Water ecological environment problem diagnosis method and device based on basin space tracking and city space refinement, equipment and storage medium

Abstract

This invention provides a method, apparatus, equipment, and storage medium for diagnosing water ecological environment problems based on watershed spatial tracking and city-level spatial refinement. It belongs to the technical field of water ecological environment protection and water quality safety. The method includes: acquiring watershed catchment unit data for a target area based on resistance factors; performing spatial autocorrelation statistical analysis on the concentrations of characteristic pollutants in the watershed catchment units to identify watersheds with prominent water environment problems; refining the watershed catchment units to obtain city-level catchment unit data; constructing a water ecological environment problem evaluation index system to determine key city-level catchment unit data; and generating a water ecological environment problem dataset based on the determined key city-level catchment units and corresponding water ecological environment problem indicators. The method of this invention fully considers the influence of surface water catchment resistance when dividing watershed catchment units and diagnoses water ecological environment problems at the city-level scale, making the refined management of water ecological environment problems more targeted.

Description

Technical Field

[0001] This invention relates to the field of water ecological environment protection and water quality safety technology, and in particular to a method, device, equipment and storage medium for diagnosing water ecological environment problems based on watershed spatial tracking and city spatial refinement. Background Technology

[0002] The protection and management of the aquatic ecological environment is crucial for the sustainable use of water resources. With social development, the demand for refined management of the aquatic ecological environment is gradually increasing. Simply conducting analysis of aquatic ecological environment problems from the perspective of the entire river basin or province is no longer sufficient to meet the requirements of "precise" management. Currently, research on refined management of water ecological environment and division of spatial units mainly focuses on methods for integrating natural catchment areas and related environmental zoning based on DEM data. For example, CN115757367A discloses a method for refined management of water ecological environment in small watersheds, which involves investigating and obtaining current water ecological environment data of the target watershed, including DEM layer information; dividing the target watershed into multiple catchment units based on spatial density differences according to the current water ecological environment data; and CN117236603A discloses a water ecological environment management method that can connect administrative regions with watersheds, which obtains watershed hydrological analysis data of the target area based on a digital elevation model (DEM), uses hydrological analysis tools, and combines the Ministry of Water Resources' three-level water resources zoning data to divide hydrological units; based on hydrological units, by overlaying control sections, water system maps, pollution source data, and land use data, the catchment units are identified through catchment analysis; and the catchment units are overlaid with administrative division and administrative location data to generate water ecological environment control units. The above methods do not consider the impact of water catchment resistance when dividing water catchment units, and also lack methods for diagnosing water ecological environment problems at the prefecture-level city scale. As a key level for promoting the continuous improvement of the water ecological environment of the basin and implementing relevant governance measures, prefecture-level cities play an important role in fundamentally solving prominent water ecological environment problems in the basin. Summary of the Invention

[0003] To address the above problems, this invention provides a method, apparatus, equipment, and storage medium for diagnosing water ecological environment problems based on watershed spatial tracking and urban spatial refinement.

[0004] This invention provides a method for diagnosing water ecological environment problems based on watershed spatial tracking and city-level spatial refinement, the method comprising:

[0005] Data on watershed catchment units in a target area based on resistance factors are obtained, wherein the resistance factors include at least one of elevation, slope, aspect, undulation, and roughness.

[0006] Spatial autocorrelation statistical analysis was performed on the concentrations of characteristic pollutants in the catchment units of the target area to identify and obtain watersheds with prominent water environment problems.

[0007] Based on the identified watersheds with prominent water environment problems, the watershed catchment units are further refined using prefecture-level cities as the basic unit to obtain prefecture-level city catchment unit data.

[0008] An evaluation index system for water ecological environment problems is constructed, and key water catchment unit data for cities is determined from the water catchment unit data of the cities based on the constructed water ecological environment problem index system.

[0009] A water ecological environment problem dataset is generated based on the identified key city catchment units and the corresponding water ecological environment problem indicators for the key city catchment units.

[0010] As a further improvement of the present invention, the acquisition of target area watershed catchment unit data based on resistance factor includes:

[0011] Identification and generalization of water systems;

[0012] The control section is determined, and the catchment area of ​​the watershed corresponding to the section is calculated based on the water system generalization results and the minimum path resistance value.

[0013] Based on geospatial boundary fitting technology, administrative responsibility boundaries are coupled with watershed catchment ranges to obtain watershed catchment units for the target area.

[0014] The control section is a national-level control section.

[0015] As a further improvement of the present invention, the identification and generalization of water systems includes:

[0016] Target area vector and raster data collection;

[0017] DEM data preprocessing;

[0018] DEM extraction of river networks;

[0019] River network system verification.

[0020] As a further improvement of the present invention, the determination of the control section, based on the generalized results of the water system, and the calculation of the catchment area of ​​the watershed corresponding to the section through the minimum path resistance value, includes:

[0021] The target area is divided into grids. The resistance factors are weighted according to different terrains, including plains, hills and mountains, using the hierarchical analysis method to obtain the weight value of each resistance factor for the corresponding terrain. The resistance coefficient for each spatial grid is obtained by weighted calculation.

[0022] A minimum resistance cumulative consumption distance model is established, and the direction and path of plastic flow of fluid particles in the space are calculated based on the resistance coefficient of each spatial grid to obtain the minimum cost path from the source to the end.

[0023] Based on the minimum cost path, the catchment area of ​​the watershed corresponding to the cross section is obtained.

[0024] As a further improvement of the present invention, the minimum resistance cumulative distance consumption model is as follows:

[0025]

[0026] In the formula, D ij R is the spatial distance from source cell i to sink cell j; i R represents the drag coefficient present during the transition from source unit i to sink unit j. i The value of i determines the different resistance values ​​generated by the path from source unit i to sink unit j. Once i is determined, calculating the MCR requires selecting the path with the minimum resistance value in the spatial distance. This path conforms to the laws of liquid flow and is the easiest path for runoff diffusion.

[0027] As a further improvement of the present invention, spatial autocorrelation statistical analysis is performed on the concentration of characteristic pollutants in the catchment units of the target area watershed to identify and obtain watersheds with prominent water environment problems. Based on the local Moran index, spatial correlation analysis is performed on the characteristic pollutants in the catchment units of the target area watershed, wherein the characteristic pollutants include chemical oxygen demand, ammonia nitrogen and total phosphorus.

[0028] As a further improvement of the present invention, the formula for the local Moran index is:

[0029]

[0030] In the formula, It is a local Moran index. and They represent the catchment units of the target area's watershed. Its adjacent target area watershed catchment unit The attribute value, It is the mean of all unit attribute values. It is variance. This represents the spatial weight matrix, used to quantify the catchment units of the target region's watershed. Its adjacent target area watershed catchment unit The strength of the spatial relationship between them.

[0031] As a further improvement of the present invention, the construction of an evaluation index system for water ecological environment problems, and the determination of key city-level water catchment unit data from the city-level water catchment unit data based on the constructed evaluation index system for water ecological environment problems, includes:

[0032] Construct an evaluation index system for the aforementioned water ecological environment issues;

[0033] The water catchment units of the cities with water ecological environment problems are identified as the key water catchment units of the cities.

[0034] On the other hand, the present invention also provides a water ecological environment problem diagnosis device based on watershed spatial tracking and city spatial refinement, including a watershed catchment unit data acquisition module, a watershed identification module with prominent water environment problems, a city catchment unit data acquisition module, a key city catchment unit data determination module, and a water ecological environment problem dataset generation module.

[0035] Among them, the watershed catchment unit data acquisition module is used to acquire watershed catchment unit data of the target area based on the resistance factor;

[0036] The watershed identification module for water environment problems performs spatial autocorrelation statistical analysis on the concentration of characteristic pollutants in the catchment units of the target area watershed to identify and obtain watersheds with prominent water environment problems.

[0037] The prefecture-level city water catchment unit data acquisition module is used to refine the water catchment units of the watershed with prefecture-level cities as the basic unit based on the obtained watershed with prominent water environment problems, and obtain the prefecture-level city water catchment unit data.

[0038] The key city catchment unit data determination module is used to construct an evaluation index system for water ecological environment problems, and to determine key city catchment unit data from the city catchment unit data based on the constructed evaluation index system for water ecological environment problems;

[0039] The water ecological environment problem dataset generation module is used to generate a water ecological environment problem dataset based on the determined key city water catchment unit data and the water ecological environment problem indicators corresponding to the key city water catchment units.

[0040] On the other hand, the present invention also provides an apparatus including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above-described method.

[0041] On the other hand, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0042] This invention provides a method, apparatus, equipment, and storage medium for diagnosing water ecological environment problems based on watershed spatial tracking and city-level spatial refinement. First, watershed-scale spatial units are divided based on resistance factors to obtain watershed catchment units. Then, spatial autocorrelation analysis is used to focus on spatial regions with water ecological environment problems. These regions are further refined at the city level to obtain city-level catchment units. Finally, a constructed water ecological environment problem evaluation index system is used to spatially diagnose city-level water ecological environment problems, identifying key city-level catchment units with water ecological environment problems and generating a dataset of water ecological environment problems for these key units. The watershed catchment unit division fully considers the impact of surface water catchment resistance and diagnoses water ecological environment problems at the city level, achieving a shift from focusing on watershed areas to focusing on city-level water ecological environment problems, making the refined management of water ecological environment problems more targeted. Attached Figure Description

[0043] Figure 1 This is a flowchart illustrating the water ecological environment problem diagnosis method based on watershed spatial tracking and city-level spatial refinement according to an embodiment of the present invention.

[0044] Figure 2 This is a schematic diagram of the process for obtaining water catchment units in the water ecological environment problem diagnosis method based on watershed spatial tracking and urban spatial refinement according to an embodiment of the present invention.

[0045] Figure 3 This is a schematic diagram of the different terrain distributions in the Yangtze River Basin according to an embodiment of the present invention.

[0046] Figure 4 This is a schematic diagram of the Yangtze River basin catchment unit division results according to an embodiment of the present invention.

[0047] Figure 5 This is a schematic diagram of the spatial autocorrelation distribution of chemical oxygen demand, ammonia nitrogen, and total phosphorus concentrations in the Yangtze River basin catchment unit according to an embodiment of the present invention.

[0048] Figure 6 This is a map showing the distribution of water catchment units in a city in the Yangtze River Basin, according to an embodiment of the present invention. Detailed Implementation

[0049] The following describes specific embodiments and appendices. Figure 1-6 The invention is described in detail so that those skilled in the art can more fully understand its purpose, features and effects.

[0050] Unless otherwise specified, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. In the event of any discrepancy between the definitions of terms in this invention and their commonly understood meaning by one of ordinary skill in the art to which this invention pertains, the definitions set forth herein shall prevail.

[0051] This invention provides a method, device, equipment, and storage medium for diagnosing water ecological environment problems based on watershed spatial tracking and city-level spatial refinement, thereby improving the pertinence of water ecological environment problem diagnosis.

[0052] Example 1

[0053] As a specific embodiment of the present invention, this embodiment provides a method for diagnosing water ecological environment problems based on watershed spatial tracking and urban spatial refinement, referring to... Figure 1 The specific steps are as follows:

[0054] S100. Obtain target area watershed catchment unit data based on resistance factor;

[0055] S200. Perform spatial autocorrelation statistical analysis on the concentration of characteristic pollutants in the catchment units of the target area to identify and obtain watersheds with prominent water environment problems.

[0056] S300. Based on the obtained watershed with prominent water environment problems, the watershed catchment units are refined using prefecture-level cities as the basic units to obtain prefecture-level city catchment unit data.

[0057] S400. Construct an evaluation index system for water ecological environment problems, and determine key city water catchment unit data from the city water catchment unit data based on the constructed evaluation index system for water ecological environment problems;

[0058] S500. Based on the determined key city catchment unit data and the corresponding water ecological environment problem indicators of the key city catchment units, generate a water ecological environment problem dataset.

[0059] The water ecological environment problem diagnosis method based on watershed spatial tracking and city-level spatial refinement in this embodiment first divides the watershed into watershed catchment units based on resistance factors. Then, it focuses on the spatial regions with water ecological environment problems based on spatial autocorrelation analysis, and further refines the spatial regions with water ecological environment problems into city-level catchment units. Finally, it performs spatial diagnosis of city-level water ecological environment problems through the constructed water ecological environment problem evaluation index system, identifies key city-level catchment units with water ecological environment problems, and generates a water ecological environment problem dataset for key city-level catchment units.

[0060] Specifically, in S100, refer to Figure 2The acquisition of target area watershed catchment unit data based on resistance factors includes:

[0061] S101, Identification and Generalization of Water Systems

[0062] Using GIS tools, and taking into account both the natural characteristics and actual needs of the watershed, the natural water system (rivers, lakes, and reservoirs) is generalized into an applicable water system. Its spatial representation consists of several intersecting straight lines or curves, primarily in a dendritic or networked pattern. The stored information includes the main stream, major first-order and second-order tributaries, and tributaries containing major pollution sources, including:

[0063] Collection of large-scale vector (river systems, administrative divisions, etc.) and high-resolution raster (DEM, remote sensing images, etc.) data;

[0064] Digital elevation model (DEM) data preprocessing;

[0065] DEM extraction of river networks;

[0066] River network system verification.

[0067] In the identification and generalization of water systems, the principle is that each sub-basin can only have one main river, that is, one flow path, and the flow direction of all rivers in the sub-basin should point towards the main river, that is, towards the flow path.

[0068] S102. Determine the catchment area of ​​the corresponding watershed at the cross-section.

[0069] Control sections are selected from existing water quality monitoring sections, wherein the control sections are national control sections, which refer to water quality control sections designated by the state that can be monitored regularly. Preferably, the selection is based on the representativeness, sensitivity, and importance of each control section. In heavily polluted areas, the number of control sections can be increased, while in areas with little human activity and no major pollution sources affecting water quality, the number of control sections can be appropriately reduced.

[0070] Furthermore, using the selected control sections as nodes, and based on the generalized results of the water system, the catchment area of ​​the corresponding watershed is calculated by the minimum path resistance value.

[0071] Specifically, including:

[0072] S1021. Divide the study area into grids. Using the Analytic Hierarchy Process (AHP), perform weighted analysis on resistance factors including elevation, slope, aspect, relief, and roughness according to different terrain types (plains, hills, and mountains). Obtain the weight value of each resistance factor for the corresponding terrain. Calculate the resistance coefficient R for each spatial grid through weighted summation. For example, in one instance, resistance factors may include five influencing factors: elevation, slope, aspect, relief, and roughness. The weight values ​​of these five influencing factors differ for plains, hills, and mountains.

[0073] S1022. Establish a minimum cumulative resistance (MCR) model to calculate the direction and path of plastic flow of fluid particles in space, and obtain the minimum cost path from source to end:

[0074]

[0075] In the formula, D ij R is the spatial distance from source cell i to sink cell j; i R represents the drag coefficient present during the transition from source unit i to sink unit j. i The value of i determines the resistance value of the path from source unit i to sink unit j. Once i is determined, calculating the MCR requires selecting the path with the minimum resistance value over the spatial distance. This path conforms to the laws of liquid flow and is the easiest path for runoff diffusion.

[0076] In the grid module of GIS software, the MCR model can determine the minimum cost path (the path with the least resistance value) between the "source" and the "target" based on the flow pattern of surface runoff, that is, the easiest path for surface runoff to spread in the region, construct the surface runoff path and network structure, and thus obtain the catchment area of ​​the watershed corresponding to each cross section.

[0077] S103. Obtain watershed catchment unit data.

[0078] Using control sections (national-level monitoring stations) as nodes and maintaining the integrity of township administrative boundaries as a constraint, the land area of ​​the watershed catchment unit is formed by combining the administrative regions involved in the watershed between control nodes. If an administrative region has multiple water discharge destinations, its dominant destination should be determined by combining the minimum cost path, and it should be completely assigned to a certain watershed catchment unit. For administrative regions that are subject to greater human interference and involve sewage interception and diversion, their watershed catchment unit should be determined based on the actual drainage destination rather than the topography.

[0079] In S100, based on the identification and generalization of the water system, using control sections (national-level monitoring sections) as nodes, the minimum resistance cumulative cost distance model is employed to calculate the minimum path resistance value between each source unit (any unit except the sink unit) and the sink unit (the unit where the national-level monitoring section is located) in the spatial grid, thus obtaining the catchment area of ​​each section. Then, based on geospatial boundary fitting technology, and taking full account of the natural catchment and sewage discharge characteristics of the watershed, the watershed catchment unit is delineated by coupling administrative responsibility boundaries with the minimum cost path catchment area of ​​the watershed. The delineation process must ensure both the integrity of the administrative divisions and achieve full coverage of the watershed.

[0080] Specifically, in S200, spatial autocorrelation statistical analysis is performed on the concentrations of characteristic pollutants in the catchment units of the target area watershed to identify and determine the watersheds with prominent water environment problems, including:

[0081] S201. Based on the Local Moran's I index, spatial correlation analysis is performed on the characteristic pollutants of the watershed catchment units in the target area. The characteristic pollutants may be chemical oxygen demand, ammonia nitrogen, and total phosphorus.

[0082] S202. Based on the spatial correlation analysis results of characteristic pollutants in the catchment units of the target area, accurately identify and obtain areas with prominent water environment problems. The local Moran index is a method used to identify where spatial aggregation phenomena and outliers occur, and can be used to characterize the degree of aggregation of water ecological environment problems in different areas.

[0083] Furthermore, the formula for the local Moran index is:

[0084]

[0085] In the formula, It is a local Moran index. and They represent the catchment units of the target area's watershed. Its adjacent target area watershed catchment unit The attribute value, It is the mean of all unit attribute values. It is variance. This represents the spatial weight matrix, used to quantify the catchment units of the target region's watershed. Its adjacent target area watershed catchment unit The strength of the spatial relationship between them.

[0086] The local Moran index falls within the range of [-1, 1]. A positive value indicates that the location has similar attribute values ​​to other locations in its neighborhood, forming a "high-high" or "low-low" spatial clustering pattern. A negative value means that the location has opposite attribute values ​​to its neighbors, exhibiting a "high-low" or "low-high" distribution. A value close to 0 indicates that there is no significant spatial autocorrelation between the location and its neighbors, meaning that the distribution of attribute values ​​is random.

[0087] By using the local Moran index to perform spatial autocorrelation statistics on the concentration of characteristic pollutants in the target area's watershed catchment units, spatial clustering analysis of the concentration of major pollutants in the target area's watershed catchment units is conducted, accurately identifying watersheds with prominent water environment problems and improving the targeting of the next step of refining the catchment units of cities and prefectures.

[0088] Specifically, in S300, based on the identified watershed with prominent water environment problems, the watershed catchment units are refined using prefecture-level cities as the basic unit, resulting in prefecture-level city catchment unit data including:

[0089] Using prefecture-level cities as the basic unit, and utilizing national and local water quality monitoring sections (including national control sections, provincial control sections, municipal control sections, and county control sections), the watersheds with prominent water environment problems obtained in S200 are further refined according to the method of obtaining watershed catchment unit data in S100, forming prefecture-level city catchment units.

[0090] In S300, refining the watershed catchment units requires more detailed data on water systems, pollution source distribution, and water quality, and the administrative divisions need to be further refined from township administrative divisions to village-level administrative divisions.

[0091] Specifically, in S400, the construction of an evaluation index system for water ecological environment problems, and the determination of key city-level water catchment unit data from the city-level water catchment unit data based on the constructed evaluation index system for water ecological environment problems, includes:

[0092] S401. Constructing an evaluation index system for water ecological environment issues.

[0093] In this embodiment, the evaluation index system includes three primary indicators: water environment, water resources, and water ecology, as well as 12 secondary indicators, as shown in Table 1.

[0094] Table 1 Evaluation Index System for Water Ecological Environment Issues

[0095]

[0096] Table 1 lists five secondary indicators for the primary water environment indicators:

[0097] 1. Significant deterioration in water quality at cross-sections: The relevant indicators of county-level and above cross-sections (national, provincial, municipal, and county-level) all showed a deteriorating trend year-on-year and month-on-month. The deteriorating trend means that the water quality category of the cross-section has decreased or the concentration of the primary pollutant has increased by 20% or more.

[0098] 2. High non-point source pollution intensity: If the ratio of the concentration of the primary pollutant in the upstream catchment area of ​​a certain section after significant rainfall runoff within 24 hours to the average annual concentration of the same indicator in the section over the past 3 years is greater than 2, then the primary pollutant is determined by analyzing the section's water quality data over the past 3 years and evaluating the water quality category of the section using the single-factor evaluation method of the "Surface Water Environmental Quality Standard" (GB3838).

[0099] 3. The existence of Class V or worse control sections: There are Class V or worse control sections at the county level and above (national, provincial, municipal, and county levels), determined by the monthly cumulative average.

[0100] 4. There are instances of drinking water source quality exceeding standards: There are instances of drinking water source quality exceeding standards at the county level and above centralized drinking water sources;

[0101] 5. Existence of black and odorous water bodies: The city had black and odorous water bodies during the assessment year.

[0102] The primary water resources indicator includes two secondary indicators:

[0103] 1. Rivers are experiencing dry-up or interruption of flow (seasonal rivers are not considered): According to relevant monitoring data, major rivers within the city area are experiencing dry-up or interruption of flow.

[0104] 2. High intensity of water resource development and utilization: Compared with cities in the same river basin and with the same level of economic development, this city has a higher intensity of water resource development and utilization.

[0105] The primary water ecological indicators include five secondary indicators, namely:

[0106] 1. Degradation of water conservation areas: According to the comparison of relevant monitoring data, the vegetation coverage area of ​​the main urban water bodies' water conservation areas has decreased significantly year-on-year. This indicator is not considered for cities that have not designated water conservation areas.

[0107] 2. High risk of algal blooms in lakes and reservoirs: According to lake and reservoir monitoring data, the risk of algal blooms exceeds the yellow warning threshold;

[0108] 3. Low natural shoreline ratio of rivers and lakes: This refers to the ratio of the length of natural, undeveloped shoreline or shoreline that has basically achieved its ecological function through ecological restoration to the total shoreline length, which is less than 50%.

[0109] 4. Poor connectivity of river and lake systems: Compared with historical data, this usually refers to the 5-10 years prior to the assessment year, with a large number of sluice gates and dams per unit river length. Among them, sluice gates and dams with effective ecological protection measures can be excluded from the count.

[0110] 5. Biodiversity decline: By evaluating the biodiversity index of the city area, it was found that compared with the historical state, usually referring to the 5-10 years before the assessment year, the biodiversity index (BI) of the city area decreased by one or more levels. The calculation method of the regional biodiversity index refers to the "Regional Biodiversity Assessment Standard" (HJ 623).

[0111] This embodiment constructs an evaluation index system for water ecological environment problems at the prefecture-level city level from three dimensions: water environment, water resources, and water ecology. The evaluation is comprehensive and highly integrated, thereby achieving an effective evaluation of water ecological environment problems at the prefecture-level city level.

[0112] S402. Cities with water ecological environment problems are identified as key cities with water catchment units.

[0113] Specifically, in S500, the step of generating a water ecological environment problem dataset based on the determined key city catchment unit data and the corresponding water ecological environment problem indicators for the key city catchment units includes:

[0114] S501. Determine the water ecological environment problem indicators corresponding to each key city's water catchment unit obtained from S400, wherein each key city's water catchment unit includes at least one water ecological environment problem indicator.

[0115] S502. Combine all key city catchment units and their corresponding water ecological environment problem indicators to generate a water ecological environment problem dataset.

[0116] Example 2

[0117] As a specific embodiment of the present invention, this embodiment provides a method for diagnosing water ecological environment problems based on watershed spatial tracking and city-level spatial refinement. Taking the Yangtze River Basin as an example, the specific steps for diagnosing water ecological environment problems are as follows:

[0118] S100. Obtain target area watershed catchment unit data based on resistance factor.

[0119] The Analytic Hierarchy Process (AHP) was used to perform a weighted analysis of the influencing factors in the Yangtze River Basin under different topographic conditions, including elevation, slope, aspect, relief, and roughness, for plains, hills, and mountains. The distribution of different topographic features in the Yangtze River Basin is shown below. Figure 3As shown in Table 2, the resistance factor weights of the surface runoff confluence process under different topographic conditions in the plains, hills, and mountains of the Yangtze River Basin were obtained.

[0120] Table 2. Weight Distribution of AHP Resistance Factors in the Yangtze River Basin

[0121]

[0122] Initially, the Yangtze River basin was divided into 628 catchment units. Later, after further refinement and optimization of water quality monitoring sections, the Yangtze River basin was divided into 1252 catchment units, as detailed below. Figure 4 As shown.

[0123] S200. Perform spatial autocorrelation statistical analysis on the concentrations of characteristic pollutants in the catchment units of the target area's watersheds to identify and obtain watersheds with prominent water environment problems.

[0124] Based on the spatial correlation analysis of the local Moran index, spatial clustering analysis was conducted on the annual average concentrations of major pollutants (chemical oxygen demand, ammonia nitrogen, and total phosphorus) at water quality monitoring sections in the Yangtze River Basin in 2023 to identify areas with prominent water environment problems. The results are as follows: Figure 5 As shown.

[0125] According to the spatial autocorrelation analysis results, chemical oxygen demand, ammonia nitrogen, and total phosphorus all showed obvious spatial aggregation.

[0126] S300. Based on the identified watersheds with prominent water environment problems, the catchment units of the watersheds are refined using prefecture-level cities as the basic unit to obtain prefecture-level city catchment unit data.

[0127] Taking a specific prefecture-level city as an example, this study analyzes water environment issues in prominent river basins of the Yangtze River. The city has established 50 water quality monitoring sections, including 16 national-level, 18 provincial-level, and 16 municipal-level sections. Through further refinement, the city is divided into 44 catchment units, as detailed below. Figure 6 As shown.

[0128] S400. Construct an evaluation index system for water ecological environment problems, and based on the constructed evaluation index system for water ecological environment problems, determine key city-level water catchment unit data from the city-level water catchment unit data.

[0129] For each water catchment unit in the city, the water ecological environment problem indicators in the constructed water ecological environment problem indicator system are compared to identify the key water catchment units in the city from all the water catchment units in the city. For the 44 prefecture-level city catchment units identified according to S300, an analysis was conducted on 12 secondary indicators in the water ecological environment problem indicator system. All prefecture-level city catchment units with water ecological environment problems were designated as key prefecture-level city catchment units, while those without water ecological environment problem indicators were excluded. A total of 16 key prefecture-level city catchment units were selected, as shown in Table 3. Among them, the number of key prefecture-level city catchment units corresponding to a single secondary water ecological environment problem indicator varied. For example, the indicator of poor river and lake connectivity existed in all 6 key prefecture-level city catchment units, and the indicator of river drying up (excluding seasonal rivers) existed in all 4 key prefecture-level city catchment units. However, the indicators of drinking water source quality exceeding standards, black and odorous water bodies, high intensity of water resource development and utilization, and low natural shoreline ratio of rivers and lakes did not exist in any prefecture-level city catchment units.

[0130] Table 3. Number of water catchment units corresponding to the evaluation index system for water ecological environment issues in prefecture-level cities.

[0131]

[0132] S500. Based on the determined key city catchment area data and the corresponding water ecological environment problem indicators for the key city catchment areas, generate a water ecological environment problem dataset.

[0133] Based on the 16 key water catchment units identified in the city and their corresponding water ecological environment problem indicators, a water ecological environment problem dataset was generated to guide the refined management of the city's water ecological environment. The results are shown in Table 4.

[0134] Table 4 Dataset of Water Ecological Environment Issues in Key Cities' Catchment Units

[0135]

[0136] In this embodiment, according to S400, 16 key water catchment units of a certain city were identified, and a dataset of water ecological environment problems of key water catchment units was generated. The water ecological environment problems corresponding to each key water catchment unit were given. Each key water catchment unit corresponds to at least one water ecological environment problem. Among them, the water catchment unit of city number 14 corresponds to three water ecological environment problem indicators: poor connectivity of river and lake systems, decline in biodiversity, and degradation of water source conservation areas. It belongs to the key water catchment units that should be given special attention. Next are the water catchment units of city number 2, city number 16, and city number 20.

[0137] Example 3

[0138] As a specific embodiment of the present invention, this embodiment provides a water ecological environment problem diagnosis device based on watershed spatial tracking and city spatial refinement, including: a watershed catchment unit data acquisition module, a watershed identification module with prominent water environment problems, a city catchment unit data acquisition module, a key city catchment unit data determination module, and a water ecological environment problem dataset generation module.

[0139] Among them, the watershed catchment unit data acquisition module is used to acquire watershed catchment unit data of the target area based on the resistance factor;

[0140] The watershed identification module for water environment problems performs spatial autocorrelation statistical analysis on the concentration of characteristic pollutants in the catchment units of the target area watershed to identify and obtain watersheds with prominent water environment problems.

[0141] The prefecture-level city water catchment unit data acquisition module is used to refine the water catchment units of the watershed with prefecture-level cities as the basic unit based on the obtained watershed with prominent water environment problems, and obtain the prefecture-level city water catchment unit data.

[0142] The key city catchment unit data determination module is used to construct an evaluation index system for water ecological environment problems, and to determine key city catchment unit data from the city catchment unit data based on the constructed water ecological environment problem index system.

[0143] The water ecological environment problem dataset generation module is used to generate a water ecological environment problem dataset based on the determined key city water catchment unit data and the water ecological environment problem indicators corresponding to the key city water catchment units.

[0144] Example 4

[0145] As a specific embodiment of the present invention, this embodiment provides a device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in Embodiment 1.

[0146] Example 5

[0147] As a specific embodiment of the present invention, this embodiment provides a computer storage medium storing a computer program, which, when executed by a processor, is used to implement the steps of the method described in Embodiment 1.

[0148] The present invention provides a water ecological environment problem diagnosis method, device, equipment, and storage medium based on watershed spatial tracking and city-level spatial refinement. First, the target area is divided into watershed catchment units. Then, the key watershed catchment units with prominent water environment problems are focused through spatial autocorrelation statistical methods. At the city level, the relevant watershed catchment units are further refined into city-level catchment units. The water ecological environment problems at the city level are comprehensively assessed from the three dimensions of "water environment, water resources, and water ecology", so as to achieve watershed-level water environment problem focusing and city-level water ecological environment problem diagnosis.

[0149] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other way. Any modifications or equivalent changes made based on the technical essence of the present invention shall still fall within the scope of protection claimed by the present invention.

Claims

1. A method for diagnosing water ecological environment problems based on watershed spatial tracking and city-level spatial refinement, characterized in that, The method includes: Acquire watershed catchment unit data for the target area based on resistance factors, including elevation, slope, aspect, undulation, and roughness. The identification and generalization of water systems are carried out according to the principle that each sub-basin can only have one main river and that the flow direction of all rivers in the sub-basin should point to the main river. With the assistance of GIS tools, the natural characteristics and actual needs of the basin are taken into account in a comprehensive manner, and the natural water system is generalized into an applicable water system. The spatial expression is a number of intersecting straight lines or curves, in a tree-like or network-like form. The information stored includes the main stream, the main first-level tributaries and second-level tributaries, and the tributaries where the main pollution sources are located. Control sections are identified from existing water quality monitoring sections. Using these control sections as nodes, and based on the generalized results of the water system, the catchment area of ​​the corresponding watershed is calculated using the minimum path resistance value. This includes: The target area is divided into grids. The resistance factors are weighted according to different terrains, including plains, hills and mountains, using the hierarchical analysis method to obtain the weight value of each resistance factor for the corresponding terrain. The resistance coefficient for each spatial grid is obtained by weighted calculation. A minimum resistance cumulative cost distance model is established, and the direction and path of plastic flow of fluid particles in the space are calculated based on the resistance coefficient of each spatial grid to obtain the minimum cost path from the source to the end. The minimum resistance cumulative cost distance model is as follows: In the formula, D ij R is the spatial distance from source cell i to sink cell j; i R represents the drag coefficient present during the transition from source unit i to sink unit j. i The value of i determines the different resistance values ​​generated by the path from source unit i to sink unit j. Once i is determined, calculating the MCR requires selecting the path with the least resistance value in the spatial distance. This path conforms to the laws of liquid flow and is the easiest path for runoff diffusion. Based on the minimum cost path, surface runoff paths and network structures are constructed to obtain the watershed catchment area corresponding to the cross-section. Based on geospatial boundary fitting technology, and taking into full account the natural water catchment and sewage discharge characteristics of the watershed, the control section is used as the node, and the integrity of the township administrative boundary is used as the constraint. The administrative responsibility boundary and the watershed catchment range are coupled, and the administrative regions involved in the watershed between the control nodes are combined to form the land area of ​​the watershed catchment unit, thus obtaining the target area watershed catchment unit. Spatial autocorrelation statistical analysis was performed on the concentrations of characteristic pollutants in the catchment units of the target area watershed to identify and obtain watersheds with prominent water environment problems, including: Spatial correlation analysis of characteristic pollutants in the catchment units of the target area's watershed was conducted based on the local Moran's index. Based on the results of this analysis, watersheds with prominent water environment problems were identified. The characteristic pollutants included chemical oxygen demand (COD), ammonia nitrogen, and total phosphorus. The formula for the local Moran's index is: In the formula, It is a local Moran index, located in the interval [-1, 1]. and They represent the catchment units of the target area's watershed. Its adjacent target area watershed catchment unit The attribute value, It is the average of the attribute values ​​of all watershed catchment units in the target area. It is variance. This represents the spatial weight matrix, used to quantify the catchment units of the target region's watershed. Its adjacent target area watershed catchment unit The strength of the spatial relationship between them, the local Moran index is used to characterize the degree of aggregation of aquatic ecological environment problems in different regions; Based on the identified watersheds with prominent water environment problems, the watershed catchment units are further refined using prefecture-level cities as the basic unit to obtain prefecture-level city catchment unit data. This includes: using prefecture-level cities as the basic unit, and utilizing national and local water quality monitoring sections, the watersheds with prominent water environment problems are further refined according to the method for obtaining watershed catchment unit data to form prefecture-level city catchment units, wherein the administrative divisions are refined to the village-level administrative divisions. An evaluation index system for water ecological environment issues is constructed, which includes three primary indicators (water environment, water resources, and water ecology) and twelve secondary indicators. Based on the constructed evaluation index system for water ecological environment issues, key city-level water catchment unit data are determined from the city-level water catchment unit data. Based on the identified key municipal catchment units and the corresponding water ecological environment problem indicators, a water ecological environment problem dataset is generated, including: The water ecological environment problem indicators corresponding to each key city's catchment area are determined, and each key city's catchment area includes at least one secondary water ecological environment problem indicator. All key cities' catchment areas and their corresponding water ecological environment problem indicators are combined to generate a water ecological environment problem dataset.

2. The method for diagnosing water ecological environment problems based on watershed spatial tracking and city-level spatial refinement according to claim 1, characterized in that, The identified and generalized water system includes: Large-scale vector and high-resolution raster data collection for the target area; DEM data preprocessing; DEM extraction of river networks; River network system verification.

3. The method for diagnosing water ecological environment problems based on watershed spatial tracking and city-level spatial refinement according to claim 1, characterized in that, The construction of the water ecological environment problem evaluation index system includes the following: The water environment indicators include five secondary indicators: significant deterioration of cross-sectional water quality, high intensity of non-point source pollution, existence of control sections with water quality worse than Class V, existence of drinking water source water quality exceeding standards, and existence of black and odorous water bodies; The water resources indicators include two secondary indicators: rivers experiencing flow interruption and drying up, and high intensity of water resources development and utilization. The water ecological indicators include five secondary indicators: degradation of water source conservation areas, high risk of algal blooms in lakes and reservoirs, low natural shoreline ratio of rivers and lakes, poor connectivity of river and lake systems, and decline in biodiversity.

4. A diagnostic device for water ecological environment problems based on watershed spatial tracking and city-wide spatial refinement, characterized in that, It includes a watershed catchment unit data acquisition module, a watershed identification module for prominent water environment problems, a prefecture-level city catchment unit data acquisition module, a key prefecture-level city catchment unit data identification module, and a water ecological environment problem dataset generation module; The watershed catchment unit data acquisition module is used to acquire target area watershed catchment unit data based on resistance factors. These resistance factors include elevation, slope, aspect, undulation, and roughness. The identification and generalization of water systems are carried out according to the principle that each sub-basin can only have one main river and that the flow direction of all rivers in the sub-basin should point to the main river. With the assistance of GIS tools, the natural characteristics and actual needs of the basin are taken into account in a comprehensive manner, and the natural water system is generalized into an applicable water system. The spatial expression is a number of intersecting straight lines or curves, in a tree-like or network-like form. The information stored includes the main stream, the main first-level tributaries and second-level tributaries, and the tributaries where the main pollution sources are located. Control sections are identified from existing water quality monitoring sections. Using these control sections as nodes, and based on the generalized results of the water system, the catchment area of ​​the corresponding watershed is calculated using the minimum path resistance value. This includes: The target area is divided into grids. The resistance factors are weighted according to different terrains, including plains, hills and mountains, using the hierarchical analysis method to obtain the weight value of each resistance factor for the corresponding terrain. The resistance coefficient for each spatial grid is obtained by weighted calculation. A minimum resistance cumulative cost distance model is established, and the direction and path of plastic flow of fluid particles in the space are calculated based on the resistance coefficient of each spatial grid to obtain the minimum cost path from the source to the end. The minimum resistance cumulative cost distance model is as follows: In the formula, D ij R is the spatial distance from source cell i to sink cell j; i R represents the drag coefficient present during the transition from source unit i to sink unit j. i The value of i determines the different resistance values ​​generated by the path from source unit i to sink unit j. Once i is determined, calculating the MCR requires selecting the path with the least resistance value in the spatial distance. This path conforms to the laws of liquid flow and is the easiest path for runoff diffusion. Based on the minimum cost path, surface runoff paths and network structures are constructed to obtain the watershed catchment area corresponding to the cross-section. Based on geospatial boundary fitting technology, and taking into full account the natural water catchment and sewage discharge characteristics of the watershed, the control section is used as the node, and the integrity of the township administrative boundary is used as the constraint. The administrative responsibility boundary and the watershed catchment range are coupled, and the administrative regions involved in the watershed between the control nodes are combined to form the land area of ​​the watershed catchment unit, thus obtaining the target area watershed catchment unit. The watershed identification module for prominent water environment problems performs spatial autocorrelation statistical analysis on the concentrations of characteristic pollutants in the catchment units of the target area watershed to identify and obtain watersheds with prominent water environment problems, including: Spatial correlation analysis of characteristic pollutants in the catchment units of the target area's watershed was conducted based on the local Moran's index. Based on the results of this analysis, watersheds with prominent water environment problems were identified. The characteristic pollutants included chemical oxygen demand (COD), ammonia nitrogen, and total phosphorus. The formula for the local Moran's index is: In the formula, It is a local Moran index, located in the interval [-1, 1]. and They represent the catchment units of the target area's watershed. Its adjacent target area watershed catchment unit The attribute value, It is the average of the attribute values ​​of all watershed catchment units in the target area. It is variance. This represents the spatial weight matrix, used to quantify the catchment units of the target region's watershed. Its adjacent target area watershed catchment unit The strength of the spatial relationship between them, the local Moran index is used to characterize the degree of aggregation of aquatic ecological environment problems in different regions; The municipal catchment unit data acquisition module is used to refine the catchment units of the watershed with the prefecture-level city as the basic unit based on the obtained watershed with prominent water environment problems, and to obtain municipal catchment unit data. This includes: using the prefecture-level city as the basic unit, using national and local water quality monitoring sections, and following the method for acquiring watershed catchment unit data, refining the watershed with prominent water environment problems to form municipal catchment units, wherein the administrative divisions are refined to the village-level administrative divisions; The key city catchment unit data determination module is used to construct an evaluation index system for water ecological environment issues. The evaluation index system for water ecological environment issues includes three primary indicators (water environment, water resources, and water ecology) and twelve secondary indicators. Based on the constructed evaluation index system for water ecological environment issues, the module determines the key city catchment unit data from the city catchment unit data. A water ecological environment problem dataset generation module is used to generate a water ecological environment problem dataset based on the determined key city catchment unit data and the corresponding water ecological environment problem indicators for the key city catchment units, including: The water ecological environment problem indicators corresponding to each key city's catchment area are determined, and each key city's catchment area includes at least one secondary water ecological environment problem indicator. All key cities' catchment areas and their corresponding water ecological environment problem indicators are combined to generate a water ecological environment problem dataset.

5. An apparatus comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1-3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-3.

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