Intelligent surveying and mapping method and system for water resource investigation and evaluation

Abstract

The invention provides an intelligent surveying and mapping method and system for water resource investigation and evaluation, and the method comprises the steps: dividing a target region into a water source region and a grid unit according to administrative boundaries, drainage basin characteristics and the like, and guaranteeing fine management; and secondly, generating a high-precision spatial distribution diagram through multi-source data acquisition and spatial registration of a geographic information system, performing comprehensive evaluation in combination with historical data and meteorological information, and setting parameters and a grid acquisition period based on risk levels to realize dynamic adjustment and ensure dense monitoring of a high-risk area. According to the method, visual data display and user experience are provided by utilizing color coding, a thematic map and an interactive query function, a scientific basis is provided for a decision maker by identifying a water resource risk area and analyzing causes, and sustainable management and protection of water resources are promoted. According to the method, the accuracy and the real-time performance of water resource evaluation are remarkably improved, resource allocation is optimized, and the decision support capability is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of water resource investigation and evaluation, and in particular to an intelligent surveying and mapping method and system for water resource investigation and evaluation. Background Art

[0002] As one of the most precious natural resources on earth, water resources management and protection are crucial to maintaining ecological balance, promoting economic development and protecting human health. However, traditional water resources survey and assessment methods are often limited by single data collection methods, delayed data analysis, insufficient visualization and poor interactivity, which makes it difficult to meet the needs of modern water resources management.

[0003] In recent years, with the rapid development of technologies such as geographic information systems (GIS), remote sensing technology, and big data analysis, intelligent mapping methods have gradually been applied to the field of water resources investigation and assessment. This method can achieve real-time monitoring, dynamic assessment, and early warning of water resources, providing a more scientific and efficient technical means for water resources management. However, the existing intelligent mapping methods still have some shortcomings in water resources investigation and assessment, such as insufficient regional division, insufficient data collection, and insufficient scientific evaluation indicators. These problems limit their in-depth application in water resources management.

[0004] Therefore, there is an urgent need to develop a more sophisticated, comprehensive and scientific intelligent surveying and mapping method to improve the accuracy and efficiency of water resources investigation and assessment. Summary of the invention

[0005] In view of the shortcomings of the prior art described above, the purpose of the present invention is to provide an intelligent surveying and mapping method and system for water resources investigation and evaluation, which is used to solve the problems in the prior art that the regional division is not fine enough, the data collection is not comprehensive enough, and the evaluation indicators are not scientific enough.

[0006] To achieve the above objectives and other related objectives, the present invention provides the following technical solutions:

[0007] An intelligent mapping method for water resources investigation and assessment comprises the following steps:

[0008] S1, divide the target area into several water source areas according to administrative boundaries, watershed boundaries, water body types and water source functions, and divide each water source area into several grid units according to the importance of water sources, sensitivity of surrounding environment and water body characteristics;

[0009] S2, collect multi-source data for each grid unit, use geographic information system to spatially align the collected multi-source data with basic geographic surveying and mapping data, generate a spatial distribution map reflecting resource conditions, and conduct a comprehensive assessment of each grid unit in combination with historical monitoring data and meteorological information;

[0010] S3, setting the parameter risk level based on historical monitoring data and expert opinions, and adjusting the parameter level collection period of each parameter according to the parameter risk level, and setting the grid risk level according to the comprehensive evaluation results of the grid unit, and adjusting the grid level collection period of all parameters in the grid unit according to the grid risk level, and the parameter level collection period is superior to the grid level collection period;

[0011] S4, use different colors to color-code grid cells of different grid risk levels in the spatial distribution map, and generate thematic maps based on the collected multi-source data and comprehensive assessment results. Combine the spatial distribution map, thematic map and comprehensive assessment results to identify water resource risk areas and generate a risk report.

[0012] To implement the above technical solution, first, the target area is divided into several relatively independent and functionally clear water source areas according to administrative boundaries, basin boundaries, water body types and water source functions. Then, in each water source area, it is further divided into smaller grid units according to the importance of the water source, the sensitivity of the surrounding environment and the characteristics of the water body. This division is conducive to refined management and monitoring; then multi-source data collection is carried out for each grid unit. These data sources may include remote sensing data, field monitoring data, meteorological data, etc., and these multi-source data are spatially aligned with basic geographic surveying and mapping data using geographic information systems to ensure the consistency and accuracy of all data in geographic space; at the same time, a spatial distribution map reflecting the resource status is generated based on these data, and a comprehensive assessment is conducted on each grid unit in combination with historical monitoring data and meteorological information to understand the current water resource status and its changing trend; different parameter risk levels are set according to historical monitoring data and expert opinions. These parameters may include water quality indicators, water volume changes, etc., and the collection cycle of each parameter is adjusted according to the parameter risk level. High-risk parameters require more frequent monitoring. In addition, the grid risk level is set according to the comprehensive assessment results of the grid unit, and the collection period of all parameters in the grid unit is adjusted according to the grid risk level, and the parameter level collection period takes precedence over the grid level collection period. Different colors are used to color-code grid cells of different grid risk levels in the spatial distribution map to facilitate intuitive identification of risk areas.

[0013] In one embodiment of the present invention, the S1 includes:

[0014] S11, divide the target area into several primary areas according to administrative boundaries, divide the primary areas into several secondary areas according to watershed boundaries, and further divide the secondary areas into several water source areas according to water body types and water source functions;

[0015] S12, each water source area is divided into several grid units based on the comprehensive consideration of the importance of the water source, the sensitivity of the surrounding environment and the characteristics of the water body;

[0016] S13, set up evenly distributed monitoring points in each grid unit, and establish a detailed file for each grid unit, including the grid unit number, geographical location, land use type, distribution of pollution sources, historical pollution events and intensity of human activities.

[0017] To implement the above technical solution, firstly, the target area is divided into several relatively independent and easy-to-manage first-level areas based on the administrative boundaries. In the first-level area, the first-level area is further divided into several second-level areas according to the naturally formed river basin boundaries. The river basin is an important unit for water resource management because it determines the natural path of water flow and the possible pollution diffusion range; in the second-level area, the second-level area is subdivided into several water source areas with specific functions and characteristics according to the water body type and water source function; the water source area is then divided into several smaller grid units to ensure that the water resource status in each unit is relatively consistent, which is convenient for subsequent monitoring and evaluation; in each grid unit, evenly distributed monitoring points are set according to the size, shape and terrain characteristics of the grid unit to facilitate the collection of key data such as water quality and water quantity; at the same time, a detailed file is established for each grid unit, including the grid unit number, geographical location, land use type, pollution source distribution, historical pollution event records and human activity intensity. These files provide basic data for subsequent comprehensive assessment and risk management.

[0018] In one embodiment of the present invention, the S2 includes:

[0019] S21, collect multi-source data for each grid unit, including water quantity parameters and water quality parameters, and create a time series data table for each type of parameter;

[0020] S22, integrating the water quantity parameter time series data table and the water quality parameter time series data table with the basic geographic surveying and mapping data in a geographic information system, and generating a spatial distribution map reflecting the resource status according to the integration result;

[0021] S23, performing water balance assessment according to the water quantity parameter time series data table, and performing water quality assessment according to the water quality parameter time series data table;

[0022] S24, collect historical monitoring data and meteorological information of the grid unit, and set a comprehensive evaluation equation for the grid unit in combination with the water balance evaluation results and the water quality evaluation results.

[0023] To implement the above technical solution, comprehensive multi-source data collection is carried out for each grid unit, including but not limited to water quantity parameters and water quality parameters; time series data tables are created for each type of parameter, and these tables record the values ​​of each parameter at different time points for subsequent analysis and evaluation; the water quantity parameter time series data table and the water quality parameter time series data table are integrated with the basic geographic surveying and mapping data in the geographic information system, and based on the integrated data, the spatial analysis function of GIS is used to generate a spatial distribution map reflecting the resource status. The chart shows the spatial distribution and changes of water quantity and water quality parameters; combined with the water balance assessment results, water quality assessment results, historical monitoring data and meteorological information, a comprehensive assessment equation is set up to more comprehensively assess the water resource status and risk level of the grid unit, and to achieve dynamic monitoring and early warning of water resources in the grid unit.

[0024] In one embodiment of the present invention, the water balance assessment method includes:

[0025] Determine the total inputs within the grid cell, including precipitation, surface water inflow, net groundwater inflow, and anthropogenic changes;

[0026] Determine the total output within the grid cell, including evaporation, surface water outflow, and net groundwater outflow;

[0027] Set up a water balance equation based on the total input and total output to calculate the water storage change of the grid cell within a time step;

[0028] The water balance equation is as follows:

[0029]

[0030] in, is the change in water storage within the grid cell, is the amount of precipitation, is the surface water inflow, is the net groundwater inflow, is the variation of human factors, is the surface water outflow, is the net groundwater outflow, is the actual evaporation amount.

[0031] By implementing the above technical solution and comprehensively considering multiple input and output factors, as well as the influence of human factors, the water balance assessment method can more accurately reflect the changes in water volume within the grid unit; the water balance assessment results can provide a scientific basis for water resources management, planning and protection, and help to formulate targeted water resources management strategies, such as water-saving measures and water resources allocation plans; at the same time, by regularly evaluating the water balance status of grid units, water shortages, over-exploitation and other problems can be discovered in a timely manner, providing early warning and decision-making support for the sustainable use and protection of water resources.

[0032] In one embodiment of the present invention, the water quality assessment method is as follows:

[0033] Collect water quality data within the grid unit, and clean and standardize different types of water quality data;

[0034] Set the upper and lower limits of each water quality parameter, and assign quality scores to each type of water quality parameter based on the actual measured values ​​of the water quality parameter;

[0035] Different weight coefficients are set according to the importance of different water quality parameters, and the comprehensive water quality index of the grid unit is calculated in combination with the corresponding quality scores;

[0036] The method for obtaining the comprehensive water quality index is as follows:

[0037]

[0038]

[0039]

[0040] in, is a comprehensive water quality index. It is Quality score of each water quality parameter, It is The weight of each water quality parameter, is the number of water quality parameters, It is The actual measured values ​​of water quality parameters, It is The upper limit of the water quality parameters It is The lower limit of the standard for water quality parameters.

[0041] To implement the above technical solution, water quality data within the grid unit is collected, including but not limited to key water quality parameters such as pH value, dissolved oxygen, turbidity, and heavy metal content. The collected water quality data is cleaned to remove abnormal values, duplicate values, or invalid data to ensure the accuracy and reliability of the data. The water quality data is standardized to ensure that data from different sources and units can be compared and calculated; the weighted summation method is used to calculate the comprehensive water quality index of the grid unit. , The higher the value, the better the water quality of the grid cell; The lower the value, the worse the water quality.

[0042] In one embodiment of the present invention, the comprehensive evaluation equation is as follows:

[0043]

[0044]

[0045]

[0046] in, It is The comprehensive evaluation results of the grid cells are yes The normalized result is yes The normalized result is yes The normalized result is yes The normalized result is It is The change in water storage per grid cell, It is The comprehensive water quality index of each grid cell, , , and is the weight coefficient and satisfies ; It is The historical data trend score of each grid unit, is the intercept obtained by linear regression fitting, is the slope obtained by linear regression fitting, Indicates a point in time; The meteorological conditions affect the score. It is The weight of each meteorological factor, It is Meteorological factors The standardized function of .

[0047] To implement the above technical solution, the single-phase index , , and Normalized to the same range to facilitate subsequent weighted summation, the comprehensive assessment results of the grid units comprehensively consider the changes in water storage, water quality conditions, historical data trends and the impact of meteorological conditions, which can provide a scientific basis for water resource protection, governance and planning, and help to formulate targeted water resource management strategies.

[0048] In one embodiment of the present invention, S3 includes:

[0049] Based on historical monitoring data and expert opinions, a lower threshold L, upper threshold U, warning value W and alert value A are set for each parameter, and the lower threshold L<warning value W<upper threshold U<alert value A, and the parameter risk level of each parameter value in different intervals is defined;

[0050] Create a parameter risk matrix, map the current value C of each parameter to the corresponding parameter risk level, and adjust the parameter level collection cycle of the parameter according to the parameter risk level;

[0051] The grid risk level is set according to the comprehensive assessment results, and the comprehensive level collection cycle of all parameters in the grid unit is comprehensively adjusted according to the grid risk level, and the parameter level collection cycle takes precedence over the comprehensive level collection cycle.

[0052] To implement the above technical solution, define the parameter risk level and corresponding collection period of each parameter value in different intervals. For example, when the current value C ≤ the lower limit threshold L, the parameter is in an extremely low risk state, and the collection period is set to 1.5P; when the lower limit threshold L < the current value C ≤ the warning value W, the parameter is in a low risk state, and the collection period is 1.25P; when the warning value W < the current value C ≤ the upper limit threshold U, the parameter is in a medium risk state, and the collection period is 1P; when the upper limit threshold U < the current value C ≤ the warning value A, the parameter is in a high risk state, and the collection period is 0.8P; the current value C > the warning value A, the parameter is in an extremely high risk state, and the collection period is 0.5P; The grid risk level is set according to the comprehensive assessment results. The grid risk level reflects the risk level of the water resources in the entire grid unit. According to the grid risk level, the comprehensive collection cycle of all parameters in the grid unit is adjusted comprehensively. At the same time, when the grid risk level is high, the monitoring frequency is increased, and when the grid risk level is low, the detection frequency is reduced. The comprehensive collection cycle is the overall collection strategy after considering the risk levels of all parameters. When adjusting the collection cycle, the principle of parameter-level collection cycle taking precedence over comprehensive-level collection cycle is followed. This means that if the parameter-level collection cycle of a parameter conflicts with the comprehensive-level collection cycle, the parameter-level collection cycle will be executed first.

[0053] In one embodiment of the present invention, the S4 includes:

[0054] In the spatial distribution map, different colors are used to color-code grid cells with different grid risk levels;

[0055] Generate a thematic map based on the collected multi-source data and the comprehensive evaluation results, the thematic map includes a water level change trend map and a water quality parameter comparison map reflecting the same water quality parameter between different grid cells;

[0056] Combine spatial distribution maps, thematic maps and comprehensive assessment results to identify potential water resource risk areas and analyze the causes of risks;

[0057] Dynamically update monitoring data, spatial distribution maps, thematic maps and comprehensive assessment results, and generate a water resources survey and assessment report. The report content includes an overview of the water source area, water quality and quantity conditions, risk assessment results, monitoring recommendations and improvement measures.

[0058] To implement the above technical solution, first, the grid cells in the spatial distribution map are color-coded according to their grid risk levels. This coding method allows observers to intuitively identify which areas have higher or lower water resource risks; the selection and allocation of colors are usually based on the level of grid risk, with high-risk areas using eye-catching colors and low-risk areas using softer colors; combined with spatial distribution maps, thematic maps and comprehensive assessment results, potential water resource risk areas are identified through spatial analysis and data mining technology. At the same time, in-depth analysis of the causes of risk is carried out, including natural and human factors.

[0059] In one embodiment of the present invention, the spatial distribution map supports interactive operations, allowing users to click to query detailed information of any grid unit, including but not limited to water resources, water quality parameters and grid risk level;

[0060] The spatial distribution map supports smooth zooming function, which is convenient for users to browse water resource characteristics at different scales;

[0061] The spatial distribution map includes a time slider control, and the user can view the dynamic change process of water resources in different time periods by dragging the time slider;

[0062] The spatial distribution map allows the user to export water resource data within a selected time period in formats including Excel and CSV.

[0063] To implement the above technical solution, the spatial distribution map integrates interactive operation functions. When the user clicks on any grid unit, the system will immediately respond and display detailed information of the grid unit. These detailed information include but are not limited to water resources, water quality parameters and grid risk level. The information can be displayed in a pop-up window, sidebar or floating window. The spatial distribution map supports smooth zooming. Users can adjust the scale of the map by rolling the mouse wheel and using the zoom button. The spatial distribution map integrates a time slider control, allowing users to intuitively understand the changing trend of water resources over time; the spatial distribution map allows users to export water resources data in the selected time period into Excel or CSV format. The exported data can be used for further data analysis, report generation or data sharing with other systems.

[0064] In another embodiment of the present invention, there is provided an intelligent surveying and mapping system for water resources investigation and assessment, comprising:

[0065] The regional division module is used to divide the target area into multiple water source areas according to administrative boundaries, watershed boundaries, water body types and water source functions, and further divide each water source area into several grid cells;

[0066] Data acquisition module, used to collect multi-source data for each grid unit, including water quantity parameters and water quality parameters;

[0067] The data integration module is used to spatially align the collected multi-source data with the basic geographic surveying and mapping data to generate a spatial distribution map reflecting the resource status;

[0068] Comprehensive evaluation module, used to conduct comprehensive evaluation of each grid unit by combining historical monitoring data and meteorological information;

[0069] A risk level setting module is used to set parameter risk levels and grid risk levels, and adjust parameter level collection cycles and grid level collection cycles according to these levels;

[0070] The result display module is used to perform color coding in the spatial distribution map, generate thematic maps, identify water resource risk areas, and generate risk reports.

[0071] To implement the above technical solution, the regional division module divides the target area into multiple water source areas, and then further subdivides each water source area into several grid units for more refined management and analysis; the data acquisition module collects multiple data sources for each grid unit; the data integration module is used to spatially align the collected multi-source data with the basic geographic surveying and mapping data to ensure the accuracy of the data's geographical location, and generate a spatial distribution map reflecting the resource status, providing a basis for subsequent analysis; the result display module intuitively displays the distribution and changing trends of water resource risks through color coding and thematic map generation. The generated risk report provides comprehensive information support for decision makers, which helps to formulate effective management and protection strategies.

[0072] As described above, the intelligent surveying and mapping method and system for water resources investigation and assessment of the present invention have the following beneficial effects:

[0073] 1. Refined regional division: By comprehensively considering factors such as administrative boundaries, watershed boundaries, water body types, and water source functions, the target area is divided into multiple relatively independent and functional water source areas, and each water source area is further divided into several grid units. This refined division helps to achieve precise management and monitoring of water resources and improve the accuracy and pertinence of the assessment.

[0074] 2. Comprehensive data collection and integration: Multi-source data collection is performed for each grid unit, including water quantity parameters, water quality parameters, etc., and the collected data is spatially aligned with the basic geographic surveying and mapping data using the geographic information system. This method can ensure the comprehensiveness and accuracy of the data and provide reliable basic data for subsequent analysis.

[0075] 3. Scientific and comprehensive assessment: Combine historical monitoring data and meteorological information to conduct a comprehensive assessment of each grid unit to generate spatial distribution maps and thematic maps reflecting the status of water resources. This comprehensive assessment method can fully reflect the dynamic changes in water resources and provide scientific decision-making basis for decision makers.

[0076] 4. Dynamic risk warning: According to the parameter risk level and grid risk level, the parameter-level collection cycle and grid-level collection cycle are dynamically adjusted to achieve real-time monitoring and early warning of water resources. This method can timely discover potential water resource risks and provide strong support for the formulation of targeted management and protection measures.

[0077] 5. Intuitive result display: In the spatial distribution map, different colors are used to color-code grid cells of different grid risk levels to generate an intuitive risk distribution map. At the same time, a detailed risk report is generated, including the overview of the water source area, water quality and quantity, risk assessment results, monitoring suggestions and improvement measures, etc., to provide comprehensive information support for decision makers. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 Shown is a schematic flow chart of the method of the present invention. DETAILED DESCRIPTION

[0079] The following specific embodiments illustrate the implementation of the present invention, and those familiar with the technology can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0080] See also Figure 1 In one embodiment of the present invention, an intelligent mapping method for water resources investigation and evaluation is provided, comprising the following steps: S1, dividing the target area into a number of water source areas according to administrative boundaries, watershed boundaries, water body types and water source functions, and dividing each water source area into a number of grid units according to the importance of the water source, the sensitivity of the surrounding environment and the characteristics of the water body; S2, collecting multi-source data for each grid unit, spatially registering the collected multi-source data with the basic geographic mapping data using a geographic information system, generating a spatial distribution map reflecting the resource status, and comprehensively evaluating each grid unit in combination with historical monitoring data and meteorological information; S3 , set the parameter risk level based on historical monitoring data and expert opinions, and adjust the parameter level collection period of each parameter according to the parameter risk level. At the same time, set the grid risk level according to the comprehensive evaluation results of the grid unit, and adjust the grid level collection period of all parameters in the grid unit according to the grid risk level, and the parameter level collection period is superior to the grid level collection period; S4, use different colors to color-code grid units of different grid risk levels in the spatial distribution map, and generate thematic maps based on the collected multi-source data and comprehensive evaluation results, and identify water resource risk areas in combination with the spatial distribution map, thematic maps and comprehensive evaluation results to generate risk reports.

[0081] The working principle of the above technical solution is as follows: First, the target area is divided into several relatively independent water source areas with clear functions according to administrative boundaries, watershed boundaries, water body types (such as rivers, lakes, etc.) and water source functions (such as drinking water sources, agricultural irrigation, industrial water, etc.). Then, within each water source area, it is further divided into smaller grid units according to the importance of the water source (such as whether it is a key drinking water source), the sensitivity of the surrounding environment (such as whether it is close to a nature reserve or a densely populated area), and the characteristics of the water body (such as flow rate, water quality, etc.). This division is conducive to refined management and monitoring; then multi-source data collection is carried out for each grid unit, and these data sources may include remote sensing data, field monitoring data, meteorological data, etc., and these multi-source data are spatially aligned with basic geographic surveying and mapping data using geographic information systems to ensure the consistency and accuracy of all data in geographic space; at the same time, a spatial distribution map reflecting the resource status is generated based on these data, and a comprehensive assessment is conducted on each grid unit in combination with historical monitoring data and meteorological information to understand the current water resource status and its changing trends; different parameter risk levels are set according to historical monitoring data and expert opinions, and these parameters may include water quality indicators, water volume changes, etc., and the collection cycle of each parameter is adjusted according to the parameter risk level, and high-risk parameters require more frequent monitoring. In addition, the grid risk level is set according to the comprehensive assessment results of the grid unit, and the collection cycle of all parameters in the grid unit is adjusted according to the grid risk level, and the parameter-level collection cycle (based on the risk of specific parameters) will take precedence over the grid-level collection cycle (based on the risk of the entire grid); different colors are used in the spatial distribution map to color-code grid units of different grid risk levels to facilitate intuitive identification of risk areas.

[0082] The beneficial effects of the above technical solution are as follows: through the refined division of water source areas and grid units, as well as the collection and spatial registration of multi-source data, the water resource status and its changing trends in the target area can be more accurately reflected; the collection cycle is adjusted according to the risk level of the parameters and grids, which not only ensures intensive monitoring of high-risk areas, but also avoids excessive monitoring of low-risk areas. Through color coding and the generation of thematic maps, water resource risk areas can be intuitively identified, providing decision makers with clear and intuitive decision support; this method not only focuses on the current water resource status, but also combines historical monitoring data and meteorological information for comprehensive evaluation, which helps to predict future water resource change trends.

[0083] The S1 includes: S11, dividing the target area into several first-level areas according to the administrative boundaries, dividing the first-level areas into several second-level areas according to the basin boundaries, and further dividing the second-level areas into several water source areas according to the water body type and water source function; S12, dividing each water source area into several grid units based on comprehensive consideration of the importance of water sources, the sensitivity of the surrounding environment and the characteristics of water bodies; S13, setting up evenly distributed monitoring points in each grid unit, and establishing a detailed file for each grid unit, including the grid unit number, geographical location, land use type, pollution source distribution, historical pollution events and human activity intensity.

[0084] First, based on administrative boundaries (such as provinces, cities, counties and other administrative divisions), the target area is divided into several relatively independent and easy-to-manage first-level areas. Within the first-level area, the first-level area is further divided into several second-level areas based on the naturally formed river basin boundaries. The river basin is an important unit for water resource management because it determines the natural path of water flow and the possible scope of pollution diffusion. In the second-level area, the second-level area is subdivided into several water source areas with specific functions and characteristics based on the type of water body and water source function. The water source area is then divided into several smaller grid units to ensure that the water resource status in each unit is relatively consistent, which is convenient for subsequent monitoring and evaluation. In each grid unit, evenly distributed monitoring points are set according to the size, shape and terrain characteristics of the grid unit to facilitate the collection of key data such as water quality and water quantity. At the same time, a detailed file is established for each grid unit, including the grid unit number, geographical location (latitude and longitude coordinates), land use type (such as agricultural land, industrial land, residential area, etc.), pollution source distribution (such as industrial discharge outlets, sewage treatment plant location, etc.), historical pollution event records and human activity intensity (such as population density, industrial activity intensity, etc.). These archives provide basic data for subsequent comprehensive assessment and risk management.

[0085] The S2 comprises: S21, collecting multi-source data for each grid unit, including water quantity parameters and water quality parameters, and creating a time series data table for each type of parameter; S22, integrating the water quantity parameter time series data table and the water quality parameter time series data table with basic geographic surveying and mapping data in a geographic information system, and generating a spatial distribution map reflecting resource conditions based on the integration results; S23, conducting a water balance assessment based on the water quantity parameter time series data table, and conducting a water quality assessment based on the water quality parameter time series data table; S24, collecting historical monitoring data and meteorological information of the grid unit, and setting a comprehensive assessment equation for the grid unit in combination with the water quantity balance assessment results and the water quality assessment results.

[0086] Comprehensive multi-source data collection is carried out for each grid unit, including but not limited to water quantity parameters (such as rainfall, runoff, evaporation, groundwater level, etc.) and water quality parameters (such as heavy metal content, nutrient concentration, etc.); time series data tables are created for each type of parameter, and these tables record the values ​​of each parameter at different time points for subsequent analysis and evaluation; the water quantity parameter time series data table and the water quality parameter time series data table are integrated with the basic geographic surveying and mapping data in the geographic information system, and based on the integrated data, the spatial analysis function of GIS is used to generate a spatial distribution map reflecting the resource status. This chart shows the spatial distribution and changes of water quantity and water quality parameters; combined with the water balance assessment results, water quality assessment results, historical monitoring data and meteorological information, a comprehensive assessment equation is set up to more comprehensively assess the water resource status and risk level of the grid unit, and to achieve dynamic monitoring and early warning of water resources in the grid unit.

[0087] The water balance assessment method includes: determining the total input in the grid unit, including precipitation, surface water inflow, net groundwater inflow and human factor change; determining the total output in the grid unit, including evaporation, surface water outflow, and net groundwater outflow; setting a water balance equation according to the total input and total output, and calculating the water storage change of the grid unit within a time step; the water balance equation is as follows:

[0088]

[0089] in, is the change in water storage within the grid cell, is the amount of precipitation, is the surface water inflow, is the net groundwater inflow, is the variation of human factors, is the surface water outflow, is the net groundwater outflow, is the actual evaporation amount.

[0090] By comprehensively considering multiple input and output factors, as well as the influence of human factors, the water balance assessment method can more accurately reflect the changes in water volume within the grid unit; the water balance assessment results can provide a scientific basis for water resources management, planning and protection, and help to formulate targeted water resources management strategies, such as water-saving measures and water resources allocation plans; at the same time, by regularly evaluating the water balance status of grid units, water resource shortages, over-exploitation and other problems can be discovered in a timely manner, providing early warning and decision-making support for the sustainable use and protection of water resources.

[0091] The water quality assessment method is as follows: water quality data within a grid unit is collected, and different types of water quality data are cleaned and standardized; a standard upper limit and a standard lower limit are set for each water quality parameter, and a quality score is given to each type of water quality parameter in combination with the actual measured value of the water quality parameter; different weight coefficients are set according to the importance of different water quality parameters, and a comprehensive water quality index of the grid unit is calculated in combination with the corresponding quality score;

[0092] The method for obtaining the comprehensive water quality index is as follows:

[0093]

[0094]

[0095]

[0096] in, is a comprehensive water quality index. It is Quality score of each water quality parameter, It is The weights of the water quality parameters, is the number of water quality parameters, It is The actual measured values ​​of water quality parameters, It is The upper limit of the water quality parameters It is The lower limit of the standard for water quality parameters.

[0097] Collect water quality data within the grid unit, including but not limited to key water quality parameters such as pH value, dissolved oxygen, turbidity, and heavy metal content. Clean the collected water quality data to remove abnormal values, duplicate values, or invalid data to ensure the accuracy and reliability of the data. Standardize the water quality data to ensure that data from different sources and units can be compared and calculated; use the weighted summation method to calculate the comprehensive water quality index of the grid unit. , The higher the value, the better the water quality of the grid cell; The lower the value, the worse the water quality.

[0098] The comprehensive evaluation equation is as follows:

[0099]

[0100]

[0101]

[0102] in, It is The comprehensive evaluation results of the grid cells are yes The normalized result is yes The normalized result is yes The normalized result is yes The normalized result is It is The change in water storage per grid cell, It is The comprehensive water quality index of each grid cell, , , and is the weight coefficient and satisfies ; It is The historical data trend score of each grid unit, is the intercept obtained by linear regression fitting, is the slope obtained by linear regression fitting, Indicates a point in time; The meteorological conditions affect the score. It is The weight of each meteorological factor, It is Meteorological factors The standardized function of .

[0103] In this system, the single-phase index , , and Normalized to the same range (e.g., 0 to 1) to facilitate subsequent weighted summation, the comprehensive assessment results of the grid units comprehensively consider the changes in water storage, water quality conditions, historical data trends, and the impact of meteorological conditions, which can provide a scientific basis for water resource protection, governance, and planning, and help formulate targeted water resource management strategies.

[0104] The S3 includes: setting a lower threshold L, an upper threshold U, a warning value W and an alert value A for each parameter based on historical monitoring data and expert opinions, and the lower threshold L<warning value W<upper threshold U<alert value A, and defining parameter risk levels for each parameter value in different intervals; creating a parameter risk matrix, mapping the current value C of each parameter to the corresponding parameter risk level, and adjusting the parameter level collection period of the parameter according to the parameter risk level; setting the grid risk level according to the comprehensive evaluation results, and comprehensively adjusting the comprehensive level collection period of all parameters in the grid unit according to the grid risk level, and the parameter level collection period takes precedence over the comprehensive level collection period.

[0105] Define the parameter risk level and corresponding collection cycle for each parameter value in different intervals. For example, when the current value C ≤ the lower limit threshold L, the parameter is in an extremely low risk state, and the collection cycle is set to 1.5P; when the lower limit threshold L < the current value C ≤ the warning value W, the parameter is in a low risk state, and the collection cycle is 1.25P; when the warning value W < the current value C ≤ the upper limit threshold U, the parameter is in a medium risk state, and the collection cycle is 1P; when the upper limit threshold U < the current value C ≤ the warning value A, the parameter is in a high risk state, and the collection cycle is 0.8P; the current value C > the warning value A, the parameter is in an extremely high risk state, and the collection cycle is 0.5P; at the same time, according to the comprehensive The grid risk level is set based on the assessment results. The grid risk level reflects the risk level of the water resources in the entire grid unit. According to the grid risk level, the comprehensive collection cycle of all parameters in the grid unit is comprehensively adjusted. When the grid risk level is high, the monitoring frequency is increased, and when the grid risk level is low, the detection frequency is reduced. The comprehensive collection cycle is the overall collection strategy after considering the risk levels of all parameters. When adjusting the collection cycle, the principle of parameter-level collection cycle taking precedence over comprehensive-level collection cycle is followed. This means that if the parameter-level collection cycle of a parameter conflicts with the comprehensive-level collection cycle, the parameter-level collection cycle will be executed first.

[0106] The S4 includes: using different colors to color-code grid cells of different grid risk levels in the spatial distribution map; generating a thematic map based on the collected multi-source data and the comprehensive assessment results, the thematic map including a water level change trend map and a water quality parameter comparison map reflecting the same water quality parameter between different grid cells; combining the spatial distribution map, thematic map and the comprehensive assessment results to identify potential water resource risk areas and analyze the causes of risks; dynamically updating monitoring data, and updating the spatial distribution map, thematic map and the comprehensive assessment results to generate a water resource investigation and assessment report, the report content including an overview of the water source area, water quality and quantity conditions, risk assessment results, monitoring recommendations and improvement measures.

[0107] First, the grid cells in the spatial distribution map are color-coded according to their grid risk levels. This coding method allows observers to intuitively identify which areas have higher or lower water resource risks; the selection and allocation of colors are usually based on the level of grid risk, with high-risk areas using eye-catching colors and low-risk areas using softer colors; combining spatial distribution maps, thematic maps and comprehensive assessment results, through spatial analysis and data mining technology, potential water resource risk areas are identified. At the same time, in-depth analysis of the causes of risk is carried out, including natural factors (such as topography, climate, etc.) and human factors (such as pollution emissions, over-exploitation, etc.).

[0108] The spatial distribution map supports interactive operations, allowing users to click to query detailed information of any grid unit, which includes but is not limited to water resource quantity, water quality parameters and grid risk level; the spatial distribution map supports smooth zooming function, which is convenient for users to browse water resource characteristics at different scales; the spatial distribution map includes a time slider control, and users can view the dynamic changes of water resources in different time periods by dragging the time slider; the spatial distribution map allows users to export water resource data in the selected time period, and the export formats include Excel and CSV.

[0109] The spatial distribution map integrates interactive operation functions. When the user clicks on any grid unit, the system will immediately respond and display detailed information of the grid unit. These detailed information include but are not limited to water resources (such as total water storage, available water, etc.), water quality parameters (such as dissolved oxygen, pH value, turbidity, etc.) and grid risk level. The information can be displayed in pop-up windows, sidebars or floating windows. The spatial distribution map supports smooth zooming. Users can adjust the scale of the map by scrolling the mouse wheel and using the zoom button. The spatial distribution map integrates a time slider control, allowing users to intuitively understand the changing trend of water resources over time; the spatial distribution map allows users to export water resource data in the selected time period into Excel or CSV format. The exported data can be used for further data analysis, report generation or data sharing with other systems.

[0110] In another embodiment of the present invention, an intelligent mapping system for water resource investigation and evaluation is provided, including: a regional division module, which is used to divide the target area into multiple water source areas according to administrative boundaries, watershed boundaries, water body types and water source functions, and further divide each water source area into a number of grid units; a data acquisition module, which is used to collect multi-source data for each grid unit, including water quantity parameters, water quality parameters, etc.; a data integration module, which is used to spatially align the collected multi-source data with basic geographic mapping data to generate a spatial distribution map reflecting the resource status; a comprehensive evaluation module, which is used to conduct a comprehensive evaluation of each grid unit in combination with historical monitoring data and meteorological information; a risk level setting module, which is used to set parameter risk levels and grid risk levels, and adjust parameter level acquisition cycles and grid level acquisition cycles according to these levels; a result display module, which is used to perform color coding in spatial distribution maps, generate thematic maps, identify water resource risk areas, and generate risk reports.

[0111] The regional division module divides the target area into multiple water source areas, and then further subdivides each water source area into several grid units for more refined management and analysis; the data acquisition module collects multiple data sources for each grid unit; the data integration module is used to spatially align the collected multi-source data with the basic geographic surveying and mapping data to ensure the accuracy of the data's geographical location, and generate a spatial distribution map reflecting the resource status, providing a basis for subsequent analysis; the result display module intuitively displays the distribution and changing trends of water resource risks through color coding and thematic map generation. The generated risk report provides comprehensive information support for decision makers, which helps to formulate effective management and protection strategies.

[0112] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. All equivalent modifications or changes made by those skilled in the art without departing from the spirit and technical ideas disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. An intelligent mapping method for water resources investigation and assessment, characterized in that: The following steps are involved: S1, divide the target area into several water source areas according to administrative boundaries, watershed boundaries, water body types and water source functions, and divide each water source area into several grid units according to the importance of water sources, sensitivity of surrounding environment and water body characteristics; S2, collect multi-source data for each grid unit, use geographic information system to spatially align the collected multi-source data with basic geographic surveying and mapping data, generate a spatial distribution map reflecting resource conditions, and conduct a comprehensive assessment of each grid unit in combination with historical monitoring data and meteorological information; S3, setting the parameter risk level based on historical monitoring data and expert opinions, and adjusting the parameter level collection period of each parameter according to the parameter risk level, and setting the grid risk level according to the comprehensive evaluation results of the grid unit, and adjusting the grid level collection period of all parameters in the grid unit according to the grid risk level, and the parameter level collection period is superior to the grid level collection period; S4, use different colors to color-code grid cells of different grid risk levels in the spatial distribution map, and generate thematic maps based on the collected multi-source data and comprehensive assessment results. Combine the spatial distribution map, thematic map and comprehensive assessment results to identify water resource risk areas and generate a risk report.

2. The intelligent surveying and mapping method for water resources investigation and assessment according to claim 1 is characterized in that: The S1 includes: S11, divide the target area into several primary areas according to administrative boundaries, divide the primary areas into several secondary areas according to watershed boundaries, and further divide the secondary areas into several water source areas according to water body types and water source functions; S12, each water source area is divided into several grid units based on comprehensive consideration of the importance of the water source, the sensitivity of the surrounding environment and the characteristics of the water body; S13, set up evenly distributed monitoring points in each grid unit, and establish a detailed file for each grid unit, including the grid unit number, geographical location, land use type, distribution of pollution sources, historical pollution events and intensity of human activities.

3. The intelligent surveying and mapping method for water resources investigation and assessment according to claim 1 is characterized in that: The S2 comprises: S21, collect multi-source data for each grid unit, including water quantity parameters and water quality parameters, and create a time series data table for each type of parameter; S22, integrating the water quantity parameter time series data table and the water quality parameter time series data table with the basic geographic surveying and mapping data in a geographic information system, and generating a spatial distribution map reflecting the resource status according to the integration result; S23, performing water balance assessment according to the water quantity parameter time series data table, and performing water quality assessment according to the water quality parameter time series data table; S24, collect historical monitoring data and meteorological information of the grid unit, and set a comprehensive evaluation equation for the grid unit in combination with the water balance evaluation results and the water quality evaluation results.

4. The intelligent surveying and mapping method for water resources investigation and assessment according to claim 3 is characterized in that: The water balance assessment method comprises: Determine the total inputs within the grid cell, including precipitation, surface water inflow, net groundwater inflow, and anthropogenic changes; Determine the total output within the grid cell, including evaporation, surface water outflow, and net groundwater outflow; Set up a water balance equation based on the total input and total output to calculate the water storage change of the grid cell within a time step; The water balance equation is as follows: in, is the change in water storage within the grid cell, is the amount of precipitation, is the surface water inflow, is the net groundwater inflow, is the variation of human factors, is the surface water outflow, is the net groundwater outflow, is the actual evaporation amount.

5. The intelligent surveying and mapping method for water resources investigation and assessment according to claim 4 is characterized in that: The water quality assessment method is as follows: Collect water quality data within the grid unit, and clean and standardize different types of water quality data; Set the upper and lower limits of each water quality parameter, and assign quality scores to each type of water quality parameter based on the actual measured values ​​of the water quality parameter; Different weight coefficients are set according to the importance of different water quality parameters, and the comprehensive water quality index of the grid unit is calculated in combination with the corresponding quality scores; The method for obtaining the comprehensive water quality index is as follows: in, is a comprehensive water quality index. It is Quality score of each water quality parameter, It is The weights of the water quality parameters, is the number of water quality parameters, It is The actual measured values ​​of water quality parameters, It is The upper limit of the standard for water quality parameters, It is The lower limit of the standard for water quality parameters.

6. The intelligent surveying and mapping method for water resources investigation and assessment according to claim 5 is characterized in that: The comprehensive evaluation equation is as follows: in, It is The comprehensive evaluation results of the grid cells are yes The normalized result is yes The normalized result is yes The normalized result is yes The normalized result is It is The change in water storage per grid cell, It is The comprehensive water quality index of each grid cell, , , and is the weight coefficient and satisfies ; It is The historical data trend score of each grid unit, is the intercept obtained by linear regression fitting, is the slope obtained by linear regression fitting, Indicates a point in time; The meteorological conditions affect the score. It is The weight of each meteorological factor, It is Meteorological factors The standardized function of .

7. The intelligent surveying and mapping method for water resources investigation and assessment according to claim 6 is characterized in that: The S3 includes: Based on historical monitoring data and expert opinions, a lower threshold L, upper threshold U, warning value W and alert value A are set for each parameter, and the lower threshold L<warning value W<upper threshold U<alert value A, and the parameter risk level of each parameter value in different intervals is defined; Create a parameter risk matrix, map the current value C of each parameter to the corresponding parameter risk level, and adjust the parameter level collection cycle of the parameter according to the parameter risk level; The grid risk level is set according to the comprehensive assessment results, and the comprehensive level collection cycle of all parameters in the grid unit is comprehensively adjusted according to the grid risk level, and the parameter level collection cycle takes precedence over the comprehensive level collection cycle.

8. The intelligent surveying and mapping method for water resources investigation and assessment according to claim 1 is characterized in that: The S4 includes: In the spatial distribution map, different colors are used to color-code grid cells with different grid risk levels; Generate a thematic map based on the collected multi-source data and the comprehensive evaluation results, the thematic map includes a water level change trend map and a water quality parameter comparison map reflecting the same water quality parameter between different grid cells; Combine spatial distribution maps, thematic maps and comprehensive assessment results to identify potential water resource risk areas and analyze the causes of risks; Dynamically update monitoring data, spatial distribution maps, thematic maps and comprehensive assessment results, and generate a water resources survey and assessment report. The report content includes an overview of the water source area, water quality and quantity conditions, risk assessment results, monitoring recommendations and improvement measures.

9. The intelligent surveying and mapping method for water resources investigation and assessment according to claim 8 is characterized by: The spatial distribution map supports interactive operations, allowing users to click to query detailed information of any grid unit, including but not limited to water resources, water quality parameters and grid risk level; The spatial distribution map supports smooth zooming function, which is convenient for users to browse water resource characteristics at different scales; The spatial distribution map includes a time slider control, and the user can view the dynamic change process of water resources in different time periods by dragging the time slider; The spatial distribution map allows the user to export water resource data within a selected time period in formats including Excel and CSV.

10. A system for implementing the method according to any one of claims 1 to 9, characterized in that: include: The regional division module is used to divide the target area into multiple water source areas according to administrative boundaries, watershed boundaries, water body types and water source functions, and further divide each water source area into several grid cells; Data acquisition module, used to collect multi-source data for each grid unit, including water quantity parameters and water quality parameters; The data integration module is used to spatially align the collected multi-source data with the basic geographic surveying and mapping data to generate a spatial distribution map reflecting the resource status; Comprehensive evaluation module, used to conduct comprehensive evaluation of each grid unit by combining historical monitoring data and meteorological information; A risk level setting module is used to set parameter risk levels and grid risk levels, and adjust parameter level collection cycles and grid level collection cycles according to these levels; The result display module is used to perform color coding in the spatial distribution map, generate thematic maps, identify water resource risk areas, and generate risk reports.

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