Huaihe River basin water resource bearing capacity evaluation system based on deep learning

Abstract

The invention provides a Huaihe River basin water resource bearing capacity evaluation system based on deep learning, and relates to the field of evaluation systems. The Huaihe River basin water resource bearing capacity evaluation system based on deep learning comprises a multi-source data acquisition function, an influence factor acquisition function, a data processing function, an evaluation system construction function, a deep model learning function and a bearing capacity evaluation output function, and the multi-source data acquisition function is used for acquiring water resource related data of the Huaihe River basin; and the influence factor acquisition function is used for acquiring influence factors of geographical, humanistic and economic factors of the Huaihe River flow-through area on the water resources of the Huaihe River basin. The method can predict the supply and demand of the water resources according to the current situation and change trend of the water resources in the Huaihe River basin in combination with the social and economic development plan, and achieves the optimal distribution of the water resources through an optimization algorithm on the premise of meeting the demands of life, production and ecological water.

Description

Technical Field

[0001] The present invention relates to the field of evaluation systems, and specifically to a water resource carrying capacity evaluation system for the Huaihe River Basin based on deep learning. Background Art

[0002] Water resources are the basic natural resources for the survival and development of human society. Their rational utilization and sustainable development play a key role in regional sustainable development. The Huaihe River Basin, as an important grain-producing area, energy base and transportation channel in my country, occupies a vital position in the national economy and social development. However, the Huaihe River Basin is densely populated, and the per capita water resources are far below the national average. It is a severely water-scarce area. With the rapid development of the economy and society, the demand for water resources continues to increase, and the contradiction between supply and demand is becoming increasingly prominent. Research on the water resource carrying capacity of the Huaihe River Basin can provide an important basis for the rational planning of water resource utilization and the formulation of scientific water resource management policies, which will help alleviate the contradiction between water supply and demand and ensure the domestic water supply of residents in the basin and the sustainable development of the economy and society.

[0003] Therefore, the present invention proposes a water resources carrying capacity assessment system for the Huaihe River Basin based on deep learning, so as to effectively solve the above-mentioned troubles and problems. Summary of the Invention

[0004] (1) Technical problems solved

[0005] In response to the shortcomings of the existing technology, the present invention provides a deep learning-based water resource carrying capacity assessment system for the Huaihe River Basin. The established deep learning-based water resource carrying capacity assessment system for the Huaihe River Basin is used to assess the current water resource carrying capacity of the Huaihe River Basin. The supply and demand of water resources can be predicted based on the current status and changing trends of water resources in the Huaihe River Basin, combined with social and economic development plans. Through optimization algorithms, the optimal allocation of water resources can be achieved while meeting the water needs of life, production and ecology. At the same time, for areas with water shortages, the system can recommend giving priority to domestic water use, reasonably allocating industrial and agricultural water use, and improving the efficiency of water resource utilization.

[0006] (2) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a Huaihe River Basin water resources carrying capacity assessment system based on deep learning, characterized in that: the Huaihe River Basin water resources carrying capacity assessment system includes multi-source data collection function, influencing factor collection function, data processing function, assessment system construction function, deep model learning function and carrying capacity assessment output function;

[0008] The multi-source data acquisition function is used to obtain water resources related data in the Huaihe River Basin;

[0009] The influencing factor collection function is used to obtain the influencing factors of the geographical, human and economic factors of the area through which the Huaihe River flows on the water resources of the Huaihe River basin;

[0010] The data processing function is used to pre-process and quality control the collected data;

[0011] The evaluation system construction function constructs an indicator system for the water resources carrying capacity of the Huaihe River Basin through the three dimensions of water resources, social economy and ecological environment;

[0012] The deep learning model uses automatic learning indicator weights to intelligently evaluate the contribution of the three-dimensional indicators of water resources, social economy, and ecological environment to the water resource carrying capacity of the Huaihe River Basin through neural network computing;

[0013] The carrying capacity assessment output function is used to output the Huaihe River Basin water resources carrying capacity conclusion report obtained by the assessment system.

[0014] Preferably, the data collected by the multi-source data acquisition function includes historical monitoring data of hydrological stations in the Huaihe River Basin, satellite remote sensing monitoring data of the Huaihe River Basin, and social and economic data of cities, counties and districts in the Huaihe River Basin;

[0015] Through the above technical solution, the scope of data collection includes precipitation, runoff, groundwater level and groundwater extraction in monitoring wells statistically recorded by each hydrological station in the Huaihe River Basin, county GDP, population, irrigated area and forest coverage, etc.

[0016] Preferably, the influencing factors collected by the influencing factor collection function include water resource endowment, socio-economic needs, and ecological and environmental factors. The core indicators of water resource endowment include precipitation, runoff depth, and groundwater reserves. The core indicators of socio-economic needs include population density and water consumption. The core indicators of ecological and environmental factors include water quality analogy, ecological water demand satisfaction rate, and sewage treatment rate.

[0017] Through the above technical solutions, water resource endowment among the influencing factors can directly determine the water resource supply capacity. The increase in pressure on the socio-economic demand side may lead to the risk of overload of the water resource carrying capacity in the Huaihe River Basin. The deterioration of water quality among the ecological and environmental factors will reduce the available water volume. The advancement of irrigation technology will improve the efficiency of water resource utilization.

[0018] Preferably, the data processing function deletes and repairs duplicate, erroneous and missing data in the original data collected by the multi-source data acquisition function, and normalizes indicators of different dimensions;

[0019] Through the above technical solution, data processing can delete outliers and duplicate data in positive hydrological data, ensure the uniqueness of socioeconomic data, and ensure that the error of key indicators is controlled within the range of ±3% after comparing the measured data of the hydrological station with the model inversion results, and the proportion of missing values ​​in the recorded data is controlled below 5% to ensure sufficient sample size.

[0020] Preferably, the water resource indicators in the evaluation system construction function include per capita water resources, groundwater development intensity, water resource development and utilization rate, and irrigation water utilization coefficient; the socioeconomic indicators include economic development coefficient, water consumption per 10,000 yuan of GDP, population living density, and urban domestic water consumption; and the ecological environment indicators include section water quality compliance rate, groundwater quality category, ecological flow guarantee rate, and vegetation coverage.

[0021] Preferably, the deep learning model function maps the three-dimensional indicators of water resources, social economy and ecological environment into high-dimensional vectors to generate a weight matrix. The weight matrix calculation formula is:

[0022]

[0023] Where hi, hj are indicator features, Q is the query vector

[0024] The historical monitoring data of hydrological stations in the Huaihe River Basin, the satellite remote sensing monitoring data of the Huaihe River Basin, and the socioeconomic data indicators of cities and counties in the Huaihe River Basin were standardized into the interval [-1, 1]. A fully connected neural network was built using the PyTorch framework, and the three-dimensional weight vector was output for training and optimization. The mean square error between the comprehensive carrying capacity index and the expert score was used as the loss function. The optimizer was iterated and trained hundreds of times until the weight converged to ≤0.01.

[0025] Preferably, the mobile APP with carrying capacity assessment output function supports remote monitoring, parameter adjustment, and fault warning functions, and the output content includes analysis of water resources in the Huaihe River Basin, assessment of water resource carrying capacity, and risk warning;

[0026] Through the above technical solutions, the assessment and analysis functions calculate the carrying capacity of each region in the Huaihe River Basin, conduct spatial analysis, and simulate scenarios, and display the results through thematic map production, three-dimensional visualization, and report generation. At the same time, risk warning information can be issued in a timely manner to remind management departments to take corresponding measures for regulation to ensure the sustainable use of water resources.

[0027] Working Principle: The deep learning-based Huaihe River Basin water resources carrying capacity assessment method has the following steps:

[0028] Step 1: Data input. Enter the system's data input page and enter the water resources, social economy, and ecological environment data for the cities and counties along the Huaihe River. In the water resources data input area, enter precipitation, runoff, and total water resources. In the socioeconomic data input area, fill in population, GDP, and industrial structure. In the ecological environment data input area, enter ecological water demand, water quality, and vegetation coverage.

[0029] Step 2: The deep learning model runs and the data is sent to the backend service. The backend service calls the deep learning model Transformer to integrate spatiotemporal features, capturing the interannual variability of runoff and water use in the spatiotemporal dimension and the hydraulic connections between sub-basins in the spatial dimension. This identifies the cross-regional impact of insufficient precipitation upstream, increased irrigation water use downstream, and a chain reaction of reduced carrying capacity. Based on the input data, the model combines characteristics and patterns to assess the water resource carrying capacity of counties and cities along the Huaihe River.

[0030] Step 3: Output of assessment results. The system displays the assessment results on the results display page, and uses bar charts and line graphs to show the changing trends of indicators related to water resource carrying capacity, such as the changes in water resource carrying capacity over time and the comparison of water resource development and utilization rates in different years. It also generates assessment results and water resource management recommendations and risk warning reports for each area through which the water flows.

[0031] (3) Beneficial effects

[0032] This invention provides a deep learning-based water resource carrying capacity assessment system for the Huaihe River Basin. It has the following beneficial effects:

[0033] 1. The present invention provides a deep learning-based water resource carrying capacity assessment system for the Huaihe River Basin. The established deep learning-based water resource carrying capacity assessment system for the Huaihe River Basin is used to assess the current water resource carrying capacity of the Huaihe River Basin. Based on the current status and changing trends of water resources in the Huaihe River Basin and combined with social and economic development plans, the supply and demand of water resources can be predicted. Through optimization algorithms, the optimal allocation of water resources can be achieved while meeting the water needs of life, production, and ecology. At the same time, for areas with water shortages, the system can recommend giving priority to domestic water use, reasonably allocating industrial and agricultural water use, and improving the efficiency of water resource utilization.

[0034] 2. The present invention provides a water resource carrying capacity assessment system for the Huaihe River Basin based on deep learning. In terms of water resource management, the system can monitor the development and utilization of water resources in real time, promptly discover problems such as over-exploitation of water resources and water pollution, and issue early warnings. By connecting with data from hydrological monitoring stations and water quality monitoring stations, the system can obtain dynamic information on water resources in real time. When the development and utilization rate of water resources exceeds a reasonable threshold or the water quality shows a deterioration trend, the system can promptly issue early warning information to remind management departments to take corresponding measures for regulation to ensure the sustainable use of water resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a system architecture diagram of the Huaihe River Basin water resources carrying capacity assessment system based on deep learning of the present invention. DETAILED DESCRIPTION

[0036] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention, in conjunction with the accompanying drawings. It should be noted that the described embodiments are only some of the embodiments of the present invention, and are not intended to be exhaustive. Based on the embodiments of the present invention, all other embodiments derived by persons of ordinary skill in the art without inventive effort are within the scope of protection of the present invention. It should be noted that the terms used herein are intended solely to describe specific embodiments and are not intended to limit the exemplary embodiments of the present invention. For ease of description, the dimensions of the various parts shown in the drawings are not drawn to scale. Technologies, methods, and devices known to persons of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, such technologies, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely illustrative and not limiting. Therefore, other examples of the exemplary embodiments may have different values. It should be noted that similar reference numerals and letters denote similar items in the following figures. Therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0037] Example 1:

[0038] like Figure 1 As shown, a Huaihe River Basin water resources carrying capacity assessment system based on deep learning is characterized in that: the Huaihe River Basin water resources carrying capacity assessment system includes multi-source data collection function, influencing factor collection function, data processing function, assessment system construction function, deep model learning function and carrying capacity assessment output function;

[0039] The multi-source data acquisition function is used to obtain water resources related data in the Huaihe River Basin;

[0040] The influencing factor collection function is used to obtain the influencing factors of the geographical, human and economic factors of the area through which the Huaihe River flows on the water resources of the Huaihe River basin;

[0041] The data processing function is used to pre-process and quality control the collected data;

[0042] The evaluation system construction function constructs an indicator system for the water resources carrying capacity of the Huaihe River Basin through the three dimensions of water resources, social economy and ecological environment;

[0043] The deep learning model uses automatic learning indicator weights to intelligently evaluate the contribution of the three-dimensional indicators of water resources, social economy, and ecological environment to the water resource carrying capacity of the Huaihe River Basin through neural network computing;

[0044] The carrying capacity assessment output function is used to output the Huaihe River Basin water resources carrying capacity conclusion report obtained by the assessment system.

[0045] Example 2:

[0046] like Figure 1 As shown, the data collected by the multi-source data collection function include historical monitoring data of hydrological stations in the Huaihe River Basin, satellite remote sensing monitoring data of the Huaihe River Basin, and socioeconomic data of cities and counties in the Huaihe River Basin. The scope of data collection includes precipitation, runoff, groundwater level and groundwater extraction of monitoring wells recorded by each hydrological station in the Huaihe River Basin, county GDP, population, irrigated area and forest coverage, etc. The influencing factors collected by the influencing factor collection function include water resource endowment, socioeconomic demand, and ecological and environmental factors. The core indicators of water resource endowment include precipitation, runoff depth, and groundwater reserves. The core indicators of socioeconomic demand include population density and water consumption. The core indicators of ecological and environmental factors include water quality analogy, ecological water demand satisfaction rate and sewage treatment rate. Among the influencing factors, water resource endowment can directly determine the water resource supply capacity. The increase in pressure on the socioeconomic demand side may lead to the risk of overload of the water resource carrying capacity in the Huaihe River Basin. Among the ecological and environmental factors, water quality deterioration reduces the available water volume, and advances in irrigation technology improve the efficiency of water resource utilization.

[0047] The data processing function deletes and repairs duplicate, erroneous, and missing data in the original data collected by the multi-source data acquisition function, and normalizes indicators of different dimensions. Data processing can delete outliers and duplicate data in the positive hydrological data to ensure the uniqueness of the socioeconomic data, and ensure that the error of key indicators is controlled within the range of ±3% after comparing the measured data of the hydrological station with the model inversion results, and the proportion of missing values ​​in the recorded data is controlled below 5% to ensure sufficient sample size. The water resource indicators in the evaluation system construction function include per capita water resources, groundwater development intensity, water resource development and utilization rate, and irrigation water utilization coefficient. The socioeconomic indicators include economic development coefficient, water consumption per 10,000 yuan of GDP, population living density, and urban domestic water consumption. The ecological environment indicators include section water quality compliance rate, groundwater quality category, ecological flow guarantee rate, and vegetation coverage. The deep learning model function maps the three-dimensional indicators of water resources-socioeconomic-ecological environment into a high-dimensional vector to generate a weight matrix. The weight matrix calculation formula is:

[0048]

[0049] Where hi, hj are indicator features, Q is the query vector

[0050] The historical monitoring data of hydrological stations in the Huaihe River Basin, the satellite remote sensing monitoring data of the Huaihe River Basin, and the socioeconomic data indicators of cities and counties in the Huaihe River Basin were standardized into the interval [-1, 1]. A fully connected neural network was built using the PyTorch framework, and the three-dimensional weight vector was output for training and optimization. The mean square error between the comprehensive carrying capacity index and the expert score was used as the loss function. The optimizer was iterated and trained hundreds of times until the weight converged to ≤0.01.

[0051] The mobile APP with the carrying capacity assessment output function supports remote monitoring, parameter adjustment, and fault warning functions. The output content includes analysis of water resources in the Huaihe River Basin, assessment of water resource carrying capacity and risk warning. The assessment and analysis function calculates the carrying capacity of each region in the Huaihe River Basin, performs spatial analysis, and simulates scenarios, and displays the results through thematic map production, three-dimensional visualization, and report generation. At the same time, risk warnings can issue warning information in a timely manner to remind management departments to take corresponding measures for regulation to ensure the sustainable use of water resources.

[0052] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A deep learning-based Huaihe River Basin water resources carrying capacity assessment system, characterized by: The Huaihe River Basin Water Resources Carrying Capacity Assessment System includes multi-source data collection, influencing factor collection, data processing, assessment system construction, deep model learning, and carrying capacity assessment output functions. The multi-source data acquisition function is used to obtain water resources related data in the Huaihe River Basin; The influencing factor collection function is used to obtain the influencing factors of the geographical, human and economic factors of the area through which the Huaihe River flows on the water resources of the Huaihe River basin; The data processing function is used to pre-process and quality control the collected data; The evaluation system construction function constructs an indicator system for the water resources carrying capacity of the Huaihe River Basin through the three dimensions of water resources, social economy and ecological environment; The deep learning model uses automatic learning indicator weights to intelligently evaluate the contribution of the three-dimensional indicators of water resources, social economy, and ecological environment to the water resource carrying capacity of the Huaihe River Basin through neural network computing; The carrying capacity assessment output function is used to output the Huaihe River Basin water resources carrying capacity conclusion report obtained by the assessment system.

2. The Huaihe River Basin Water Resources Carrying Capacity Assessment System Based on Deep Learning according to claim 1 is characterized by: The data collected by the multi-source data acquisition function include historical monitoring data of hydrological stations in the Huaihe River Basin, satellite remote sensing monitoring data of the Huaihe River Basin, and social and economic data of cities, counties and districts in the Huaihe River Basin.

3. The Huaihe River Basin Water Resources Carrying Capacity Assessment System Based on Deep Learning according to claim 1 is characterized by: The influencing factors collected by the influencing factor collection function include water resource endowment, socio-economic needs, and ecological and environmental factors. The core indicators of water resource endowment include precipitation, runoff depth, and groundwater reserves. The core indicators of socio-economic needs include population density and water consumption. The core indicators of ecological and environmental factors include water quality analogy, ecological water demand satisfaction rate, and sewage treatment rate.

4. The Huaihe River Basin water resources carrying capacity assessment system based on deep learning according to claim 1 is characterized by: The data processing function deletes and repairs duplicate, erroneous and missing data in the original data collected by the multi-source data acquisition function, and normalizes indicators of different dimensions.

5. The Huaihe River Basin water resources carrying capacity assessment system based on deep learning according to claim 1 is characterized by: The water resource indicators in the evaluation system construction function include per capita water resources, groundwater development intensity, water resource development and utilization rate, and irrigation water utilization coefficient; the socio-economic indicators include economic development coefficient, water consumption per 10,000 yuan of GDP, population living density, and urban domestic water consumption; the ecological environment indicators include section water quality compliance rate, groundwater quality category, ecological flow guarantee rate, and vegetation coverage.

6. The Huaihe River Basin Water Resources Carrying Capacity Assessment System Based on Deep Learning according to claim 1 is characterized by: The deep learning model function maps the three-dimensional indicators of water resources, social economy and ecological environment into high-dimensional vectors to generate a weight matrix. The weight matrix calculation formula is: Where hi, hj are indicator features, Q is the query vector The historical monitoring data of hydrological stations in the Huaihe River Basin, the satellite remote sensing monitoring data of the Huaihe River Basin, and the socioeconomic data indicators of cities and counties in the Huaihe River Basin were standardized into the interval [-1, 1]. A fully connected neural network was built using the PyTorch framework, and the three-dimensional weight vector was output for training and optimization. The mean square error between the comprehensive carrying capacity index and the expert score was used as the loss function. The optimizer was iterated and trained hundreds of times until the weight converged to ≤0.

01.

7. The Huaihe River Basin Water Resources Carrying Capacity Assessment System Based on Deep Learning according to claim 1, characterized in that: The mobile APP with carrying capacity assessment output function supports remote monitoring, parameter adjustment, and fault warning functions. The output content includes analysis of water resources in the Huaihe River Basin, assessment of water resource carrying capacity, and risk warning.

8. A deep learning-based method for assessing the water resources carrying capacity of the Huaihe River Basin, characterized by: The steps of the method include: Step 1: Data input. Enter the system's data input page and enter the water resources, social economy, and ecological environment data for the cities and counties along the Huaihe River. In the water resources data input area, enter precipitation, runoff, and total water resources. In the socioeconomic data input area, fill in population, GDP, and industrial structure. In the ecological environment data input area, enter ecological water demand, water quality, and vegetation coverage. Step 2: The deep learning model runs and the data is sent to the backend service. The backend service calls the deep learning model Transformer to integrate spatiotemporal features, capturing the interannual variability of runoff and water use in the spatiotemporal dimension and the hydraulic connections between sub-basins in the spatial dimension. This identifies the cross-regional impact of insufficient precipitation upstream, increased irrigation water use downstream, and a chain reaction of reduced carrying capacity. Based on the input data, the model combines characteristics and patterns to assess the water resource carrying capacity of counties and cities along the Huaihe River. Step 3: Output of assessment results. The system displays the assessment results on the results display page, and uses bar charts and line graphs to show the changing trends of indicators related to water resource carrying capacity, such as the changes in water resource carrying capacity over time and the comparison of water resource development and utilization rates in different years. It also generates assessment results and water resource management recommendations and risk warning reports for each area through which the water flows.

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