Water environment resource bearing capacity assessment method and system

Abstract

The invention relates to the technical field of water resource management, in particular to a water environment resource bearing capacity assessment method and system, and the method comprises the following steps: synchronously recording rainfall and water inventory based on water quality index data collected by a water quality monitoring station, analyzing the correlation degree between data sets, recognizing key factors affecting the water resource bearing capacity, and calculating the water environment resource bearing capacity. And obtaining a key influence identification result. According to the method, the identification precision of the water resource use mode and the dynamic prediction of the bearing capacity are improved by refining the analysis of the seasonal mode and optimizing the real-time data monitoring, and the adaptability and prediction accuracy of the dynamic change of the water resource are enhanced by adopting the vector autoregression model and the STL time sequence decomposition algorithm; the decision-making process is more reasonable, through the nonlinear programming model and real-time data, the water resource configuration is optimized, the conflict between supply and demand is effectively solved, and the water utilization efficiency is improved, so that the sustainable utilization of water resources is maintained under the influence of population growth and climate change.

Description

Technical Field

[0001] The present invention relates to the technical field of water resource management, and in particular to a method and system for evaluating the carrying capacity of water environment resources. Background Art

[0002] Water resources management is a comprehensive technical field for the protection, development, allocation and optimal use of water resources. It focuses on the sustainable management of water resources to support agriculture, industry, residents' lives and the health of ecosystems. The core goal of water resources management is to ensure that water resources can meet current and future needs in terms of quality and quantity. To achieve this goal, this field involves water resources monitoring, water quality analysis, water quantity regulation, sewage treatment and reuse technology, and the formulation of water policies and regulations.

[0003] Among them, the water environment resource carrying capacity assessment method is a method used to assess how much human activities a specific water body can support without damaging the water environment. This assessment helps decision makers understand the scale of population and industrial activities that a region's water resources can withstand without affecting water quality and the health of the water ecosystem. The uses of this method include planning urban development, formulating environmental protection measures, optimizing water resource allocation and preventing environmental degradation. Through accurate assessment of water resource carrying capacity, it can effectively guide the rational use and protection of water resources and ensure the long-term sustainability of the water environment.

[0004] Existing technologies show insufficient flexibility and limited predictive capabilities when dealing with water resource data with high variability and strong seasonality, especially in extreme climate events or dealing with irregular seasonal changes. Traditional methods use fixed patterns in seasonal change analysis, which limits their effectiveness. Existing water resource allocation strategies are often based on static or outdated data, resulting in an inability to effectively respond to changing environmental conditions, such as insufficient resource allocation during extreme droughts or floods, leading to uneven and inefficient resource distribution, and even inability to conduct effective emergency responses under extreme conditions, affecting the overall effectiveness and sustainability of water resource management. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a method and system for evaluating the carrying capacity of water environment resources.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme: a method for evaluating the carrying capacity of water environment resources, comprising the following steps:

[0007] S1: Based on the water quality index data collected by the water quality monitoring station, the rainfall and reservoir capacity are recorded synchronously, the correlation between the data sets is analyzed, the key factors affecting the water resources carrying capacity are identified, and the key impact identification results are obtained;

[0008] S2: Based on the key impact identification results, a vector autoregression model is used to perform seasonal pattern analysis, identify water resource use patterns, and predict the water resource carrying capacity in future seasons to obtain seasonal carrying capacity prediction indicators;

[0009] S3: Based on the seasonal carrying capacity prediction index, the STL time series decomposition algorithm is used to refine the water resource use pattern of each season, analyze the impact of seasonal changes on water resource carrying capacity, and obtain a seasonal impact analysis matrix;

[0010] S4: Based on the seasonal impact analysis matrix, water resources management is carried out, including reservoir water regulation and sewage reuse, management implementation effects and real-time data are monitored, and long-term impacts and benefits are evaluated to obtain resource management benefit information;

[0011] S5: Based on the resource management benefit information, extract water quality improvement and water use efficiency data, compare the differences before and after management, use a nonlinear programming model to optimize water resource allocation, handle supply and demand conflicts, and obtain a resource allocation optimization diagram;

[0012] S6: Based on the resource allocation optimization map, the water resource carrying capacity is evaluated in the long term. In combination with climate change, population growth and industrial development, the sustainable use of water resources in the future is analyzed to obtain water resource planning results.

[0013] As a further scheme of the present invention, the key impact identification results include water pollution index, rainfall frequency, and water storage capacity; the seasonal carrying prediction indicators include high demand season index, low demand season index, and resource supply volatility; the seasonal impact analysis matrix includes seasonal change sensitivity, resource utilization, and seasonal carrying pressure; the resource management benefit information includes water regulation success rate, reuse water quality indicators, and monitoring response time; the resource allocation optimization diagram includes optimized water use efficiency, conflict reduction ratio, and resource reallocation quota.

[0014] As a further solution of the present invention, based on the water quality index data collected by the water quality monitoring station, the rainfall and reservoir capacity are recorded synchronously, the correlation between the data sets is analyzed, and the key factors affecting the water resources carrying capacity are identified. The steps of obtaining the key influence identification results are specifically as follows:

[0015] S101: Based on the water quality index data collected by the water quality monitoring station, the rainfall and reservoir capacity are recorded synchronously, a data set is created, and the data is processed in a time series to generate a water resource status record;

[0016] S102: Based on the water resource status record, perform data processing, exclude abnormal values ​​and missing information in the data, and verify the validity of the data to obtain a data quality overview;

[0017] S103: Based on the data quality overview, correlation analysis is performed to identify and record key influencing factors of water resources carrying capacity, and obtain key impact identification results.

[0018] As a further solution of the present invention, based on the key impact identification results, a vector autoregression model is used to perform seasonal pattern analysis, identify water resource usage patterns, and predict the water resource carrying capacity of future differential seasons. The steps of obtaining seasonal carrying capacity prediction indicators are specifically as follows:

[0019] S201: Based on the key impact identification results, use a vector autoregression model to sort and adjust the data, perform seasonal analysis, analyze the data points of seasonal changes, and verify the consistency of the seasonal pattern to generate a seasonal pattern record;

[0020] S203: Based on the seasonal pattern record, use historical data to perform seasonal forecasting, adjust the forecasting process, and compare the historical seasonal data with the forecasting result to obtain a forecast adjustment feedback result;

[0021] S203: Based on the prediction adjustment feedback result, the water resource carrying capacity of different seasons in the future is predicted, and the different water resource use patterns are matched to obtain seasonal carrying prediction indicators.

[0022] As a further solution of the present invention, based on the seasonal carrying capacity prediction index, the STL time series decomposition algorithm is used to refine the water resource usage pattern of each season, analyze the impact of seasonal changes on water resource carrying capacity, and obtain the seasonal impact analysis matrix in the following steps:

[0023] S301: Based on the seasonal load prediction index, the original data is time-marked using the STL time series decomposition algorithm to distinguish the water resource usage data of each season, and the seasonal data is classified and marked to generate a seasonal data classification result;

[0024] S302: Based on the seasonal data classification results, each seasonal data is analyzed by comparing data points, the water resource usage pattern of each season is identified, and a usage pattern matrix is ​​generated;

[0025] S303: Based on the usage pattern matrix, the seasonal data is analyzed in detail, the changes in the data of each season are compared, and seasonal influencing factors are recorded to obtain a seasonal impact analysis matrix.

[0026] As a further solution of the present invention, based on the seasonal impact analysis matrix, water resources management is performed, including reservoir water regulation and sewage reuse, management implementation effects and real-time data are monitored, and long-term impacts and benefits are evaluated. The steps of obtaining resource management benefit information are specifically as follows:

[0027] S401: Based on the seasonal impact analysis matrix, adjust the water volume of the reservoir, dynamically monitor the reservoir volume, perform real-time data recording, and generate a reservoir operation log;

[0028] S402: Based on the reservoir operation log, evaluate the current sewage treatment effect, adjust key operating parameters in the sewage treatment process, monitor the adjusted data, and generate sewage treatment update information;

[0029] S403: Based on the sewage treatment update information, evaluate the long-term impact of water resource management implementation, record management effects and real-time monitoring data, analyze long-term trends and changes, and obtain resource management benefit information.

[0030] As a further solution of the present invention, based on the resource management benefit information, water quality improvement and water use efficiency data are extracted, the differences before and after management are compared, a nonlinear programming model is used to optimize water resource allocation, and supply and demand conflicts are handled. The steps of obtaining a resource allocation optimization diagram are specifically as follows:

[0031] S501: Based on the resource management benefit information, by screening key indicators, extracting data on water quality improvement and water use efficiency, and performing difference comparison analysis, identifying change points before and after management, and generating efficiency comparison data;

[0032] S502: Based on the efficiency comparison data, a nonlinear programming model is used to determine the imbalance point of resource allocation through quantitative analysis, and adjustment and optimization operations are performed to process supply and demand requirements and generate a deployment configuration summary;

[0033] S503: Based on the allocation configuration summary, by monitoring the real-time data flow and usage pattern, the resource flow direction and allocation ratio are adjusted to optimize the allocation configuration of water resources and obtain a resource allocation optimization diagram.

[0034] As a further solution of the present invention, the nonlinear programming model is according to the formula:

[0035] ZD=(c T +αd T )x

[0036] The optimal solution ZD for resource allocation is captured, where c represents the resource cost or benefit coefficient, x represents the resource allocation vector, α is the resource cost adjustment coefficient, d is the seasonal adjustment factor, and T represents the transposition operation.

[0037] As a further solution of the present invention, based on the resource allocation optimization map, the water resource carrying capacity is evaluated in the long term, and the sustainable use of water resources in the future is analyzed in combination with climate change, population growth and industrial development. The specific steps for obtaining the water resource planning results are as follows:

[0038] S601: Based on the resource allocation optimization map, the impact of climate change, population growth and industrial development is evaluated through long-term data collection and analysis operations to generate an environmental impact overview;

[0039] S602: Based on the environmental impact overview, evaluate future water resource use and demand forecasts through data integration and pattern recognition, analyze resource sustainability, and generate a sustainable water use plan;

[0040] S603: Based on the sustainable water use plan, analyze the interaction of multiple environmental and development factors, evaluate the long-term impact on water resources carrying capacity, and generate water resources planning results.

[0041] A water environment resource carrying capacity assessment system, the water environment resource carrying capacity assessment system is used to execute the water environment resource carrying capacity assessment method, the system comprises:

[0042] The water quality monitoring and analysis module is based on the water quality index data collected by the water quality monitoring station, records rainfall and reservoir capacity at the same time, analyzes the correlation between data sets, identifies the key factors affecting the water resources carrying capacity, and generates key impact identification results;

[0043] The seasonal pattern analysis module uses a vector autoregression model to perform pattern analysis on seasonal impacts based on the key impact identification results, predicts the water resource carrying capacity of different seasons, and obtains seasonal carrying capacity prediction indicators;

[0044] The seasonal impact analysis module uses the seasonal carrying capacity prediction index and applies the STL time series decomposition algorithm to refine the water resource use pattern in each season, analyze the impact of seasonal changes on the water resource carrying capacity, and obtain a seasonal impact analysis matrix;

[0045] The resource management execution module regulates the water volume of the reservoir, implements wastewater reuse, monitors the implementation effect and real-time data, and evaluates the long-term impact and benefits based on the seasonal impact analysis matrix to obtain resource management benefit information;

[0046] The long-term water resources planning module extracts water quality improvement and water use efficiency data based on the resource management benefit information, combines climate change, population growth and industrial development data, optimizes water resource allocation and use, handles supply and demand conflicts, and analyzes the sustainable use of water resources in the future to obtain water resources planning results.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, by refining the analysis of seasonal patterns and optimizing real-time data monitoring, the recognition accuracy of water resource usage patterns and the dynamic prediction of carrying capacity are improved. The vector autoregression model and STL time series decomposition algorithm are used to enhance the adaptability and prediction accuracy to the dynamic changes of water resources, making the decision-making process more reasonable. Through nonlinear programming models and real-time data, water resource allocation is optimized, supply and demand conflicts are effectively resolved, and water use efficiency is improved, thereby maintaining the sustainable use of water resources under the influence of population growth and climate change. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0050] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0051] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0052] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0053] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0054] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0055] Figure 7 This is a detailed flow chart of S6 of the present invention;

[0056] Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0058] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0059] Embodiment 1

[0060] See also Figure 1 The present invention provides a technical solution: a method for evaluating the carrying capacity of water environment resources, comprising the following steps:

[0061] S1: Based on the water quality index data collected by the water quality monitoring station, the rainfall and reservoir capacity are recorded synchronously, the correlation between the data sets is analyzed, the key factors affecting the water resources carrying capacity are identified, and the key impact identification results are obtained;

[0062] S2: Based on the results of key impact identification, a vector autoregression model is used to conduct seasonal pattern analysis, identify water resource use patterns, and predict the water resource carrying capacity in future seasons to obtain seasonal carrying capacity prediction indicators;

[0063] S3: Based on the seasonal carrying capacity prediction index, the STL time series decomposition algorithm is used to refine the water resource use pattern in each season, analyze the impact of seasonal changes on water resource carrying capacity, and obtain the seasonal impact analysis matrix;

[0064] S4: Based on the seasonal impact analysis matrix, water resources management is carried out, including reservoir water regulation and sewage reuse, and management implementation effects and real-time data are monitored, and long-term impacts and benefits are evaluated to obtain resource management benefit information;

[0065] S5: Based on the resource management benefit information, extract water quality improvement and water use efficiency data, compare the differences before and after management, use nonlinear programming models to optimize water resource allocation, deal with supply and demand conflicts, and obtain resource allocation optimization diagrams;

[0066] S6: Based on the resource allocation optimization diagram, long-term assessment of water resource carrying capacity is conducted. Combined with climate change, population growth and industrial development, the sustainable use of water resources in the future is analyzed to obtain water resource planning results.

[0067] The key impact identification results include water pollution index, rainfall frequency, and water storage capacity. Seasonal carrying prediction indicators include high-demand seasonal index, low-demand seasonal index, and resource supply volatility. The seasonal impact analysis matrix includes seasonal change sensitivity, resource utilization, and seasonal carrying pressure. Resource management benefit information includes water regulation success rate, reuse water quality indicators, and monitoring response time. The resource allocation optimization diagram includes optimized water use efficiency, conflict reduction ratio, and resource reallocation quota.

[0068] See also Figure 2 , based on the water quality index data collected by the water quality monitoring station, the rainfall and reservoir capacity are recorded synchronously, the correlation between the data sets is analyzed, and the key factors affecting the water resources carrying capacity are identified. The specific steps to obtain the key impact identification results are as follows:

[0069] S101: Based on the water quality index data collected by the water quality monitoring station, the rainfall and reservoir capacity are recorded synchronously, a data set is created, and the data is processed in a time series to generate a water resource status record;

[0070] S102: Based on the water resource status records, perform data processing, exclude outliers and missing information in the data, and verify the validity of the data to obtain an overview of the data quality;

[0071] S103: Based on the data quality overview, correlation analysis is performed to identify and record the key factors affecting water resources carrying capacity and obtain key impact identification results.

[0072] Based on the water quality index data collected by the water quality monitoring stations, the initialization data set includes water quality indicators, rainfall and reservoir capacity. The time serialization processing involves associating the data of each collection point with a specific timestamp to ensure the accuracy of the time sequence of the data for subsequent analysis. The data points are checked for continuity and integrity of their timestamps to ensure that there are no missing or duplicate records, providing accurate basic data for analysis.

[0073] Based on the water resource status records, data processing is performed. Data processing includes eliminating data points that deviate significantly from the normal range and filling in missing data records. Outliers are identified by comparing the differences between each data point and its adjacent time points. If the difference exceeds the set threshold, it is considered an anomaly. Data validity verification is completed by checking the integrity and consistency of the data set to ensure that all data are suitable for further analysis.

[0074] Based on the data quality overview, correlation analysis is carried out. Correlation analysis uses statistical methods to determine the relationship between various data, especially the dependence between water quality indicators and rainfall and reservoir capacity. This analysis relies on multivariate statistical techniques to analyze which factors have the greatest impact on water resources carrying capacity. The identification results are integrated by analyzing the various correlation coefficients obtained to determine the most critical influencing factors.

[0075] See also Figure 3 Based on the key impact identification results, the vector autoregression model is used to perform seasonal pattern analysis, identify water resource use patterns, and predict the water resource carrying capacity in future seasons. The specific steps to obtain the seasonal carrying capacity prediction index are as follows:

[0076] S201: Based on the key impact identification results, use the vector autoregression model to organize and adjust the data, perform seasonal analysis, analyze the data points of seasonal changes, and verify the consistency of seasonal patterns to generate seasonal pattern records;

[0077] S203: Based on the seasonal pattern record, use historical data to perform seasonal forecasting, adjust the forecasting process, and compare the historical seasonal data with the forecasting results to obtain forecast adjustment feedback results;

[0078] S203: Based on the forecast adjustment feedback results, the water resource carrying capacity of different seasons in the future is predicted, and the different water resource use patterns are matched to obtain seasonal carrying prediction indicators.

[0079] Based on the results of key impact identification, the data is sorted and adjusted. When sorting the data, first ensure that all key factors are grouped in seasonal order. In seasonal analysis, the fluctuation pattern and cyclical characteristics of the data in each season are checked to confirm whether it is consistent with the previous seasonal pattern. In this process, the data points are evaluated to ensure that they reflect the expected seasonal trend. The consistency of the pattern is verified by comparing the data patterns of consecutive years to ensure that the newly identified seasonal pattern is highly consistent with the historical records.

[0080] Based on seasonal pattern records, when using historical data for forecasting, statistical methods are used to analyze the data change trends of the same season in previous years, and the parameters of the forecasting model are set based on this. The adjustment of the forecasting process includes the refinement of the forecasting model to improve the forecast accuracy. After the forecast is completed, the forecast results are compared with the historical data of the same season to evaluate the deviation and accuracy of the forecast, and the model is adjusted to reduce future forecast errors.

[0081] Based on the forecast adjustment feedback results, the forecast model will be refined according to the results of the adjustment feedback to more accurately match the water resource use patterns in different seasons. By analyzing the various difference factors pointed out in the forecast adjustment feedback results, the water resource use pattern analysis of the target season is carried out to ensure that the forecast indicators can accurately reflect the water resource carrying capacity in the future season.

[0082] See also Figure 4 Based on the seasonal carrying capacity prediction index, the STL time series decomposition algorithm is used to refine the water resource use pattern in each season, analyze the impact of seasonal changes on water resource carrying capacity, and obtain the seasonal impact analysis matrix in the following steps:

[0083] S301: Based on the seasonal carrying capacity prediction index, the STL time series decomposition algorithm is used to time-mark the original data, distinguish the water resource usage data of each season, and classify and mark the seasonal data to generate seasonal data classification results;

[0084] S302: Based on the seasonal data classification results, the data of each season is analyzed by comparing data points, the water resource usage pattern of each season is identified, and a usage pattern matrix is ​​generated;

[0085] S303: Based on the usage pattern matrix, the seasonal data is analyzed in detail, the changes in the data of each season are compared, and the seasonal influencing factors are recorded to obtain a seasonal impact analysis matrix.

[0086] Based on the seasonal carrying capacity prediction index, the seasonal carrying capacity prediction index dataset is imported, and the time series decomposition technology is applied to decompose the data by season, extracting independent datasets for each season, and performing detailed time marking on the data for each season to ensure that the data points accurately reflect the water resource usage status within a specific time period. Afterwards, the seasonal data are grouped according to the preset classification standards, such as the water resource usage in the rainy season and the dry season, to complete the data classification and generate the classification results.

[0087] Based on the seasonal data classification results, the classified data of each season is further analyzed to identify the pattern of water resource use in a specific season. By comparing data points from different years in the same season, their similarities and differences are analyzed to identify continuous or changing usage patterns. The analysis of usage patterns relies on statistical tools, such as the calculation of statistical quantities such as mean and variance, and the drawing of trend lines, to form a usage pattern matrix for each season.

[0088] Based on the usage pattern matrix, the pattern matrix is ​​used as the basis for analysis to further explore the reasons and patterns of data changes in each season, and a detailed analysis of seasonal influencing factors is conducted, including how factors such as climate change and human activities affect water resource use. During the analysis process, the factors are associated with data changes to evaluate their specific impact on water resource use patterns. All analysis results are integrated to form a seasonal impact analysis matrix.

[0089] See also Figure 5 Based on the seasonal impact analysis matrix, water resources management is carried out, including reservoir water regulation and sewage reuse, monitoring management implementation effects and real-time data, and evaluating long-term impacts and benefits. The specific steps to obtain resource management benefit information are as follows:

[0090] S401: Based on the seasonal impact analysis matrix, adjust the water volume of the reservoir, dynamically monitor the reservoir volume, perform real-time data recording, and generate a reservoir operation log;

[0091] S402: Based on the reservoir operation log, evaluate the current sewage treatment effect, adjust the key operating parameters in the sewage treatment process, monitor the adjusted data, and generate sewage treatment update information;

[0092] S403: Based on the updated information of sewage treatment, evaluate the long-term impact of water resource management implementation, record the management effect and real-time monitoring data, analyze the long-term trends and changes, and obtain resource management benefit information.

[0093] Based on the seasonal impact analysis matrix, according to the results of the seasonal impact analysis matrix, the water level of the reservoir is adjusted to adapt to the changes in demand in different seasons, such as increasing the water level to cope with the dry season, or lowering the water level to reserve space to prepare for the arrival of the rainy season. Dynamic monitoring involves the use of sensors and remote monitoring systems to track the water level, inflow and outflow of the reservoir in real time. All monitoring data is recorded in real time in the reservoir operation log, which includes timestamps, water levels, flows and detailed information of any automatic or manual adjustment operations to ensure transparency of operations and traceability of data.

[0094] Based on the reservoir operation log, analyze the data recorded in the reservoir operation log, evaluate the effectiveness of the sewage treatment facilities, especially the performance under different reservoir water level adjustments, and adjust the key operating parameters based on the actual water quality monitoring results, such as COD and BOD indicators, to optimize the treatment effect. After the adjustment, continue to monitor various indicators of sewage treatment to ensure that the adjustment achieves the expected effect. All adjustments and result data will be recorded and updated to form sewage treatment update information.

[0095] Based on the sewage treatment update information, the sewage treatment update information provides a data foundation for analyzing and evaluating the impact of long-term water resources management strategies. By collecting and comparing data at a series of time points, such as water quality indicators, treatment capacity and water resource utilization rate, the long-term trend of the data is analyzed. The trend reflects the effectiveness of water resources management measures and points out areas that need adjustment. The analysis is organized into resource management benefit information.

[0096] See also Figure 6 , based on the resource management benefit information, extract water quality improvement and water use efficiency data, compare the differences before and after management, use nonlinear programming model to optimize water resource allocation, deal with supply and demand conflicts, and obtain the resource allocation optimization diagram in the following steps:

[0097] S501: Based on the resource management benefit information, by screening key indicators, extracting data on water quality improvement and water use efficiency, and conducting comparative analysis of differences, identifying the change points before and after management, and generating efficiency comparison data;

[0098] S502: Based on the efficiency comparison data, a nonlinear programming model is used to determine the imbalance points of resource allocation through quantitative analysis, and adjustment and optimization operations are performed to process supply and demand requirements and generate a deployment configuration summary;

[0099] S503: Based on the allocation configuration summary, by monitoring the real-time data flow and usage pattern, adjust the resource flow direction and allocation ratio, optimize the allocation configuration of water resources, and obtain a resource allocation optimization diagram.

[0100] Based on the resource management benefit information, water quality parameters (such as turbidity, pH value, dissolved oxygen) and water use efficiency (such as the amount of water consumed per unit output) are selected as key indicators from the resource management benefit information. Using data analysis tools, the changes in indicators before and after the implementation of the new water resource management strategy are compared to quantify the effects of management measures. The difference comparison analysis is presented through charts and percentage changes, clearly pointing out the improvement points and deficiencies, providing data support for further optimization.

[0101] Based on efficiency comparison data, nonlinear programming technology is used to solve complex problems in resource allocation, such as dealing with the mismatch between seasonal demand peaks and water supply capacity. By building an optimization model, inputting actual consumption data and demand forecasts, key bottlenecks and imbalances in the allocation process are identified. Model optimization includes adjusting water supply time, reconfiguring water sources and water volume control strategies. Adjustments are made based on model results to achieve the best supply and demand balance and generate a deployment configuration summary.

[0102] Nonlinear programming model, according to the formula:

[0103] ZD=(c T +αd T )x

[0104] The optimal solution ZD for resource allocation is captured, where c represents the resource cost or benefit coefficient, each element represents the cost or benefit of a resource unit, x represents the resource allocation vector, each element represents the allocation quantity or level of the corresponding resource, α is the resource cost adjustment coefficient, which is used to adjust the cost or benefit of the resource according to seasonal factors, d is the seasonal adjustment factor, each element reflects the change in the cost or benefit of the corresponding resource in different seasons, and T represents the transposition operation.

[0105] Execution process:

[0106] First, calculate the adjusted cost or benefit coefficient by combining the original cost-benefit coefficient c with the seasonal adjustment factor d, and use the adjustment coefficient α to reflect seasonal cost changes. This step ensures that the cost-benefit coefficient can adapt to the changing needs of different seasons. The formula is used to find the optimal solution for resource allocation. The entire process reflects the resource cost fluctuations in different time periods through adjustments to maximize the cost-effectiveness of resource allocation.

[0107] Based on the allocation configuration summary, attention is paid to the allocation effect after implementation. The real-time monitoring system collects the flow and usage of water resources to ensure that the actual effect of the adjustment measures meets expectations. The monitoring data includes water flow, usage area, time period, etc. The data is used to dynamically adjust the allocation ratio to adapt to real-time demand changes or respond to emergencies. The resource allocation optimization diagram is a visualization tool that shows the flow of water resources from source to user, as well as the supply and demand status of each region, to ensure the transparency and efficiency of management decisions.

[0108] See also Figure 7 Based on the resource allocation optimization diagram, the water resources carrying capacity is evaluated in the long term. In combination with climate change, population growth and industrial development, the sustainable use of water resources in the future is analyzed. The specific steps to obtain the water resources planning results are as follows:

[0109] S601: Based on the resource allocation optimization map, evaluate the impact of climate change, population growth and industrial development through long-term data collection and analysis operations to generate an environmental impact overview;

[0110] S602: Based on the environmental impact overview, evaluate future water resource use and demand forecasts, analyze resource sustainability, and generate sustainable water use plans through data integration and pattern recognition;

[0111] S603: Analyze the interaction of multiple environmental and development factors based on sustainable water use plans, assess the long-term impact on water resources carrying capacity, and generate water resources planning results.

[0112] Based on the resource allocation optimization diagram, data obtained from meteorological stations, demographic departments and industrial records are used to analyze how variables affect water demand and supply, with special attention paid to factors such as changes in rainfall patterns and increased frequency of droughts caused by climate change, as well as how population growth and industrial expansion increase pressure on water resources. Through professional data analysis software, long-term data are subjected to trend analysis and correlation research to generate an overview of environmental impacts.

[0113] Based on the environmental impact overview, the identified data patterns are used to predict water demand in the next few years or decades. Taking into account the uncertainty of climate change and the needs of economic development, predictive models are used to assess future water pressure points and propose solutions. Sustainable water use plans include measures such as water reuse, promotion of rainwater collection systems, and improvement of industrial water use efficiency to ensure effective management and sustainable use of water resources in the long term.

[0114] Based on the sustainable water use plan, all relevant data and strategies are integrated, and multiple future scenarios are considered, such as the impact of extreme climate events, economic fluctuations and policy changes on water resources management. Through complex system dynamic models, how factors interact with each other is evaluated, and the long-term impact on water resources carrying capacity is predicted to generate water resources planning results.

[0115] A water environment resource carrying capacity assessment system, the system comprising:

[0116] The water quality monitoring and analysis module is based on the water quality index data collected by the water quality monitoring station, records rainfall and reservoir capacity at the same time, analyzes the correlation between data sets, identifies the key factors affecting the water resources carrying capacity, and generates key impact identification results;

[0117] The seasonal pattern analysis module uses the vector autoregression model to analyze the seasonal impact pattern based on the key impact identification results, predicts the water resource carrying capacity of different seasons, and obtains the seasonal carrying capacity prediction index;

[0118] The seasonal impact analysis module uses seasonal carrying capacity prediction indicators and applies the STL time series decomposition algorithm to refine the water resource use pattern in each season, analyze the impact of seasonal changes on water resource carrying capacity, and obtain the seasonal impact analysis matrix;

[0119] The resource management execution module regulates reservoir water volume, implements wastewater reuse, monitors implementation effects and real-time data, and evaluates long-term impacts and benefits based on the seasonal impact analysis matrix to obtain resource management benefit information;

[0120] The long-term water resources planning module extracts water quality improvement and water use efficiency data based on resource management benefit information, combines climate change, population growth and industrial development data, optimizes water resource allocation and use, handles supply and demand conflicts, and analyzes the sustainable use of water resources in the future to obtain water resources planning results.

[0121] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for assessing the carrying capacity of water environment resources, characterized in that: The following steps are involved: Based on the water quality index data collected by the water quality monitoring station, the rainfall and reservoir capacity are recorded synchronously, the correlation between the data sets is analyzed, the key factors affecting the water resources carrying capacity are identified, and the key impact identification results are obtained; Based on the key impact identification results, a vector autoregression model is used to conduct seasonal pattern analysis, identify water resource use patterns, and predict the water resource carrying capacity in future seasons to obtain seasonal carrying capacity prediction indicators; Based on the seasonal carrying capacity prediction index, the STL time series decomposition algorithm is used to refine the water resource use pattern in each season, analyze the impact of seasonal changes on water resource carrying capacity, and obtain a seasonal impact analysis matrix; Based on the seasonal impact analysis matrix, water resources management is carried out, including reservoir water regulation and sewage reuse, management implementation effects and real-time data are monitored, and long-term impacts and benefits are evaluated to obtain resource management benefit information; Based on the resource management benefit information, water quality improvement and water use efficiency data are extracted, the differences before and after management are compared, and a nonlinear programming model is used to optimize water resource allocation, handle supply and demand conflicts, and obtain a resource allocation optimization diagram; Based on the resource allocation optimization map, the water resource carrying capacity is evaluated in the long term, and the sustainable use of water resources in the future is analyzed in combination with climate change, population growth and industrial development to obtain water resource planning results.

2. The method for assessing the carrying capacity of water environment resources according to claim 1, characterized in that: The key impact identification results include water pollution index, rainfall frequency, and water storage capacity; the seasonal carrying prediction indicators include high-demand seasonal index, low-demand seasonal index, and resource supply volatility; the seasonal impact analysis matrix includes seasonal change sensitivity, resource utilization, and seasonal carrying pressure; the resource management benefit information includes water regulation success rate, reuse water quality indicators, and monitoring response time; the resource allocation optimization diagram includes optimized water use efficiency, conflict reduction ratio, and resource reallocation quota.

3. The method for assessing the carrying capacity of water environment resources according to claim 1, characterized in that: Based on the water quality index data collected by the water quality monitoring station, the rainfall and reservoir capacity are recorded synchronously, the correlation between the data sets is analyzed, and the key factors affecting the water resources carrying capacity are identified. The specific steps to obtain the key impact identification results are as follows: Based on the water quality index data collected by the water quality monitoring station, the rainfall and reservoir capacity are recorded synchronously, the data set is created, and the data is processed in time series to generate water resource status records; Based on the water resource status records, data processing is performed to exclude abnormal values ​​and missing information in the data, and the validity of the data is verified to obtain an overview of the data quality; Based on the data quality overview, correlation analysis is performed to identify and record the key influencing factors of water resources carrying capacity and obtain key impact identification results.

4. The method for assessing the carrying capacity of water environment resources according to claim 1, characterized in that: Based on the key impact identification results, the vector autoregression model is used to perform seasonal pattern analysis, identify water resource usage patterns, and predict the water resource carrying capacity of future seasons. The specific steps for obtaining seasonal carrying capacity prediction indicators are as follows: Based on the key impact identification results, use the vector autoregression model to sort and adjust the data, perform seasonal analysis, analyze the data points of seasonal changes, and verify the consistency of seasonal patterns to generate seasonal pattern records; Based on the seasonal pattern records, seasonal forecasting is performed using historical data, the forecasting process is adjusted, and the historical seasonal data is compared with the forecast results to obtain forecast adjustment feedback results; Based on the forecast adjustment feedback results, the water resource carrying capacity of future different seasons is predicted, and the different water resource use patterns are matched to obtain the seasonal carrying prediction index.

5. The method for assessing the carrying capacity of water environment resources according to claim 1, characterized in that: Based on the seasonal carrying capacity prediction index, the STL time series decomposition algorithm is used to refine the water resource use pattern of each season, analyze the impact of seasonal changes on water resource carrying capacity, and obtain the seasonal impact analysis matrix in the following steps: Based on the seasonal carrying capacity prediction index, the STL time series decomposition algorithm is used to time-mark the original data, distinguish the water resource usage data of each season, and classify and mark the seasonal data to generate seasonal data classification results; Based on the seasonal data classification results, each seasonal data is analyzed by comparing data points, identifying the usage pattern of water resources in each season and generating a usage pattern matrix; Based on the usage pattern matrix, the seasonal data is analyzed in detail, the changes in the data of each season are compared, and the seasonal influencing factors are recorded to obtain a seasonal impact analysis matrix.

6. The method for assessing the carrying capacity of water environment resources according to claim 1, characterized in that: Based on the seasonal impact analysis matrix, water resources management, including reservoir water regulation and sewage reuse, monitoring management implementation effects and real-time data, and evaluating long-term impacts and benefits, the specific steps to obtain resource management benefit information are as follows: Based on the seasonal impact analysis matrix, the water volume of the reservoir is regulated, the reservoir volume is dynamically monitored, and real-time data recording is performed to generate a reservoir operation log; Based on the reservoir operation log, evaluate the current sewage treatment effect, adjust key operating parameters in the sewage treatment process, monitor the adjusted data, and generate sewage treatment update information; Based on the sewage treatment update information, the long-term impact of water resource management implementation is evaluated, the management effect and real-time monitoring data are recorded, the long-term trends and changes are analyzed, and the resource management benefit information is obtained.

7. The method for assessing the carrying capacity of water environment resources according to claim 1, characterized in that: Based on the resource management benefit information, water quality improvement and water use efficiency data are extracted, the differences before and after management are compared, and a nonlinear programming model is used to optimize water resource allocation and deal with supply and demand conflicts. The specific steps for obtaining a resource allocation optimization diagram are as follows: Based on the resource management benefit information, by screening key indicators, extracting data on water quality improvement and water use efficiency, and conducting comparative analysis of differences, identifying change points before and after management, and generating efficiency comparison data; Based on the efficiency comparison data, a nonlinear programming model is used to determine the imbalance points of resource allocation through quantitative analysis, perform adjustment and optimization operations, handle supply and demand requirements, and generate a deployment configuration summary; Based on the allocation configuration summary, by monitoring real-time data flow and usage patterns, the resource flow direction and allocation ratio are adjusted to optimize the allocation configuration of water resources and obtain a resource allocation optimization diagram.

8. The method for assessing the carrying capacity of water environment resources according to claim 7, characterized in that: The nonlinear programming model is based on the formula: ZD=(c T +αd T )x The optimal solution ZD for resource allocation is captured, where c represents the resource cost or benefit coefficient, x represents the resource allocation vector, α is the resource cost adjustment coefficient, d is the seasonal adjustment factor, and T represents the transposition operation.

9. The method for assessing the carrying capacity of water environment resources according to claim 1, characterized in that: Based on the resource allocation optimization diagram, the water resources carrying capacity is evaluated in the long term. In combination with climate change, population growth and industrial development, the sustainable use of water resources in the future is analyzed. The specific steps to obtain the water resources planning results are as follows: Based on the resource allocation optimization map, evaluate the impact of climate change, population growth and industrial development through long-term data collection and analysis operations to generate an environmental impact overview; Based on the environmental impact overview, evaluate future water resource use and demand forecasts and analyze resource sustainability through data integration and pattern recognition to generate a sustainable water use plan; Based on the sustainable water use plan, the interaction of multiple environmental and development factors is analyzed, the long-term impact on water resources carrying capacity is evaluated, and water resources planning results are generated.

10. A water environment resource carrying capacity assessment system, characterized in that: According to the method for assessing the carrying capacity of water environment resources according to any one of claims 1 to 9, the system comprises: The water quality monitoring and analysis module is based on the water quality index data collected by the water quality monitoring station, records rainfall and reservoir capacity at the same time, analyzes the correlation between data sets, identifies the key factors affecting the water resources carrying capacity, and generates key impact identification results; The seasonal pattern analysis module uses a vector autoregression model to perform pattern analysis on seasonal impacts based on the key impact identification results, predicts the water resource carrying capacity of different seasons, and obtains seasonal carrying capacity prediction indicators; The seasonal impact analysis module uses the seasonal carrying capacity prediction index and applies the STL time series decomposition algorithm to refine the water resource use pattern in each season, analyze the impact of seasonal changes on the water resource carrying capacity, and obtain a seasonal impact analysis matrix; The resource management execution module regulates the water volume of the reservoir, implements wastewater reuse, monitors the implementation effect and real-time data, and evaluates the long-term impact and benefits based on the seasonal impact analysis matrix to obtain resource management benefit information; The long-term water resources planning module extracts water quality improvement and water use efficiency data based on the resource management benefit information, combines climate change, population growth and industrial development data, optimizes water resource allocation and use, handles supply and demand conflicts, and analyzes the sustainable use of water resources in the future to obtain water resources planning results.

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