A global river blockage identification and evaluation method based on data features

Abstract

This invention belongs to the technical field of data identification, specifically a method for identifying and assessing global river blockages based on data features. The method includes: collecting water level data at river nodes and constructing river network topological adjacency relationships; determining multi-temporal river water level characteristics based on the determined river network topological adjacency relationships; extracting water level step change features along the river direction based on a determined joint longitudinal water level profile and determining candidate blockage points; simultaneously, performing cross-border spatial consistency aggregation analysis to determine actual river blockage points; and conducting seasonal difference analysis based on the actual river blockage points. This invention identifies blockages based on the discontinuous characteristics of river hydrodynamics, directly using the upstream and downstream water level changes caused by the blockage as a criterion to achieve the identification of the "functional impact" of river blockages.

Description

Technical Field

[0001] This invention belongs to the technical field of data identification, specifically a method for identifying and assessing global river blockages based on data features. Background Technology

[0002] Current methods for identifying and assessing the impact of river blockages (such as dams, sluice gates, weirs, and other artificial or natural barriers) mainly rely on manual surveys, interpretation of single remote sensing images, analysis of hydrological stations in local river sections, or static comparison methods based on existing engineering databases. These methods typically focus on discrete river sections or local areas, lacking the ability to model global river networks in a unified manner. Furthermore, they often rely on water level, flow, or image information under single-temporal or limited observation conditions, making it difficult to characterize the true impact of blockages on the longitudinal water level structure of the river channel from the perspective of continuous hydrodynamic changes.

[0003] However, existing technologies are mainly limited in achieving automatic identification and hydrodynamic impact assessment of river blockages on a global scale due to the lack of a unified longitudinal stitching mechanism for multi-temporal water level observation data, as well as the core technical bottleneck of difficulty in distinguishing the differences between water level discontinuities caused by actual blockages and natural hydrological fluctuations. This makes it difficult to balance the spatial continuity and global applicability of blockage identification results with the stability and hydrological scenario repeatability of blockage impact assessment in practical applications, thus affecting the overall identification accuracy and assessment reliability. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a global river blockage identification and assessment method based on data features. This method solves the core technical bottlenecks in existing technologies, such as the lack of a unified longitudinal stitching mechanism for multi-temporal water level observation data and the difficulty in distinguishing between water level discontinuities caused by actual blockages and natural hydrological fluctuations.

[0005] A method for identifying and assessing global river blockages based on data features includes the following steps: constructing multi-temporal river channel water level characteristics based on river network topological constraints, specifically: River node water level data are collected, and river network topology adjacency relationships are constructed. Based on the determined river network topology adjacency relationships, the node water level observation data that are discretely distributed in different river sections and different transit phases are automatically expanded and continuously spliced ​​according to the actual flow direction of the river. At the same time, based on the constraints of the number of effective nodes, the limit of the length of the empty segment, and the judgment of the consistency of node water level, abnormal observations of available river sections are removed, thereby determining the multi-temporal river water level characteristics. Furthermore, river blockage identification and assessment are conducted based on the constructed multi-temporal river water level characteristics, specifically as follows: Based on the determined joint longitudinal water level profile, the statistical difference in water level between adjacent spatial windows under the same transit conditions is calculated. The water level step change characteristics along the river direction are extracted from the calculated statistical difference in water level. Based on the extracted water level step change characteristics, candidate blocking points are determined. At the same time, the actual river blocking points are determined through cross-border spatial consistency aggregation analysis, and seasonal difference analysis is performed based on the actual river blocking points.

[0006] Preferably, the collection of water level data at river nodes is specifically as follows: At the scale of global river networks, using river segments as the basic spatial unit, water level data is collected from multiple observation nodes distributed along the course of each river segment. This includes river segment identifiers, node identifiers, transit identifiers, and the water surface elevation values ​​corresponding to the nodes. The distance of each node along the river direction relative to the downstream end of the river segment is as follows: ; in, Indicates the first Water level data for each node Indicates the first River segment identifiers at each node, Indicates the first The node identifier of each node. Indicates the first Transit markers for each node, Indicates the first The water surface elevation value corresponding to each node. Indicates the first The distance of each node along the river relative to the downstream end of the river section.

[0007] Preferably, the anomaly detection and removal process for available river sections is as follows: For any river segment Calculate the valid node data within the corresponding river segment; And during the downstream expansion, the corresponding cumulative length is calculated based on the set of empty segments formed by continuous river segments without effective nodes; Meanwhile, for the same node under multiple transit conditions, the cross-transit standard deviation of the node's water level is calculated, and abnormal observations are eliminated based on the calculation results.

[0008] Preferably, the calculation of the statistical difference in water level between adjacent spatial windows under the same transit conditions is as follows: A sliding space window is set along the vertical coordinate system direction for the first... The set of nodes within a window is used to calculate the median water level within the window and determine the representative water level statistics within the corresponding spatial window. And calculate the statistical difference in water level between two adjacent spatial windows along the river direction; Based on the statistical differences in water levels between adjacent spatial windows calculated by computation, the characteristics of abrupt changes in water level steps along the river direction are extracted.

[0009] Preferably, the determination of candidate blocking points is specifically as follows: Set minimum water level difference thresholds for significant water level jumps and natural fluctuations respectively. and the minimum slope threshold within a limited spatial scale. ; Candidate blocking points are determined based on the set threshold, specifically as follows: If the statistical difference in water level between adjacent spatial windows at any location satisfies the formula... This indicates that the corresponding location is in transit. The following shows the characteristics of abrupt changes in water level steps, and the corresponding longitudinal coordinate position is set as a candidate point for blocking.

[0010] Preferably, the determination of the actual river blockage point is as follows: By introducing a cross-border analysis mechanism to determine the actual river blockage point, we have: Set in different transit The determined set of blocking candidate points is identified, and the aggregated blocking cluster is determined based on the determined set of blocking candidate points; Based on the clusters of aggregation Calculate the corresponding cross-border support. The actual river blockage point is determined based on the calculated cross-border support.

[0011] Preferably, the step of determining the aggregated blocking cluster based on the determined set of blocking candidate points is as follows: Set in different transit The determined set of blocking candidate points is as follows ; Based on the determined set of candidate blocking points, if the longitudinal coordinates of two candidate points from different transit points satisfy the formula... This indicates that the two candidate points correspond to the same potential blocking location, and the two candidate points are aggregated into the same blocking cluster. .

[0012] Preferably, the calculation of the corresponding cross-border support is as follows: Blocking clusters for aggregation Calculating the corresponding cross-border support, we have: ; in, Indicates the number of transits in which blocking clusters were detected. , This represents the total number of transits included in the analysis. Indicates blocking cluster Corresponding cross-border support.

[0013] Preferably, the determination of the actual river blockage point based on the calculated cross-border support is as follows: The actual river blockage point is determined based on the calculated cross-border support, specifically: If the calculated cross-border support satisfies the formula , The threshold for determining actual river blockage points represents the blockage cluster. The corresponding location represents the actual river blockage point; conversely, the location represents a cluster of blockages. The corresponding location is not the actual point where the river is blocked.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention identifies the discontinuous characteristics of river hydrodynamics by directly using the sudden change in water level between upstream and downstream caused by the blockage as the criterion, thereby identifying the "functional impact" of river blockage rather than just its spatial morphology. This effectively avoids misjudgments such as "having a structure but no significant hydrodynamic impact" or "having no obvious structure but water level blockage".

[0015] 2. Based on the physical laws of water level change and river topological constraints, this invention makes judgments using deterministic indicators such as water level difference, slope, and spatial consistency. It does not require training samples and model transfer processes, and has stronger interpretability and regional applicability. It is particularly suitable for applications on a global scale or in regions with large differences in data conditions.

[0016] 3. This invention spatially aggregates blocking candidates across multiple transits. Only when the same location continuously exhibits discontinuous water level characteristics across multiple transits is it determined to be a stable blockage. This mechanism effectively distinguishes long-term blocking structures from occasional water level anomalies, significantly reducing the false alarm rate.

[0017] 4. While identifying the location of the blockage, this invention further outputs indicators such as the statistical characteristics of the water level difference caused by the blockage, the spatial spacing, and the slope of the water level change. It can quantitatively characterize the hydrodynamic intensity and stability of the blockage, and realize a comprehensive assessment from "whether there is a blockage" to "how strong the impact of the blockage is", providing richer information for water resource management and river connectivity evaluation.

[0018] 5. This invention can extract multi-temporal water level difference time series based on the location of the blockage and perform statistical analysis according to season or hydrological period, thereby revealing the difference in water level regulation characteristics of the blockage during the wet and dry seasons. It can reflect the storage and flood control behavior of engineering facilities such as reservoirs and dams, and provide support for operational status assessment and water resource allocation.

[0019] 6. This invention automatically constructs upstream and downstream analysis river segment chains through river topology relationships and introduces continuous data-free river segment length constraints, which can automatically determine a reasonable analysis range while ensuring hydrodynamic continuity, avoiding the instability caused by manual segment selection or fixed length truncation in existing methods. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the overall method steps of the global river blockage identification and assessment method based on data features of the present invention. Detailed Implementation

[0021] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0022] Example 1

[0023] Reference Figure 1 As one embodiment of the present invention, a method for identifying and assessing global river blockages based on data features is provided, comprising the following steps: S1: Constructing multi-temporal river water level characteristics based on river network topological constraints.

[0024] Specifically, constructing multi-temporal river level characteristics based on river network topological constraints involves collecting water level data from river nodes, including river segment identifiers, node identifiers, transit identifiers, and the water surface elevation values ​​corresponding to each node, as well as the distance of each node along the river direction relative to the downstream end of the river segment. Based on the acquired river node water level data, a river network topological adjacency relationship is constructed. According to this relationship, the node water level observation data, discretely distributed across different river segments and transit time phases, are automatically expanded and continuously stitched together according to the actual river flow direction. Simultaneously, based on constraints on the number of effective nodes, limitations on the length of empty segments, and judgments on node water level consistency, abnormal observations in usable river segments are removed, thereby determining multi-temporal river level characteristics. This results in a spatially continuous and temporally repeatable joint longitudinal water level profile. The specific implementation is as follows: Collecting water level data at river nodes involves collecting water level data from multiple observation nodes distributed along the course of each river segment at a global river network scale, using river segments as the basic spatial unit. This includes river segment identifiers, node identifiers, transit identifiers, and the water surface elevation values ​​corresponding to each node, along the river's direction relative to the downstream end of the river segment. Specifically: ; in, Indicates the first Water level data for each node Indicates the first The river segment identifier of each node is used to represent the river segment unit to which the node belongs. Indicates the first The node identifier of each node is used to distinguish observation nodes at different spatial locations within the same river segment. Indicates the first The transit markers of each node are used to distinguish the observation phases under different time or orbital conditions. Indicates the first The water surface elevation value corresponding to each node is used to characterize the water level status of that node under the current passage. Indicates the first The distance of each node along the river relative to the downstream end of the river segment is used to describe the longitudinal positional relationship of the node within the river segment.

[0025] It should be noted that by uniformly collecting node-level water level data, the water level information not only has distinguishability in the time dimension, but also has strict spatial positioning attributes, providing basic data support for subsequent hydrodynamic analysis along the river channel.

[0026] Based on the acquired river node water level data, a river network topological adjacency relationship is constructed. This relationship is established after the node water level data collection, by introducing river network topological structure information to describe the upstream and downstream connectivity between river segments. ; in, Indicates the upstream section of the river. Indicates a downstream section marker that is directly connected to the upstream section marker; Based on the introduced river network topology information, for any river segment to be analyzed... Starting with the initial river segment, the sequence of downstream river segments is recursively determined along the direction of water flow, resulting in: ; in, Indicates the starting section of the river. This represents a defined sequence of downstream river segments, where represents the th segment in the sequence. Each section of the river, This indicates the total number of river segments in the downstream river segment sequence, which is set by the implementers based on the actual application scenario. Meanwhile, based on the determined downstream river segment sequence, the node water level data within each river segment are sorted and spliced ​​according to the dual constraint rule of "river segment sequence + node position along the river segment".

[0027] To achieve a unified description across river segments, a joint longitudinal coordinate system is introduced. Then we have: ; in, Indicates the first The global coordinates of each node along the joint longitudinal profile. Indicates the first The length of each upstream section Indicates the first The distance of each node along the river relative to the downstream end of the river section.

[0028] It should be noted that by mapping the nodal water level observations of different river sections and spatial locations to the same longitudinal coordinate system, a water level observation sequence that unfolds continuously along the actual flow direction of the river is formed, providing a spatially consistent analytical basis for the identification of hydrodynamic discontinuities.

[0029] Based on constraints on the number of effective nodes, limitations on the length of empty segments, and judgment of node water level consistency, anomaly observations are removed from available river segments to determine multi-temporal river water level characteristics. The specific implementation is as follows: First, for any river segment The valid node data within the corresponding river segment is set as follows: ; in, Indicates river section The effective node data volume, This indicates an indicator function, which takes a value of 1 when the node water level meets the observation conditions. Secondly, during the downstream expansion, the set of empty segments formed by continuous river segments without effective nodes is considered. Set the corresponding cumulative length as: ; in, This represents the cumulative length of consecutive empty segments. Indicates the length of the empty river segment. This represents the set of empty segments consisting of continuous river segments without valid nodes. It should be noted that when the cumulative length of consecutive empty segments... Exceeding the set maximum empty segment length threshold At that time, longitudinal expansion is terminated to prevent excessive crossing of river sections without observational support.

[0030] Furthermore, for water level observations of the same node under multiple transit conditions, a water level consistency judgment index is introduced, such as the cross-transit standard deviation of the node's water level, then: ; in, This represents the water level observation value of node n during the passage of p. This represents the average water level at node n. This indicates the number of transit points that the node is involved in the statistics for; It should be noted that when the calculated cross-border standard deviation of the node water level exceeds the set threshold, the corresponding observation is considered abnormal and is removed.

[0031] It should be noted that after anomaly removal, the remaining node water level data form a continuous spatial structure and have a basis for repeated observation in time, thereby constructing a multi-temporal river water level feature set and forming a joint longitudinal water level profile that can be used for subsequent blockage identification.

[0032] S2: River blockage identification and assessment are conducted based on the constructed multi-temporal river water level characteristics.

[0033] Specifically, river blockage identification is performed based on the constructed multi-temporal river water level characteristics. This involves calculating the statistical differences in water levels between adjacent spatial windows under the same transit conditions using a determined joint longitudinal water level profile. Based on the calculation results, the water level step abrupt change characteristics along the river direction are extracted, and candidate blockage points are determined. Simultaneously, cross-transit spatial consistency aggregation analysis is performed to identify actual river blockage points, and seasonal difference analysis is conducted based on these actual river blockage points. The specific implementation is as follows: Calculating the statistical difference in water level between adjacent spatial windows under the same transit conditions involves segmenting the node water levels along the longitudinal direction of the river channel based on a determined joint longitudinal water level profile under the same transit conditions, and then calculating the statistical difference in water level between adjacent spatial windows. Specifically: According to the joint vertical coordinate system The following multi-node water level sequence, setting any transit In the joint vertical coordinate system The following multi-node water level sequence is: ; in, Indicates the first The global coordinates of each node along the joint longitudinal profile. Indicates the first Each node is in transit. Water level observations below; Along the longitudinal coordinate system Direction setting length is The sliding space window, for the first A collection of nodes within a window By calculating the median water level within the window to determine the representative water level statistics within the corresponding spatial window, we have: ; in, Indicates the first Representative water level statistics within each window; Subsequently, two adjacent spatial windows along the river were examined. and Calculating the statistical differences in water levels, we have: ; in, Indicates the first Representative water level statistics within each window Indicates the first Representative water level statistics within each window This indicates the statistical difference in water level between adjacent spatial windows under the same transit conditions.

[0034] Based on the calculated statistical differences in water levels between adjacent spatial windows, the abrupt changes in water level steps along the river direction are extracted, as follows: Based on the calculated statistical differences in water levels between adjacent spatial windows Introducing the vertical distance between corresponding windows, we have: ; The rate of change of water level per unit longitudinal distance (slope index) is set as follows: ; in, Indicates the vertical distance between adjacent windows. This indicates the slope index per unit longitudinal distance; Set minimum water level difference thresholds for significant water level jumps and natural fluctuations respectively. and the minimum slope threshold within a limited spatial scale. ; Candidate blocking points are determined based on the set threshold, specifically as follows: If the statistical difference in water level between adjacent spatial windows at any location satisfies the formula... This indicates that the corresponding location is in transit. The following shows the characteristics of abrupt changes in water level steps, and the corresponding longitudinal coordinate position is set as a candidate point for blocking.

[0035] Based on cross-border spatial consistency aggregation analysis, the actual river blockage point is determined. This is done by introducing a cross-border analysis mechanism based on the identified candidate blockage points, as detailed below: Set in different transit The determined set of blocking candidate points is as follows ; Based on the determined set of candidate blocking points, if the longitudinal coordinates of two candidate points from different transit points satisfy the formula... This indicates that the two candidate points correspond to the same potential blocking location, and the two candidate points are aggregated into the same blocking cluster. ; Blocking clusters for aggregation Calculating the corresponding cross-border support, we have: ; in, Indicates the number of transits in which blocking clusters were detected. , This represents the total number of transits included in the analysis. Indicates blocking cluster Corresponding cross-border support; The actual river blockage point is determined based on the calculated cross-border support, specifically: If the calculated cross-border support satisfies the formula , The threshold for determining actual river blockage points is set by the implementers based on the actual application scenario, thus representing a blockage cluster. The corresponding location represents the actual river blockage point; conversely, the location represents a cluster of blockages. The corresponding location is not the actual point where the river is blocked.

[0036] Seasonal variation analysis based on actual river blockage points involves assessing the blockage water level characteristics at the identified actual river blockage points, and then performing seasonal variation analysis based on the assessment results. The details are as follows: An assessment of the water level characteristics of the blocked river is conducted based on the identified actual river blockage points, specifically: For any identified actual river blockage point, its position in the joint longitudinal coordinate system can be denoted as: During a certain transit Below, using the location of the actual river blockage point as the boundary, spatial windows are set upstream and downstream of the blockage point to extract representative water levels upstream and downstream of the blockage. Then: Set the set of node water levels within the upstream window as follows The set of water levels at nodes within the downstream window is ; And by calculating the corresponding representative water level statistics, we have: ; ; And calculate the blocking point at the border crossing. The strength of the water level step below is then: ; Wherein, the calculated blocking point is located at the border crossing. The strength of the water level step below is used to characterize the intensity of the instantaneous hydrodynamic impact of the blockage.

[0037] Calculation-based blocking points at border crossings The strength of the water level step below is used to assess the characteristics of the blocking water level, specifically: Water level difference sequence obtained at the same blocking point under multiple transit conditions Its statistical characteristics, such as minimum, median, quartiles and maximum, can be calculated to characterize the hydrodynamic control capability and stability of the blockage on a long-term scale. When the blockade exhibits significant characteristics in most transit scenarios Furthermore, if the statistical dispersion is small, it can be determined that the blockage has a significant and stable hydrodynamic blocking effect; otherwise, it may correspond to a structure with weak blocking or limited control capabilities.

[0038] Seasonal difference analysis based on the assessment results of blocking water level characteristics involves incorporating transit time information into the assessment results, and grouping and statistically analyzing the differences in blocking water level under different seasons or hydrological periods to analyze the seasonal differences in the impact of blocking hydrodynamics. The specific implementation is as follows: In obtaining the water level step strength at the blockage point under multiple crossing conditions Subsequently, further action was taken based on the transit... The corresponding time information, when divided into different seasonal or hydrological period sets, yields: ; in, Indicates seasonal or hydrological grouping (such as high water season, low water season, or meteorological season). This indicates a transit group belonging to the same season; For any season The representative water level difference characteristics of the blocking point under different seasons are calculated as follows: ; in, This represents the representative water level difference characteristics of the calculated blocking point under different seasons, used to analyze the differences in water level regulation during wet and dry seasons, or under different hydrological scenarios. Specifically: If the blockage exhibits a significantly increased water level difference during the wet season but remains relatively stable during the dry season, it indicates that it has significant water storage or flood regulation characteristics. If the water level difference between seasons is not significant, it indicates that the blockage has a weak regulatory effect on seasonal water level changes.

[0039] It should be noted that this embodiment also uses a single upstream segment in a global river network as the starting point for analysis. Based on multi-temporal channel node water level observation data, it automatically constructs a continuous longitudinal water level profile along the river topology, and identifies stable river blockages through multi-transit consistency analysis. The specific steps are as follows: First, the river network topology and river length information of the target river segment and several downstream river segments are obtained; then, the number of nodes with effective water level observations in each river segment is counted, and the downstream river segment chain for analysis is automatically selected under the condition of satisfying the minimum number of effective nodes constraint.

[0040] Within each river segment, distance coordinates along the river are constructed based on the relative position of the nodes within the segment. Statistical screening of the water levels of the nodes across multiple time phases is performed to remove nodes with insufficient observations or excessively large abnormal fluctuations in water level.

[0041] Subsequently, the node data of the upstream and downstream river sections are spliced ​​into a unified vertical distance coordinate system. Within the same river section, the median difference of water level between adjacent windows is calculated using a sliding window method. When the water level difference exceeds a preset threshold and the corresponding slope meets the conditions, it is determined as a candidate event for blocking.

[0042] Candidate blocking events identified in different transits are aggregated according to their spatial location. Only when a certain location appears repeatedly in multiple transits and the number of transits exceeds a threshold is it finally identified as a stable river blocking location.

[0043] Furthermore, based on the identified stable river blockages, the hydrodynamic characteristics of the blockages are further quantitatively assessed, specifically as follows: For each stable blocking location, representative water level values ​​upstream and downstream under different transit conditions are extracted, and the corresponding water level difference and water level change rate per unit distance are calculated as indicators of blocking strength.

[0044] By statistically analyzing the minimum, quartile, median, and maximum values ​​of water level differences under multiple transit conditions, the overall impact of blockage on the longitudinal water level distribution of the river channel can be quantified.

[0045] When the water level difference corresponding to the blockage remains large and stable throughout different passages, the blockage can be determined to have a significant hydrodynamic control effect; conversely, if the water level difference fluctuates greatly, it may correspond to a temporary or weak blockage structure.

[0046] Furthermore, time information was incorporated to conduct a seasonal-scale analysis of the characteristics of the blocked water level, specifically: By analyzing the transit timestamps, the water level difference data corresponding to the blockage is divided into different seasons or hydrological periods, and the median and dispersion of the water level difference between the upstream and downstream of the blockage are statistically analyzed in each season.

[0047] Comparing water level differences between different seasons can be used to analyze the differences in water storage characteristics between the wet and dry seasons.

[0048] When a blockage exhibits a significantly increased water level difference during the wet season but remains relatively stable during the dry season, it indicates that the blockage may have a significant flood control or water storage function; if the water level difference does not change significantly in each season, it indicates that the blockage has a weak impact on seasonal water level changes.

[0049] Furthermore, if the aforementioned function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0050] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0051] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0052] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for global river blockage identification and assessment based on data characteristics, characterized in that: Includes the following steps, Multi-temporal river level characteristics are constructed based on river network topological constraints, specifically as follows: River node water level data are collected, and river network topology adjacency relationships are constructed. Based on the determined river network topology adjacency relationships, the node water level observation data that are discretely distributed in different river sections and different transit phases are automatically expanded and continuously spliced ​​according to the actual flow direction of the river. At the same time, based on the constraints of the number of effective nodes, the limit of the length of the empty segment, and the judgment of the consistency of node water level, abnormal observations of available river sections are removed, thereby determining the multi-temporal river water level characteristics. Furthermore, river blockage identification and assessment are conducted based on the constructed multi-temporal river water level characteristics, specifically as follows: Based on the determined joint longitudinal water level profile, the statistical difference in water level between adjacent spatial windows under the same transit conditions is calculated. The water level step change characteristics along the river direction are extracted from the calculated statistical difference in water level. Based on the extracted water level step change characteristics, candidate blocking points are determined. At the same time, the actual river blocking points are determined through cross-border spatial consistency aggregation analysis, and seasonal difference analysis is performed based on the actual river blocking points.

2. The method of identifying and assessing global river blockages based on data characteristics of claim 1, wherein: The water level data collected at river nodes are as follows: At the scale of global river networks, using river segments as the basic spatial unit, water level data is collected from multiple observation nodes distributed along the course of each river segment. This includes river segment identifiers, node identifiers, transit identifiers, and the water surface elevation values ​​corresponding to the nodes. The distance of each node along the river direction relative to the downstream end of the river segment is as follows: ； wherein, represents water level data of the th node, represents a river segment identifier of the th node, represents a node identifier of the th node, represents a transit identifier of the th node, represents a water surface elevation value corresponding to the th node, represents a distance along the river direction relative to the downstream end of the river segment of the th node.

3. The method for identifying and assessing global river blockages based on data features as described in claim 2, characterized in that: The specific steps for anomaly detection and removal in available river sections are as follows: For any river segment Calculate the valid node data within the corresponding river segment; And during the downstream expansion, the corresponding cumulative length is calculated based on the set of empty segments formed by continuous river segments without effective nodes; Meanwhile, for the same node under multiple transit conditions, the cross-transit standard deviation of the node's water level is calculated, and abnormal observations are eliminated based on the calculation results.

4. The method for identifying and assessing global river blockages based on data features as described in claim 3, characterized in that: The calculation of statistical differences in water levels between adjacent spatial windows under the same transit conditions is as follows: A sliding space window is set along the vertical coordinate system direction for the first... The set of nodes within a window is used to calculate the median water level within the window and determine the representative water level statistics within the corresponding spatial window. And calculate the statistical difference in water level between two adjacent spatial windows along the river direction; Based on the statistical differences in water levels between adjacent spatial windows calculated by computation, the characteristics of abrupt changes in water level steps along the river direction are extracted.

5. The global river blockage identification and assessment method based on data features as described in claim 4, characterized in that: The determination of candidate blocking points is specifically as follows: Set minimum water level difference thresholds for significant water level jumps and natural fluctuations respectively. and the minimum slope threshold within a limited spatial scale. ; Candidate blocking points are determined based on the set threshold, specifically as follows: If the statistical difference in water level between adjacent spatial windows at any location satisfies the formula... This indicates that the corresponding location is in transit. The following shows the characteristics of abrupt changes in water level steps, and the corresponding longitudinal coordinate position is set as a candidate point for blocking.

6. The method for identifying and assessing global river blockages based on data features as described in claim 5, characterized in that: The determination of the actual river blockage point is as follows: By introducing a cross-border analysis mechanism to determine the actual river blockage point, we have: Set in different transit The determined set of blocking candidate points is identified, and the aggregated blocking cluster is determined based on the determined set of blocking candidate points; Based on the clusters of aggregation Calculate the corresponding cross-border support. The actual river blockage point is determined based on the calculated cross-border support.

7. The method for identifying and assessing global river blockages based on data features as described in claim 6, characterized in that: The process of determining the aggregated blocking cluster based on the determined set of blocking candidate points is as follows: Set in different transit The determined set of blocking candidate points is as follows ; Based on the determined set of candidate blocking points, if the longitudinal coordinates of two candidate points from different transit points satisfy the formula... This indicates that the two candidate points correspond to the same potential blocking location, and the two candidate points are aggregated into the same blocking cluster. .

8. The method for identifying and assessing global river blockages based on data features as described in claim 7, characterized in that: The calculation of the corresponding cross-border support is as follows: Blocking clusters for aggregation Calculating the corresponding cross-border support, we have: ； in, Indicates the number of transits in which blocking clusters were detected. , This represents the total number of transits included in the analysis. Indicates blocking cluster Corresponding cross-border support.

9. The method for identifying and assessing global river blockages based on data features as described in claim 8, characterized in that: The determination of the actual river blockage point based on the calculated cross-border support is as follows: The actual river blockage point is determined based on the calculated cross-border support, specifically: If the calculated cross-border support satisfies the formula , The threshold for determining actual river blockage points represents the blockage cluster. The corresponding location represents the actual river blockage point; conversely, the location represents a cluster of blockages. The corresponding location is not the actual point where the river is blocked.

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