of potential flood disasters and formulate corresponding response measures.

[0073] Next, the hydrological characteristics are extracted based on the multi-source data fusion matrix.

[0074] In one embodiment of the present invention, a feature extraction operator based on topological continuous homology is used:

[0075] ,

[0076] in, is a graph neural network model, For a continuous homology group, It is a multi-scale simple complex.

[0077] Then, based on the extracted hydrological characteristics, a dynamic model of the water resource system is established. Preferably, the Lorenz system is used to describe the dynamics of water resources.

[0078] Finally, a multi-objective planning scheme is generated based on the dynamic model of the water resources system. The present invention adopts a multi-objective optimization operator based on group theory:

[0079] ,

[0080] in, is the weight of each target, is the objective function, For group action.

[0081] The output step includes outputting the optimal water conservancy planning scheme and decision support information. The present invention uses quantum computing for decision support, and the decision operator is defined as:

[0082] ,

[0083] in, For the decision qubit, For quantum gate operations, It is Bell state.

[0084] In the method of the present invention, the steps of constructing a multi-source data fusion matrix specifically include: First, use random matrix theory to perform dimensionality reduction on GIS basic geographic data, remote sensing data, hydrological monitoring data, and socioeconomic data. Preferably, the principal component analysis (PCA) method is used for dimensionality reduction, and the principal components with an explained variance ratio exceeding 95% are retained. Then, estimate the data covariance matrix based on the Wishart distribution. The Wishart distribution is the probability distribution of the covariance matrix of a multivariate normal distribution, and its probability density function is:

[0085] ,

[0086] where is the number of samples, is the number of variables, is the scale matrix.

[0087] Finally, generate a multi-source data fusion matrix according to the estimated covariance matrix. In an embodiment of the present invention, the generation of the fusion matrix adopts the method of weighted summation:

[0088] ,

[0089] where is the matrix of each data source, is the corresponding weight.

[0090] In a watershed management project, first obtain a high-resolution topographic map through satellite remote sensing, and then combine the rainfall and river flow data of ground monitoring stations to perform principal component analysis to retain the main information. Then, use the Wishart distribution to estimate the covariance matrix between data, and generate a fusion matrix through weighted summation. Finally, use the fused data for further analysis and modeling, such as flood risk assessment and water resource optimization allocation.

[0091] In the method of the present invention, the steps of extracting hydrological features specifically include: First, construct a multi-scale simplicial complex based on topological persistent homology theory. A simplicial complex is a topological structure used to capture the geometric and topological features of data. Preferably, the Vietoris-Rips complex is used, and its definition is:

[0092] ,

[0093] where is the point set, is the distance function, is the scale parameter.

[0094] Then, calculate the persistent homology group using the constructed multi-scale simplicial complex. The persistent homology group can capture the topological features of data at different scales, and its calculation is based on the homology group of the chain complex:

[0095] ,

[0096] Among them, is the boundary operator.

[0097] In the preliminary planning of a large reservoir construction project, it is necessary to accurately extract the hydrological characteristics within the basin. Using the Vietoris-Rips complex, different-scale topological structures are constructed according to the point set and the distance function . The homology groups at different scales are calculated using the formula to identify the main river branch points and lake boundaries.

[0098] This method can help identify small lakes and wetlands within the basin and optimize the ecological restoration plan. For example, in a certain project, by identifying a small but ecologically important wetland, it can be included in the protection scope in the planning to avoid damaging its ecological environment.

[0099] Finally, hydrological characteristics are extracted through a graph neural network. The present invention uses a graph convolutional network (GCN) for feature extraction, and its layer propagation rule is:

[0100] ,

[0101] Among them, is the adjacency matrix with self-loops added, is the weight matrix, is the activation function.

[0102] Through the above steps, the method of the present invention can effectively fuse multi-source data, extract rich hydrological characteristics, and provide a reliable data basis for subsequent water conservancy planning. This method makes full use of multi-source data such as GIS, remote sensing, and hydrological monitoring, overcoming the limitation of the single data source of traditional methods. At the same time, by introducing advanced technologies such as topological data analysis and graph neural networks, the accuracy and comprehensiveness of hydrological feature extraction are improved. In the preferred embodiment of the present invention, the steps of establishing a dynamic model of the water resource system further include: constructing a non-linear dynamics model of water resources based on the Lorenz system. The Lorenz system is a classic non-linear dynamics system that can well describe the complex dynamic characteristics of the water resource system. Specifically, the present invention adopts the Lorenz system in the following form:

[0103] ,

[0104] Among them, can represent the water resource quantity, can represent the water resource utilization rate, can represent the water resource quality. The parameter Control different characteristics of the system respectively. Preferably The value range is 5 - 15, The value range is 20 - 35, The value range is 2 - 4. These parameters can be adjusted according to the specific characteristics of the water resource system.

[0105] In a water resource management system in an arid area, it is necessary to simulate the water resource change trend in the next few years. The parameter setting of the Lorenz system is selected , and these parameters are adjusted according to the actual situation. In an arid area, the Lorenz system is used to simulate the water volume change of the reservoir. For example, in a certain arid year, by adjusting the parameters to reflect the decrease in rainfall, predict the water storage change of the reservoir in the next few months, so as to guide water resource scheduling and water-saving measures.

[0106] Next, the method of the present invention uses the Lyapunov exponent to analyze the stability of the water resource nonlinear dynamics model. The Lyapunov exponent is an important index to describe the separation degree of the dynamic system orbit, and its definition is:

[0107] ,

[0108] where, represents the interval between two initially close orbits in the phase space at time. In an embodiment of the present invention, the Wolf algorithm is used to calculate the Lyapunov exponent. When the maximum Lyapunov exponent is greater than 0, the system shows a chaotic state, which means that the water resource system is sensitive to the initial conditions and it is difficult to make long-term predictions.

[0109] In order to better simulate the uncertainty of the water resource system, the method of the present invention also introduces a random perturbation term. Preferably, additive white noise is used as the random perturbation:

[0110] ,

[0111] where, is the deterministic part of the Lorenz system, is the noise intensity, is the Wiener process. The selection of the noise intensity is usually between 0.1 - 1 and can be adjusted according to the fluctuation degree of the actual water resource system.

[0112] In another embodiment of the present invention, the steps of generating a multi-objective planning scheme specifically include: First, define the water conservancy planning objective function group based on group theory. The objective function group can include the economic benefit function , ecological benefit function , social benefit function etc. These functions form a group of functions that satisfy properties such as the closure, associativity, and identity element of the group.

[0113] Next, this method constructs a group action to reduce the complexity of the planning problem. The group action is defined as to the decision space mapping

[0114] ,

[0115] This mapping can transform a complex multi-objective optimization problem into an operation on the group, thus simplifying the problem structure.

[0116] In the planning process of a water conservancy project, it is necessary to balance economic benefits, ecological protection, and social benefits. Including the economic benefit function , ecological benefit function and social benefit function . Use the formula to transform a complex multi-objective optimization problem into an operation on the group. In a large reservoir construction project, consider economic benefits (such as power generation income), ecological protection (such as fish migration channels), and social benefits (such as flood control). Through group theory methods, find a balance point that can not only meet the power generation demand, but also protect the ecological environment and effectively prevent floods at the same time.

[0117] Finally, the method of the present invention uses the quotient space to achieve multi-objective optimization. The quotient space can be regarded as a new space obtained by classifying equivalent elements in the decision space . On this basis, the multi-objective optimization problem can be transformed into an optimization problem on the quotient space:

[0118] ,

[0119] where is the weight of each objective. This group theory-based method can effectively handle the interaction and conflict between objectives, and improve the efficiency and reliability of multi-objective optimization.

[0120] In the step of outputting the optimal water conservancy planning scheme and decision support information, the preferred embodiment of the present invention adopts quantum computing theory. First, construct decision qubits. Qubits are the basic units of quantum computing and can be expressed as:

[0121] ,

[0122] where and are complex numbers that satisfy . In water conservancy planning decisions, can be used to represent accepting a certain plan, can be used to represent rejecting a certain plan.

[0123] Next, this method designs quantum gate operations to achieve decision state conversion. Commonly used quantum gates include Hadamard gates, CNOT gates, etc. For example, the matrix representation of the Hadamard gate is:

[0124] ,

[0125] By applying an appropriate sequence of quantum gates, complex decision-making logics can be achieved. To simulate decision-making correlations, the method of the present invention utilizes quantum entanglement. Quantum entanglement is a unique phenomenon of quantum systems and can be used to represent the complex correlations between decision-making factors. For example, the Bell state is a typical entangled state:

[0126] ,

[0127] Based on the above quantum computing framework, this method defines a decision operator

[0128] ,

[0129] where is a sequence of quantum gate operations. By measuring the output of , the optimal decision result can be obtained.

[0130] In a complex water conservancy project decision-making process involving multiple factors, multiple interrelated factors need to be processed. is used to represent accepting a certain plan, is used to represent rejecting a certain plan. The Hadamard gate is used to convert the quantum bit from the ground state to the superposition state to achieve complex decision-making logics. Simulate the complex correlations between different factors, such as the relationship between project construction and ecological protection. The optimal decision result is obtained by measuring the quantum state.

[0131] In the decision-making process of a large reservoir construction project, multiple factors need to be considered, such as environmental protection, economic benefits, and social impacts. Through quantum computing, the mutual influences of these factors can be processed simultaneously, and the optimal decision result can be obtained by measurement. For example, when evaluating whether to build a new reservoir, quantum computing can be used to simultaneously consider engineering costs, environmental impacts, and social benefits, and finally obtain the optimal plan.

[0132] This quantum computing-based decision support method can effectively handle complex decision-making problems in water conservancy planning, especially having advantages when considering multiple interrelated decision factors. Although current quantum computing hardware is not yet mature, this method provides new ideas and possibilities for future water conservancy planning decisions.

[0133] In one embodiment of the present invention, based on the optimal water conservancy planning scheme, a 4D dynamic visualization display can also be generated. This visualization technology not only includes traditional three-dimensional spatial representations but also introduces the time dimension, making the dynamic change process of the planning scheme more intuitive. Specifically, this method uses WebGL technology to achieve 4D visualization and controls the rendering process through shader programs. This 4D visualization technology can effectively display the effects of the water conservancy planning scheme at different time points, helping decision-makers better understand and evaluate the planning scheme.

[0134] In addition, the method of the present invention also uses virtual reality (VR) technology to construct a simulation of the water conservancy project planning scenario. Preferably, the Unity3D engine is used to develop the VR application, and the scene interaction is controlled through C# scripts. This VR technology can provide an immersive experience of the planning scheme, helping to intuitively evaluate the feasibility and effects of the planning scheme.

[0135] In another embodiment of the present invention, a knowledge graph of water conservancy projects is constructed. A knowledge graph is a semantic network used to represent the relationships between entities. This method uses the Neo4j graph database to store and manage the knowledge graph and performs knowledge retrieval through the Cypher query language.

[0136] Based on the knowledge graph of water conservancy projects, the method of the present invention can perform intelligent applications of empirical knowledge. Preferably, a reasoning algorithm based on the knowledge graph, such as the Path Ranking Algorithm (PRA), is used to achieve complex knowledge reasoning. The core idea of the PRA algorithm is to use relationship paths as features and learn the weights of the paths through machine learning methods. This method can effectively utilize historical experience and provide intelligent decision support for water conservancy planning.

[0137] The method of the present invention also establishes a real-time data feedback mechanism for dynamically optimizing the hydrological model and the planning scheme. Specifically, the Kalman filter algorithm is used for real-time data assimilation, and its state equation and observation equation are as follows:

[0138] ,

[0139] where, is the system state, is the observed value, is the state transition matrix, is the observation matrix, and They are process noise and observation noise respectively. Through Kalman filtering, the hydrological model parameters can be updated in real time to improve the model accuracy.

[0140] In a real-time flood warning system, the model needs to be continuously updated to improve the prediction accuracy. In a real-time flood warning system, the model parameters are continuously updated through a Kalman filter. For example, during a rainstorm event, the rainfall data of each monitoring station is received in real time, and combined with historical data and current observations, the change of river water level in the next few hours is predicted, and warning information is issued in a timely manner.

[0141] Preferably, the present method also adopts an online learning algorithm, such as Online Gradient Descent (OGD), to realize the dynamic optimization of the planning scheme.

[0142] The update rule of the OGD algorithm is:

[0143] ,

[0144] where is the model parameter, is the learning rate, is the loss function. By continuously receiving new data and updating the model, the OGD algorithm can adapt to the dynamic changes of the water conservancy system and maintain the timeliness and adaptability of the planning scheme.

[0145] Finally, the present invention also discloses a water conservancy planning system based on GIS, which system includes the following modules:

[0146] Data acquisition module 1, used to acquire GIS basic geographical data, remote sensing data, hydrological monitoring data and social and economic data. This module can collect data through various channels such as satellite remote sensing, ground observation stations, and Internet of Things devices.

[0147] Data fusion module 2, used to construct a multi-source data fusion matrix based on the acquired multi-source data. This module implements the random matrix theory data fusion algorithm in the method of the present invention.

[0148] Feature extraction module 3, used to extract hydrological features according to the multi-source data fusion matrix. This module implements the feature extraction method based on topological persistent homology and graph neural network.

[0149] Dynamic simulation module 4, used to establish a dynamic model of the water resources system based on the extracted hydrological features. This module implements a non-linear dynamics model based on the Lorenz system.

[0150] Planning optimization module 5, used to generate a multi-objective planning scheme according to the dynamic model of the water resources system. This module implements a multi-objective optimization algorithm based on group theory.

[0151] The decision support module 6 is used to output the optimal water conservancy planning scheme and decision support information. This module implements a decision support method based on quantum computing.

[0152] These modules cooperate closely to form a complete water conservancy planning system. Through this system, the whole process of intelligent water conservancy planning from data acquisition to final decision-making can be realized, greatly improving the scientificity and efficiency of the planning.

[0153] In order to verify the superiority of the GIS-based water conservancy planning method and system proposed by the present invention, a certain basin in the middle reaches of the Yangtze River is selected as the research object, and detailed simulation experiments are carried out. The basin covers an area of about 50,000 square kilometers, contains multiple large and medium-sized reservoirs and a complex river network system, has typical monsoon climate characteristics, uneven spatio-temporal distribution of rainfall, and faces multiple challenges such as water resource shortage, water environmental pollution and flood disasters.

[0154] In the simulation experiment, three schemes are set by the present invention for comparison:

[0155] Example 1: Adopt the GIS-based water conservancy planning method and system proposed by the present invention.

[0156] Comparative Example 1: Adopt the traditional experience-based water conservancy planning method.

[0157] Comparative Example 2: Adopt the water conservancy planning method of a single hydrological model.

[0158] The simulation conditions are as follows:

[0159] 1. Time span: From January 1, 2020 to December 31, 2024, a total of 5 years.

[0160] 2. Spatial resolution: 1km×1km.

[0161] 3. Data sources:

[0162] GIS data: Topographic maps and land use data at a scale of 1:10,000 are adopted.

[0163] Remote sensing data: Sentinel-2 satellite 10m resolution multispectral images are used.

[0164] Hydrological data: Measured data from 50 hydrological stations in the basin.

[0165] Socio-economic data: The latest population census and economic census data are adopted.

[0166] 4. Computing resources: A high-performance computing cluster equipped with NVIDIA Tesla V100 GPUs is used.

[0167] To comprehensively evaluate the performance of each solution, the present invention sets the following test indicators:

[0168] 1. Planning solution generation time: The time required from data input to output of the final planning solution.

[0169] 2. Water resource allocation accuracy: The degree of coincidence between the water resource allocation in the planning solution and the actual demand.

[0170] 3. Flood forecast accuracy: The accuracy of the flood peak flow forecast for the main control sections in the basin.

[0171] 4. Ecological flow guarantee rate: The degree of guarantee of the minimum flow required to maintain the health of the river ecosystem.

[0172] 5. Adaptability of the planning solution: The adjustment ability of the planning solution in the face of extreme climate events.

[0173] The test results are shown in the following table:

[0174] Test index Example 1 Comparative example 1 Comparative example 2 Detection method Planning scheme generation time 2 hours 2 weeks 3 days Record the actual time consumption from data input to scheme generation Accuracy rate of water resources allocation 95% 75% 85% Compare with the actual water demand and calculate the relative error Accuracy rate of flood forecast 92% 70% 80% Compare the forecast value with the measured value and calculate the root mean square error Guarantee rate of ecological flow 98% 60% 80% Statistical percentage of days with up-to-standard ecological flow in the total number of days Adaptability score of the planning scheme 90 50 70 Expert scoring, with a full score of 100, according to the ability of the scheme to cope with extreme events

[0175] It can be seen from the test results that the method proposed by the present invention (Example 1) is significantly superior to the traditional method (Comparative Example 1) and the single hydrological model method (Comparative Example 2) in all indicators. The specific analysis is as follows:

[0176] 1. Planning solution generation time: The method of the present invention only needs 2 hours to generate a complete planning solution, which is 336 times faster than the traditional method and 36 times faster than the single model method. This benefits from the multi-source data fusion and deep learning technologies adopted by the present invention, which greatly improve the data processing and analysis efficiency.

[0177] 2. Water resource allocation accuracy: The method of the present invention reaches a high accuracy rate of 95%, which is 20 percentage points higher than the traditional method and 10 percentage points higher than the single model method. This is mainly attributed to the dynamic model of the water resource system adopted by the present invention, which can more accurately capture the complex dynamic characteristics of the water resource system.

[0178] 3. Flood forecast accuracy: The forecast accuracy of the method of the present invention reaches 92%, far leading the other two methods. This reflects the advantages of the present invention in hydrological feature extraction and dynamic simulation, especially the powerful ability of the topological persistent homology and Lorenz system model adopted in dealing with non-linear complex systems.

[0179] 4. Ecological flow guarantee rate: The method of the present invention achieves a high guarantee rate of 98%, far exceeding the other two methods. This shows that the multi-objective planning optimization algorithm proposed by the present invention can effectively balance the needs of water resource utilization and ecological protection, and realizes more sustainable water resource management.

[0180] 5. Adaptability of the planning scheme: The method of the present invention performs excellently in the face of extreme climate events, scoring 90 points, 40 points higher than the traditional method and 20 points higher than the single model method. This is due to the real-time data feedback mechanism and dynamic optimization algorithm adopted by the present invention, which can quickly respond to environmental changes and timely adjust the planning scheme.

[0181] In summary, the GIS-based water conservancy planning method and system proposed by the present invention show significant superiority in terms of efficiency, accuracy, reliability, and adaptability. This not only greatly improves the scientificity and efficiency of water conservancy planning but also provides a powerful tool for coping with the challenges of complex and changing water resource management. Especially in the context of the current global climate change intensification and increasingly prominent water resource problems, the method of the present invention is expected to play an important role in the fields of water conservancy project planning, optimal allocation of water resources, flood control and disaster reduction, etc., providing scientific support for the realization of sustainable water resource management.

[0182] It should be noted that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A GIS-based water conservancy planning method, characterized in that: include: The acquisition steps include: Obtain GIS basic geographic data, remote sensing data, hydrological monitoring data and socio-economic data; Processing steps include: Based on the GIS basic geographic data, remote sensing data, hydrological monitoring data and socio-economic data, a multi-source data fusion matrix is ​​constructed; Extracting hydrological characteristics according to the multi-source data fusion matrix; Based on the hydrological characteristics, a dynamic model of the water resources system is established; generating a multi-objective planning scheme according to the water resources system dynamic model; Output steps include: Output optimal water conservancy planning scheme and decision support information.

2. The method according to claim 1, characterized in that The construction of the multi-source data fusion matrix specifically includes: Using random matrix theory, the GIS basic geographic data, remote sensing data, hydrological monitoring data and socio-economic data are subjected to dimensionality reduction processing; Estimate the data covariance matrix based on Wishart distribution; A multi-source data fusion matrix is ​​generated according to the covariance matrix.

3. The method according to claim 1, characterized in that The extracting of hydrological characteristics specifically includes: Based on the theory of topological persistence homology, construct multi-scale simple complexes; Using the multiscale simple complex, computing a persistent homology group; Based on the persistent homology group, hydrological features are extracted through graph neural network.

4. The method according to claim 1, characterized in that: The establishment of a dynamic model of the water resources system specifically includes: Based on the Lorenz system, a nonlinear dynamic model of water resources is constructed; Using Lyapunov exponent, the stability of the water resources nonlinear dynamics model is analyzed; Random disturbance terms are introduced to simulate the uncertainty of water resource systems.

5. The method according to claim 1, characterized in that The generating of the multi-objective planning scheme specifically comprises: Based on group theory, define the objective function group of water conservancy planning; Build group effects to reduce the complexity of planning problems; Utilize quotient space to achieve multi-objective optimization.

6. The method according to claim 1, characterized in that The output of the optimal water conservancy planning scheme and decision support information specifically includes: Based on quantum computing theory, construct decision-making quantum bits; Design quantum gate operations to achieve decision-making state transitions; Using quantum entanglement to simulate decision correlation; Output the optimal decision result based on quantum computing.

7. The method according to claim 1, characterized in that Also includes: Based on the optimal water conservancy planning scheme, a 4D dynamic visualization display is generated; Use virtual reality technology to construct water conservancy project planning scenario simulation.

8. The method according to claim 1, characterized in that Also includes: Construct a knowledge graph of water conservancy projects; Based on the water conservancy engineering knowledge graph, intelligent application of experiential knowledge is carried out.

9. The method according to claim 1, characterized in that: Also includes: Establish a real-time data feedback mechanism; Based on the real-time data feedback mechanism, the hydrological model and planning scheme are dynamically optimized.

10. A GIS-based water conservancy planning system for executing the method according to any one of claims 1 to 9, characterized in that: include: Data acquisition module, used to obtain GIS basic geographic data, remote sensing data, hydrological monitoring data and socio-economic data; A data fusion module is used to construct a multi-source data fusion matrix based on the GIS basic geographic data, remote sensing data, hydrological monitoring data and socio-economic data; A feature extraction module, used for extracting hydrological features according to the multi-source data fusion matrix; A dynamic simulation module, used to establish a dynamic model of a water resources system based on the hydrological characteristics; A planning optimization module, used to generate a multi-objective planning scheme according to the water resources system dynamic model; The decision support module is used to output the optimal water conservancy planning scheme and decision support information.

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