Artificial precipitation enhancement influence ecological analysis method based on regional dynamic matching

Abstract

The invention relates to the technical field of artificial rainfall, in particular to an ecological analysis method for influence of artificial rainfall enhancement on the basis of regional dynamic matching. The method comprises the following steps: acquiring artificial precipitation enhancement operation information of a target area and ecological information of the target area; according to the topographic relief and water system distribution difference of each local area in the target area ecological information, establishing ecological state description corresponding to each analysis unit; time evolution characteristics of an artificial rainfall process are constructed based on occurrence time periods of all operations in the artificial rainfall enhancement operation information and corresponding meteorological condition changes; based on the ecological state description of each analysis unit, determining an effective matching time window corresponding to each analysis unit; and extracting ecological state change data in the effective matching time window to calculate the ecological influence degree of each analysis unit. According to the method, the dynamic influence of artificial precipitation on multi-dimensional ecological indexes such as water circulation, soil humidity and vegetation growth can be carefully captured, and the accuracy, pertinence and operability of evaluation are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of artificial rainfall technology, and in particular to a method for ecological analysis of the impact of artificial rainfall enhancement based on regional dynamic matching. Background Technology

[0002] Early research on artificial rain enhancement mainly focused on improving precipitation efficiency, analyzing the mechanisms of cloud physics processes, and evaluating the effectiveness of rain enhancement materials. Quantitative evaluations of rain enhancement effects were achieved through methods such as cloud observation, radar echo analysis, and statistical analysis of measured precipitation. However, current technologies still have significant shortcomings in analyzing the impact of rain enhancement activities on the ecological environment.

[0003] Specifically, traditional research methods typically rely on macroscopic precipitation statistics or single-point observation data, which are insufficient to reflect the dynamic response of regional ecosystems to artificial rain enhancement. Existing ecological analysis methods are mostly based on historical averages of vegetation cover, soil moisture, or hydrological indicators, lacking refined simulation and matching of the spatiotemporal dynamics of rain enhancement. Therefore, artificial rain enhancement activities may have complex impacts on local water cycles, soil nutrient status, plant growth, and biodiversity, and existing methods struggle to achieve dynamic correlation analysis and causal inference across spatiotemporal scales. Summary of the Invention

[0004] Therefore, it is necessary for the present invention to provide an ecological analysis method for artificial rain enhancement based on regional dynamic matching, in order to solve at least one of the above-mentioned technical problems.

[0005] To achieve the above objectives, a method for ecological analysis of the impacts of artificial rain enhancement based on regional dynamic matching is proposed, comprising the following steps:

[0006] Step S1: Obtain information on artificial rain enhancement operations and ecological information of the target area;

[0007] Step S2: Based on the differences in topographic relief and water system distribution in the local areas of the target area's ecological information, the target area is divided into multiple analysis units, and an ecological status description corresponding to each analysis unit is established.

[0008] Step S3: Based on the occurrence time of each artificial rain enhancement operation and the corresponding changes in meteorological conditions in the artificial rain enhancement operation information, construct the temporal evolution characteristics of the artificial rainmaking process;

[0009] Step S4: Based on the ecological state description of each analysis unit, calculate the impact delay time and impact duration between the artificial rain enhancement operation and the analysis unit to determine the effective matching time window for each analysis unit;

[0010] Step S5: Extract the ecological state change data within the effective matching time window, and determine the magnitude and trend of ecological state change in order to calculate the ecological impact of each analysis unit.

[0011] This application constructs an ecological state description system based on spatial analysis units, dynamically matching the timing, coverage, and meteorological conditions of artificial rain enhancement operations with ecological information such as topographic relief, water system distribution, and vegetation type of each unit. This enables high-precision simulation of the regional ecosystem's response to rain enhancement events. By calculating the delay time, duration of impact, and magnitude and trend of ecological state changes, this method can quantify the response intensity and persistence of each analysis unit, thereby identifying candidate sensitive impact areas and conducting review and verification to ultimately determine the sensitive impact areas. This method can span spatiotemporal scales, precisely capturing the dynamic impacts of artificial rain enhancement on multidimensional ecological indicators such as water cycle, soil moisture, vegetation growth, and biodiversity. It achieves accurate assessment of regional ecological impacts, providing a scientific basis for ecological management decisions and optimization of artificial rain enhancement operations, significantly improving the accuracy, relevance, and operability of the assessment. Attached Figure Description

[0012] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0013] Figure 1 This is a schematic diagram of the steps in the ecological analysis method for artificial rain enhancement based on regional dynamic matching of the present invention.

[0014] Figure 2 This is a schematic diagram of the spatial grid division results in an embodiment of the present invention;

[0015] Figure 3 This is a visual schematic diagram illustrating the temporal evolution characteristics of the artificial rainfall process in an embodiment of the present invention;

[0016] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0018] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0019] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0020] To achieve the above objectives, please refer to Figures 1 to 3 This invention provides a method for ecological analysis of the impact of artificial rain enhancement based on regional dynamic matching, the method comprising the following steps:

[0021] Step S1: Obtain information on artificial rain enhancement operations and ecological information of the target area;

[0022] In this embodiment, information on artificial rain enhancement operations in the target area is obtained through a meteorological monitoring platform. This information includes the start and end times of the operation, the coverage area, and the type of rain enhancement materials used. Ecological information of the target area is collected using remote sensing imagery and high-resolution DEM (Digital Elevation Model) data. This information includes topographic elevation, the location of surface water systems, and vegetation types. The operation information is stored in a time-series matrix format, while the ecological information is organized using a spatial raster data structure.

[0023] Step S2: Based on the differences in topographic relief and water system distribution in the local areas of the target area's ecological information, the target area is divided into multiple analysis units, and an ecological status description corresponding to each analysis unit is established.

[0024] In a further embodiment, the target area is first divided into several spatial grids. Based on the topographic elevation variation and water system distribution characteristics of the spatial grids, the differences in topographic slope and the consistency of water system connectivity directions between adjacent grids are calculated. A hydro-topographic analysis model is used to determine the dominant direction of water discharge, and the angular deviation between adjacent grids is calculated. Combining the slope difference with the consistency of water conduction, the analysis unit division is determined by an average score method, and grids with consistent characteristics are merged. The ecological state description includes: the proportion of each vegetation type within the grid, the spatial density of the water system, the main direction of water discharge, and the topographic elevation variation.

[0025] Step S3: Based on the occurrence time of each artificial rain enhancement operation and the corresponding changes in meteorological conditions in the artificial rain enhancement operation information, construct the temporal evolution characteristics of the artificial rainmaking process;

[0026] In a further embodiment, time reference points are established based on the start and end times of artificial rain enhancement operations, and meteorological condition data (precipitation, temperature, humidity, and wind speed) of the covered area are acquired. An artificial rainfall temporal evolution feature matrix is ​​constructed, where rows represent operational events, columns represent time steps (which can be set to 5 minutes), and matrix elements represent precipitation changes at corresponding times. The rainfall change trends before and after each operation are identified, and the precipitation curve is smoothed using a moving average filter (15-minute window) to reduce short-term fluctuation interference and provide continuous and reliable rainfall evolution features for subsequent time matching.

[0027] Step S4: Based on the ecological state description of each analysis unit, calculate the impact delay time and impact duration between the artificial rain enhancement operation and the analysis unit to determine the effective matching time window for each analysis unit;

[0028] In a further embodiment, the impact delay time and impact duration are calculated based on the evolution characteristics of artificial rainfall and the trend of changes in water system spatial density. The impact delay time is obtained by comparing the difference between the time at which significant changes in precipitation occur (precipitation greater than the weighted average) and the time at which changes in water system density begin. The impact duration is calculated by identifying the time length between inflection points in adjacent directions of the water system density trend curve, with a minimum time interval set to 30 minutes. The impact delay time and impact duration are superimposed to determine the effective matching time window for the analysis unit, facilitating the screening of relevant ecological response data.

[0029] Step S5: Extract the ecological state change data within the effective matching time window, and determine the magnitude and trend of ecological state change in order to calculate the ecological impact of each analysis unit.

[0030] In a further embodiment, ecological state change data (including vegetation type change data, water system spatial density change data, and water discharge dominant direction change data) within an effective matching time window are extracted from the target area's ecological information. The magnitude of the change is calculated as the response intensity, and the first-order difference divergence of the change trend slope is used as the continuity. The ecological impact of each analysis unit is calculated by weighted averaging, with a magnitude weight of 0.7 and a continuity weight of 0.3.

[0031] Optionally, step S5 may be followed by:

[0032] If the ecological impact of any analysis unit is greater than the preset impact threshold, then the analysis unit is marked as a candidate sensitive impact area.

[0033] In another embodiment, the ecological impact matrix of the analysis unit is compared with a preset impact threshold (e.g., 0.6) unit by unit, and the analysis units with ecological impact greater than the threshold are identified as candidate sensitive impact areas.

[0034] The system detects the start time of ecological state changes within candidate sensitive impact areas, compares the start time of ecological state changes with the impact delay time, and if the deviation of the comparison result is greater than the preset time offset tolerance range, the start time of the effective matching time window corresponding to the candidate sensitive impact area is adjusted to update the effective matching time window corresponding to the candidate sensitive impact area.

[0035] In a further embodiment, the start time of ecological state change in candidate sensitive impact areas within the effective matching time window is extracted. This can be done by comparing the impact delay time using a time series matrix to calculate the time difference. If the time difference exceeds the allowable time offset range (±10 minutes), the start time of the effective matching time window for the candidate area is adjusted to make the window start closer to the actual start of the ecological response. For example, if the original effective matching time window start time is 08:00 and the ecological state change start time is 08:12, then the effective matching time window start time is adjusted backward by 12 minutes to 08:12, while keeping the window length unchanged at 120 minutes.

[0036] The ecological impact update value of the candidate sensitive impact area is determined based on the update results of the effective matching time window. If the ecological impact update value is greater than the impact threshold, the candidate sensitive impact area is determined as a sensitive impact area, and a sensitive impact area analysis report is generated.

[0037] In a further embodiment, the magnitude and trend of ecological state changes are recalculated within the updated window to obtain an updated ecological impact value. If the updated ecological impact value is still greater than the threshold, the area is identified as a sensitive impact area, and a sensitive impact area analysis report is generated, including the area number, location, impact value, and time window information. The report is output in a combination of structured data tables and visual maps.

[0038] Optionally, the method for dividing the analysis unit in step S2 includes:

[0039] Based on the spatial range of the target area, the target area is divided into multiple spatial grids, and the topographic elevation variation and water system distribution characteristics of each spatial grid are extracted from the ecological information of the target area.

[0040] In this embodiment, the target area is divided into an initial spatial grid, with each grid having a side length of 50 meters × 50 meters to ensure spatial resolution accuracy consistent with subsequent analysis unit division. The topographic elevation variation within each grid is calculated from the DEM (Digital Elevation Model) data in the target area's ecological information. Simultaneously, the distribution location and area proportion of water bodies within each grid are extracted using water system vector data (obtained from remote sensing imagery or existing water system GIS maps, with each water system represented by its start and end coordinates and linear geometric information). The spatial grid and its attribute information are stored in a two-dimensional raster matrix, with row and column indices corresponding to grid positions, and matrix elements representing elevation variation or water body proportions.

[0041] The consistency of water accumulation conditions between adjacent spatial grids can be determined by the difference in the range of topographic elevation changes.

[0042] In a further embodiment, the similarity of slope changes between adjacent grids and the angular deviation of the dominant direction of water outflow are calculated to determine the consistency of water convergence conditions. The slope change similarity is calculated using normalized Euclidean distance, and the angular deviation is converted into a consistency score using cosine similarity. The average of the two is the water convergence consistency score. Data is stored in the form of a list of adjacent grid pairs, with each record containing two grid numbers and a consistency score, facilitating subsequent merging operations.

[0043] Compare the water system distribution characteristics of adjacent spatial grids to determine the consistency of water conduction paths between adjacent spatial grids;

[0044] In a further embodiment, the water system vectors within each grid are superimposed based on the water system distribution characteristics of adjacent spatial grids. The system checks whether the end point forms a spatial connection with the starting segment of the adjacent grid, and determines whether the water system connection direction is continuous. The water system connection is determined based on the end point falling within a 10-meter buffer zone of the adjacent grid, and the connection direction must be continuous. The water conduction consistency score can be taken as the proportion of the continuous length of the connection direction to the total water system length of the grid.

[0045] If the consistency of water accumulation conditions and water conduction paths of adjacent spatial grids both meet the preset consistency score threshold, then the adjacent spatial grids will be merged as analysis units.

[0046] In a further embodiment, grids with an average score of 0.85 or greater for both water accumulation condition consistency and water transport path consistency among adjacent grids are merged into an analysis unit. Taking an initial grid side length of 50 meters as an example, the merged analysis unit can be dynamically expanded to no more than 200 meters × 200 meters to maintain ecological homogeneity. The merging results are stored in the form of an analysis unit number list and a corresponding matrix containing grid numbers.

[0047] Figure 2 This is a schematic diagram of the spatial grid division results in an embodiment of the present invention; as shown below. Figure 2 As shown, the red text represents the elevation variation of each spatial grid, the blue solid line R represents perennial rivers, the blue dashed line S represents seasonal streams, the blue circle L represents lakes / wetlands, and N represents a spatial grid that does not contain water systems. Figure 2 In this study, spatial grids with similar water system conditions and similar elevation amplitudes are divided into one analysis unit, such as the first analysis unit represented by reddish-brown and the second analysis unit represented by light blue. Figure 2 The white space grid that has not been divided into analysis units is an independent grid that does not meet the merging conditions.

[0048] Optionally, determining the consistency of water convergence conditions among adjacent spatial grids includes:

[0049] Based on the elevation variation range of each spatial grid, the main direction of elevation change from high to low within each spatial grid is determined, and the similarity of elevation slope variation between adjacent spatial grids is calculated.

[0050] The main direction of change is taken as the dominant direction of water discharge in each spatial grid;

[0051] In this embodiment, based on the elevation change range of each spatial grid, the ratio of elevation change rates along the east-west and north-south directions is calculated. The direction with the highest elevation change rate from high to low is determined as the dominant downward direction, and this dominant downward direction is used as the dominant direction of water outflow for each spatial grid. By comparing the elevation change range of adjacent grids, the slope change similarity is calculated. The elevation change ranges of two grids are normalized, and the cosine similarity ratio is calculated to obtain a similarity value between 0 and 1.

[0052] Calculate the angular deviation between the dominant directions of water discharge in adjacent spatial grids to determine the similarity of water discharge trends;

[0053] In a further embodiment, the angular deviation between the dominant directions of water outflow in adjacent spatial grids is calculated, and the angular deviation is mapped to a trend similarity of 0 to 1, with a larger value indicating a more consistent outflow direction.

[0054] If the similarity of terrain slope change and the similarity of water discharge trend of any adjacent spatial grid are both greater than the preset similarity threshold, then the adjacent spatial grid is determined to have consistent water convergence conditions, and the average of the similarity of terrain slope change and the similarity of water discharge trend is used as the water convergence consistency score.

[0055] In a further embodiment, if the similarity of terrain slope change and the similarity of water discharge trend of any adjacent spatial grid are both greater than the similarity threshold (e.g., 0.85), then the grid pair is determined to have consistency in water convergence conditions, and the average of the similarity of terrain slope change and the similarity of water discharge trend is used as the water convergence consistency score.

[0056] Optionally, determining the consistency of water conduction paths between adjacent spatial grids includes:

[0057] Based on the water system distribution characteristics of the spatial grid, determine the distribution location of the water system and the corresponding connection direction within each spatial grid;

[0058] In this embodiment, based on the water system distribution characteristics of the spatial grid, the water system vectors in each spatial grid are clipped according to the grid boundary, and the water system grid of each water body unit is represented in a 10m×10m rasterization method. Then, by calculating the water flow vector in each water system grid (the unit vector from the start point to the end point represents the water flow direction), the main flow direction of the water system (0°~360°) is obtained.

[0059] It is worth noting that while cropping the water system vector, other areas within the spatial grid are also cropped to complete the overall rasterization of the spatial grid.

[0060] The distribution locations of water systems within adjacent spatial grids are superimposed to determine the adjacent spatial grids where water systems are interconnected.

[0061] In a further embodiment, the water system grids of two grids are superimposed, and it is identified whether the end grid falls into the buffer zone of the starting segment of the adjacent grid (the buffer zone is set to 10 meters). If the end grid is continuous with the starting segment of the adjacent grid, and the length of the starting segment is ≥ 30% of the total length of the water system of the grid, then the water systems of the two grids are determined to be spatially connected. The connection status of each grid pair is stored as a Boolean value, along with the number of connecting grids and the length of the starting segment, to facilitate the calculation of the consistency score.

[0062] If the water systems in any adjacent spatial grid are connected to each other, and the corresponding connection direction remains continuous between adjacent spatial grids, then the water conduction path of adjacent spatial grids is consistent.

[0063] The continuity of the connectivity direction of adjacent spatial grids with the same water conduction path is used as the water conduction consistency score.

[0064] In a further embodiment, if the spatial connection between adjacent grid water systems is established, the angular difference of the main flow direction of the connecting grids is compared. If the angular difference is ≤15°, the direction is determined to be continuous. The degree of continuity of the connection direction is calculated by the proportion of the length of the continuous grid to the total length of the water system. For example, if the continuous length of adjacent grids is 80 meters and the total length of the water system is 100 meters, then the degree of continuity of the connection direction is 0.8. Subsequently, the degree of continuity of the connection direction is used as the consistency score of water conduction.

[0065] Of particular importance is determining the interconnected spatial grids of the water systems, including:

[0066] The positional relationship between the end and beginning segments of the water system within adjacent spatial grids is compared based on the superposition results.

[0067] When the end of a water system within any spatial grid falls within the boundary buffer zone of an adjacent spatial grid, and the end of the water system connects to the continuously distributed starting segments in the adjacent spatial grid, it is determined that the water systems between the adjacent spatial grids form a spatial connection relationship.

[0068] In this embodiment, based on the water system grid overlay results, the coordinates of the end grid cells within each grid are extracted and spatially compared with the coordinates of the starting grid cells of adjacent grids. The spatial deviation value between grid cells is obtained by calculating the Euclidean distance between the nearest point of the end grid cell and the starting grid cell of the adjacent grid. A boundary buffer range of 10-15 meters is set. If the deviation value of any end grid cell is less than or equal to the buffer range, it indicates that the end cell falls into the potential connection area of ​​the adjacent grid's water system. If the end grid cell falls into the buffer range of the adjacent grid, it is further determined whether the starting grid cell of the adjacent grid is continuous. By counting the number of continuous water grid cells in the starting grid cell of the adjacent grid and calculating the proportion of its length to the total length of the adjacent grid's water system, for example, if the continuous segment length is ≥30 meters and the proportion is ≥30%, a spatial connection relationship is determined to be formed.

[0069] Optionally, the ecological state descriptions for each analysis unit established in step S2 include:

[0070] Based on the spatial range of each analysis unit, the corresponding vegetation type information is extracted from the ecological information of the target area;

[0071] In this embodiment, vegetation type data corresponding to the spatial range is extracted from the ecological information of the target area based on the boundary coordinates (including the boundary buffer range) of each analysis unit. Vegetation types are represented in the form of vector polygons or raster, and each pixel or polygon records the vegetation category number (e.g., grassland=1, shrubs=2, woodland=3).

[0072] Based on the vegetation type information corresponding to each analysis unit, the distribution ratio of different vegetation types is statistically analyzed, and the spatial density and distribution direction of the water system are determined based on the water system distribution characteristics corresponding to each analysis unit.

[0073] In a further embodiment, the proportion of each type of vegetation to the total area of ​​the analysis unit is calculated. For example, if the total area of ​​an analysis unit is 10,000... ², grassland area 5000 If the grassland percentage is 0.5, then the spatial density of the water system is calculated based on the proportion of water system grid cells to the total number of grid cells in the unit. For example, if there are 500 water system grid cells in a certain analysis unit and a total of 10,000 grid cells, the density is 0.05. The direction vectors of the water system grid cells are extracted, and the average direction angle (0°~360°) is calculated as the direction of the water system distribution. This is stored in an array and corresponds to the analysis unit number and the water system density.

[0074] The range of topographic elevation change, the dominant direction of water discharge, the distribution ratio of different vegetation types, and the spatial density and distribution direction of water systems are used as ecological status descriptions for the corresponding analysis units.

[0075] In a further embodiment, the elevation change range, the dominant direction of water discharge, the distribution ratio of different vegetation types, and the spatial density and distribution direction of the water system are stored in a structured array as analysis units. The array fields include elevation change range (meters), dominant direction of water discharge (°), proportion of each vegetation type (0~1), spatial density of the water system (0~1), and distribution direction of the water system (°).

[0076] Optionally, the temporal evolution characteristics of the artificial rainfall process constructed in step S3 include:

[0077] Based on the information on artificial rain enhancement operations, the start time, end time, and coverage area of ​​each operation are determined, and the start time and end time are used as time reference points.

[0078] In this embodiment, the start and end times of each artificial rain enhancement operation are extracted based on the artificial rain enhancement operation information, accurate to the minute, for example, start time 08:15 and end time 09:00. The operation coverage area is represented by the operation flight path or operation sector area in the Geographic Information System (GIS), and each operation coverage area is stored as a polygon coordinate array, including latitude and longitude or projected coordinates.

[0079] Obtain meteorological condition information for the target area and extract the meteorological condition changes for the corresponding time reference point and the corresponding operation coverage area;

[0080] In a further embodiment, meteorological observation or numerical simulation data of the target area is obtained through a meteorological monitoring platform. Then, meteorological conditions within the operational coverage area are extracted at each time reference point, including precipitation (mm), precipitation intensity (mm / h), wind speed (m / s), and humidity (%). The raster data of meteorological conditions within each operational coverage area are projected according to the analysis unit, and the average value of each parameter within the coverage area is calculated. For example, the average precipitation within a certain operational coverage area is 3mm, and the average precipitation intensity is 1.2mm / h.

[0081] Based on the changes in meteorological conditions at each time reference point, the rainfall change trends before and after each operation are identified, and the continuous changes in precipitation amount and intensity between time reference points are correlated in the rainfall change trends to construct the temporal evolution characteristics of the artificial rainfall process.

[0082] In a further embodiment, based on changes in meteorological conditions at each time reference point, the trends in precipitation and precipitation intensity before and after the operation are compared to identify time segments with significant rainfall changes. For example, if the baseline precipitation is 0.2 mm 10 minutes before the start of the operation and increases to 3.0 mm after the operation, a significant change is observed. Adjacent time reference points are used as time series, and the precipitation and precipitation intensity at each time reference point are correlated to form temporal evolution characteristics and corresponding visualized temporal evolution curves. The cumulative increment of rainfall changes due to the operation's impact is calculated. The temporal evolution curve is... Figure 3 As shown.

[0083] Optionally, step S4, calculating the impact delay time and impact duration between the artificial rain enhancement operation and the analysis unit, includes:

[0084] The study identifies time periods in the temporal evolution of artificial rainfall processes where precipitation amounts are greater than the weighted average precipitation value; simultaneously, it extracts the trend of water system spatial density changes based on the ecological state description of the analysis units.

[0085] In this embodiment, a weighted average of the precipitation time series is calculated based on the temporal evolution characteristics of the artificial rainfall process; for example, the weighted average precipitation is 2.5 mm. The time series is scanned, and time segments where precipitation is continuously higher than the weighted average are identified as segments of significant change. For example, if precipitation is continuously greater than 2.5 mm from 08:20 to 08:35, the start and end times of this time segment are recorded as segments of significant precipitation change. The time series of water system raster density for the corresponding time segment of the artificial rainfall process is extracted from the ecological state description of the analysis unit, and the proportion of water system to the total raster of the unit can be calculated every minute. The density time series is smoothed (e.g., by using a 5-minute moving average window) to obtain the trend of water system spatial density change. The start and inflection points of the trend also need to be recorded.

[0086] Compare the time difference between the time interval of significant precipitation change and the starting time of the trend of change in water system spatial density, and use the time difference result as the influence delay time;

[0087] In a further embodiment, the time difference between the time period of significant change in precipitation and the starting time of the trend of change in water system spatial density is calculated. For example, if the starting time of significant change in precipitation is 08:20 and the starting time of the trend of change in water system density is 08:25, then the time difference between the two is taken as 5 minutes, and the delay time is 5 minutes.

[0088] Based on the changing trend of water system spatial density, the time interval between two adjacent trend inflection points is determined, and the duration of this time interval is taken as the influence period.

[0089] In a further embodiment, based on the changing trend of water system spatial density, the time interval between two adjacent trend inflection points (such as local maximum and minimum values) is identified, and the duration is calculated. For example, the duration between the two inflection points of 08:25 and 08:40 is 15 minutes. This duration is used as the duration of the influence of artificial rain enhancement on the analysis unit.

[0090] Optionally, determining the effective matching time window corresponding to each analysis unit in step S4 includes:

[0091] The start time of the overlay operation and the impact delay time of the corresponding analysis unit are used as the effective start time of the response of the analysis unit.

[0092] In this embodiment, the start time of the task and the impact delay time are superimposed. For example, if the start time of the task is 08:15 and the impact delay time is 5 minutes, the superimposed result is 08:20, which is used as the effective start time of the response of the analysis unit.

[0093] The effective start time of the correlation is used to determine the duration of the influence of the analysis unit. The correlation result is used as the effective end time of the response of the analysis unit, and the time interval between the effective start time and the effective end time of the response is used as the effective matching time window.

[0094] In a further embodiment, the effective start time of the response is added to the duration of the impact to determine the effective end time of the response. For example, if the effective start time of the response is 08:20 and the duration of the impact is 15 minutes, then the effective end time of the response is 08:35. After determining the effective start time and end time of the response, the time interval formed by the two is used as the effective matching time window of the analysis unit. For example, 08:20~08:35 is the time window of this analysis unit.

[0095] Optionally, the determination of the ecological impact of each analysis unit in step S5 includes:

[0096] The response intensity of each analysis unit is determined based on the magnitude of changes in ecological status;

[0097] In this embodiment, the response intensity of each analysis unit is calculated based on the magnitude of ecological state changes within the effective matching time window. For example, the changes in vegetation index, surface water spatial density, and the magnitude of changes in the dominant direction of water discharge are normalized and then weighted and superimposed according to weight ratios of 0.4, 0.3, and 0.3 to obtain the response intensity of each analysis unit.

[0098] The persistence of each ecological response direction in each analysis unit is determined based on the trend of ecological state change, and the response intensity of each analysis unit is weighted and averaged with the persistence of each ecological response direction to obtain the ecological impact of the analysis unit.

[0099] In a further embodiment, based on the ecological state change trend of each analysis unit, continuous directional change segments are identified, and the first-order difference divergence of the length of each time segment and the slope of the change amplitude is calculated, so that the slope dispersion reflects the degree of continuity; combined with the length of each continuous time segment, the persistence of each ecological response direction of each analysis unit is obtained by weighted average method, for example, the weight of the length of the continuous time segment is 0.6, and the weight of the slope continuity is 0.4.

[0100] In a further embodiment, the response intensity of each analysis unit and the persistence of each ecological response direction are weighted and averaged, with a weight of 0.5 for response intensity, 0.3 for persistence of rising ecological response directions, and 0.3 for persistence of falling ecological response directions, to obtain the final ecological impact degree of the analysis unit. The final ecological impact degree value can be used for subsequent sensitive impact area determination and ecological response analysis.

[0101] Of particular importance is determining the persistence of each ecological response direction in each analysis unit, including:

[0102] Based on the trend of ecological state change, identify the directional inflection points of the ecological state change direction in each analysis unit, so as to determine the time length of adjacent directional inflection points;

[0103] In this embodiment, based on the ecological state change trend of each analysis unit within the effective matching time window, the directional inflection points of the ecological state change direction are identified, such as the local maximum value of the rising segment and the local minimum value of the falling segment, and the time length of adjacent directional inflection points is recorded.

[0104] Based on the time length of adjacent directional inflection points, select adjacent directional inflection points that exceed a preset continuous time length threshold as a single-directional continuous time period.

[0105] In a further embodiment, the time length of the inflection point in the adjacent direction is compared with a preset continuous time length threshold, for example, the threshold is set to 8 minutes, and the inflection points in the adjacent direction that exceed the threshold are selected as a continuous time period in one direction.

[0106] The first-order difference divergence of the slope of the change in ecological state in a single continuous time period is used as the degree of continuity. Combined with the corresponding time length, the persistence of each ecological response direction in each analysis unit is calculated.

[0107] In a further embodiment, for a continuous time period in one direction, the first-order difference divergence of the slope of the change in ecological state is calculated. This divergence is used as a continuity index, and the persistence of each ecological response direction of each analysis unit is calculated in a weighted manner in combination with the length of the continuous time period. For example, the weight of the length of the continuous time period is 0.6, and the weight of the slope continuity is 0.4, so as to obtain a quantifiable value of ecological response persistence.

[0108] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0109] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for ecological analysis of the impacts of artificial rain enhancement based on regional dynamic matching, characterized in that, Includes the following steps: Step S1: Obtain information on artificial rain enhancement operations and ecological information of the target area; Step S2: Based on the differences in topographic relief and water system distribution in the local areas of the target area's ecological information, the target area is divided into multiple analysis units, and an ecological status description corresponding to each analysis unit is established. Step S3: Based on the occurrence time of each artificial rain enhancement operation and the corresponding changes in meteorological conditions in the artificial rain enhancement operation information, construct the temporal evolution characteristics of the artificial rainmaking process; Step S4: Based on the ecological state description of each analysis unit, calculate the impact delay time and impact duration between the artificial rain enhancement operation and the analysis unit to determine the effective matching time window for each analysis unit; Step S5: Extract the ecological state change data within the effective matching time window, and determine the magnitude and trend of ecological state change in order to calculate the ecological impact of each analysis unit.

2. The method for ecological analysis of the impact of artificial rain enhancement based on regional dynamic matching according to claim 1, characterized in that, Step S5 is followed by: If the ecological impact of any analysis unit is greater than the preset impact threshold, then the analysis unit is marked as a candidate sensitive impact area. The system detects the start time of ecological state changes within candidate sensitive impact areas, compares the start time of ecological state changes with the impact delay time, and if the deviation of the comparison result is greater than the preset time offset tolerance range, the start time of the effective matching time window corresponding to the candidate sensitive impact area is adjusted to update the effective matching time window corresponding to the candidate sensitive impact area. The ecological impact update value of the candidate sensitive impact area is determined based on the update results of the effective matching time window. If the ecological impact update value is greater than the impact threshold, the candidate sensitive impact area is determined as a sensitive impact area, and a sensitive impact area analysis report is generated.

3. The method for ecological analysis of the impact of artificial rain enhancement based on regional dynamic matching according to claim 1, characterized in that, The method for dividing the analysis unit in step S2 includes: Based on the spatial range of the target area, the target area is divided into multiple spatial grids, and the topographic elevation variation and water system distribution characteristics of each spatial grid are extracted from the ecological information of the target area. The consistency of water accumulation conditions between adjacent spatial grids can be determined by the difference in the range of topographic elevation changes. Compare the water system distribution characteristics of adjacent spatial grids to determine the consistency of water conduction paths between adjacent spatial grids; If the consistency of water accumulation conditions and water conduction paths of adjacent spatial grids both meet the preset consistency score threshold, then the adjacent spatial grids will be merged as analysis units.

4. The method for ecological analysis of the impact of artificial rain enhancement based on regional dynamic matching according to claim 3, characterized in that, Determining the consistency of water convergence conditions between adjacent spatial grids includes: Based on the elevation variation range of each spatial grid, the main direction of elevation change from high to low within each spatial grid is determined, and the similarity of elevation slope variation between adjacent spatial grids is calculated. The main direction of change is taken as the dominant direction of water discharge in each spatial grid; Calculate the angular deviation between the dominant directions of water discharge in adjacent spatial grids to determine the similarity of water discharge trends; If the similarity of terrain slope change and the similarity of water discharge trend of any adjacent spatial grid are both greater than the preset similarity threshold, then the adjacent spatial grid is determined to have consistent water convergence conditions, and the average of the similarity of terrain slope change and the similarity of water discharge trend is used as the water convergence consistency score.

5. The method for ecological analysis of the impact of artificial rain enhancement based on regional dynamic matching according to claim 3, characterized in that, Determining the consistency of water transport paths between adjacent spatial grids includes: Based on the water system distribution characteristics of the spatial grid, determine the distribution location of the water system and the corresponding connection direction within each spatial grid; The distribution locations of water systems within adjacent spatial grids are superimposed to determine the adjacent spatial grids where water systems are interconnected. If the water systems in any adjacent spatial grid are connected to each other, and the corresponding connection direction remains continuous between adjacent spatial grids, then the water conduction path of adjacent spatial grids is consistent. The continuity of the connectivity direction of adjacent spatial grids with the same water conduction path is used as the water conduction consistency score.

6. The method for ecological analysis of the impact of artificial rain enhancement based on regional dynamic matching according to claim 1, characterized in that, Step S2 involves establishing the ecological state description for each analysis unit, including: Based on the spatial range of each analysis unit, the corresponding vegetation type information is extracted from the ecological information of the target area; Based on the vegetation type information corresponding to each analysis unit, the distribution ratio of different vegetation types is statistically analyzed, and the spatial density and distribution direction of the water system are determined based on the water system distribution characteristics corresponding to each analysis unit. The range of topographic elevation change, the dominant direction of water discharge, the distribution ratio of different vegetation types, and the spatial density and distribution direction of water systems are used as ecological status descriptions for the corresponding analysis units.

7. The method for ecological analysis of the impact of artificial rain enhancement based on regional dynamic matching according to claim 1, characterized in that, The temporal evolution characteristics of the artificial rainfall process constructed in step S3 include: Based on the information on artificial rain enhancement operations, the start time, end time, and coverage area of ​​each operation are determined, and the start time and end time are used as time reference points. Obtain meteorological condition information for the target area and extract the meteorological condition changes for the corresponding time reference point and the corresponding operation coverage area; Based on the changes in meteorological conditions at each time reference point, the rainfall change trends before and after each operation are identified, and the continuous changes in precipitation amount and intensity between time reference points are correlated in the rainfall change trends to construct the temporal evolution characteristics of the artificial rainfall process.

8. The method for ecological analysis of artificial rain enhancement impacts based on regional dynamic matching according to claim 1, characterized in that, Step S4 involves calculating the impact delay time and duration between the artificial rain enhancement operation and the analysis unit, including: The study identifies time periods in the temporal evolution of artificial rainfall processes where precipitation amounts are greater than the weighted average precipitation value; simultaneously, it extracts the trend of water system spatial density changes based on the ecological state description of the analysis units. Compare the time difference between the time interval of significant precipitation change and the starting time of the trend of change in water system spatial density, and use the time difference result as the influence delay time; Based on the changing trend of water system spatial density, the time interval between two adjacent trend inflection points is determined, and the duration of this time interval is taken as the influence period.

9. The method for ecological analysis of the impact of artificial rain enhancement based on regional dynamic matching according to claim 1, characterized in that, Step S4, which determines the effective matching time window for each analysis unit, includes: The start time of the overlay operation and the impact delay time of the corresponding analysis unit are used as the effective start time of the response of the analysis unit. The effective start time of the correlation is used to determine the duration of the influence of the analysis unit. The correlation result is used as the effective end time of the response of the analysis unit, and the time interval between the effective start time and the effective end time of the response is used as the effective matching time window.

10. The method for ecological analysis of the impact of artificial rain enhancement based on regional dynamic matching according to claim 1, characterized in that, Step S5, which determines the ecological impact of each analysis unit, includes: The response intensity of each analysis unit is determined based on the magnitude of changes in ecological status; The persistence of each ecological response direction in each analysis unit is determined based on the trend of ecological state change, and the response intensity of each analysis unit is weighted and averaged with the persistence of each ecological response direction to obtain the ecological impact of the analysis unit.

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