A river channel evolution analysis method and system

Abstract

The present invention relates to the field of river channel evolution prediction, and in particular to a river channel evolution analysis method and system. The method comprises the following steps: constructing a three-dimensional model of a river channel area to obtain a three-dimensional model of the river channel; analyzing the river channel morphology evolution curve of the three-dimensional model of the river channel to obtain the river channel morphology evolution curve; dividing the surrounding area of ​​the river channel area into classified grids to obtain natural land grid data and human activity grid data; analyzing the river channel evolution influencing parameters of the river channel area based on the natural land grid data and the human activity grid data to obtain the river channel evolution influencing parameters; correcting the river channel morphology evolution curve based on the river channel evolution influencing parameters, and performing a correction simulation evolution prediction on the three-dimensional model of the river channel to obtain the river channel evolution prediction result. The present invention obtains the hydrological data of the river channel affected by the influencing parameters by analyzing the natural influencing parameters and the influencing parameters of human activities around the river channel, which is conducive to accurately predicting the river channel evolution.

Description

Technical Field

[0001] The present invention relates to the field of river channel evolution prediction, and in particular to a river channel evolution analysis method and system. Background Art

[0002] River channel evolution analysis and prediction are important components of river management and flood prevention and disaster reduction. By simulating river channel evolution under different flow scenarios and analyzing the impact of river channel evolution on water resources, we can predict the future trend of water resource changes near the river. Not only that, river channel evolution also threatens the safety of bridges and road infrastructure. Therefore, predicting and analyzing river channel evolution trends is crucial for infrastructure safety. At present, a river channel evolution analysis and prediction method that comprehensively analyzes historical river channel data, topographic information, and multi-faceted river channel hydrological data has realized the prediction of river channel evolution. This method can effectively assess the risk of river channel evolution by identifying and quantifying the characteristics of river channel erosion, siltation, river channel width and depth changes in river channel evolution. However, river channel hydrological data are affected by many complex factors, such as human activities, which makes it difficult to accurately simulate river channel evolution. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a river channel evolution analysis method and system to solve at least one of the above technical problems.

[0004] To achieve the above purpose, the river channel evolution analysis method includes the following steps:

[0005] Step S1: constructing a three-dimensional model of a river channel area to obtain a three-dimensional model of the river channel; analyzing hydrological data of the river channel area based on the three-dimensional model of the river channel to obtain river hydrological data; analyzing a river morphology evolution curve of the three-dimensional model of the river channel based on the river hydrological data to obtain a river morphology evolution curve;

[0006] Step S2: gridding the surrounding remote sensing data of the river area to obtain grid remote sensing data of the river area; acquiring natural land grids from the grid remote sensing data of the river area to obtain natural land grid data; acquiring human activity grids from the grid remote sensing data of the river area to obtain human activity grid data;

[0007] Step S3: Analyze the influencing parameters of river channel evolution in the river channel area based on the natural land grid data and the human activity grid data to obtain the influencing parameters of river channel evolution;

[0008] Step S4: Based on the grid remote sensing data around the river channel and the influencing parameters of the river channel evolution, the river channel morphology evolution curve is corrected to obtain a corrected river channel evolution curve; based on the corrected river channel evolution curve and the river channel three-dimensional model, the river channel area is predicted to evolve to obtain a river channel evolution prediction result.

[0009] By constructing a three-dimensional model of the river channel, the present invention can intuitively and accurately express the geometric morphology, spatial structure and terrain characteristics of the river channel, provide basic data for subsequent analysis, and combine with hydrological data analysis to deeply understand the flow, velocity, water level and hydrological characteristics of the river channel, reveal the relationship between the hydrological process and the river channel morphology, and the drawing of the river channel morphology evolution curve can clearly show the historical evolution process of the river channel, and provide a reference basis for predicting future evolution trends; dividing the area around the river channel into remote sensing data grids can realize the refined management and analysis of the study area, and through the classification and processing of grid remote sensing data, natural land and human activity information can be extracted respectively to form a grid data set with multi-source data fusion, which provides a data basis for analyzing the impact of natural environment and human activities on river channel evolution, and also provides data support for the subsequent establishment of a more refined river channel evolution model; based on the natural land and human activity grid Grid data can be used to analyze the degree of influence and mechanism of different factors on river evolution, the relationship between vegetation coverage and riverbank erosion, the impact of land use changes on river runoff, and changes in river morphology. By quantifying each influencing factor, its contribution to river evolution can be more accurately evaluated, providing a scientific basis for formulating more targeted river management measures. Using grid remote sensing data around the river and river evolution influencing parameters, the river morphology evolution curve based on historical data can be corrected to reduce the influence of various complex factors on river hydrological data, such as human activities, so as to finely simulate the river evolution and improve the accuracy of the prediction results. Combining the corrected evolution curve with the three-dimensional river model for evolution prediction can simulate the evolution trend of the river under different scenarios in the future, provide decision support for river management, flood prevention and disaster reduction, and water resources protection, and minimize the negative impact of river evolution.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: performing segmented sonar processing on the river area to obtain segmented sonar data of the area;

[0012] Step S12: merging the regional segmented sonar data to obtain river channel sonar data;

[0013] Step S13: constructing a three-dimensional model of the river channel area based on the river channel sonar data to obtain a three-dimensional model of the river channel;

[0014] Step S14: analyzing the hydrological data of the river area based on the three-dimensional model of the river to obtain the river hydrological data;

[0015] Step S15: analyzing the river morphology evolution curve of the three-dimensional river model based on the river hydrological data to obtain the river morphology evolution curve.

[0016] The present invention can divide a large river channel area into several small areas by performing segmented sonar processing on the river channel area, and collect and process sonar data separately to effectively improve the efficiency and accuracy of data collection. It is especially suitable for rivers with large water depth changes and complex underwater terrain. Segmented processing also facilitates the use of different sonar parameter settings according to the characteristics of different river sections, thereby further improving the quality of data. By merging the regional segmented sonar data, the sonar data of each river section can be integrated into a unified coordinate system and reference plane to form a complete river channel sonar data set. Data merging requires splicing, correction and fusion processing to eliminate data redundancy and errors and ensure data continuity and consistency, which lays a data foundation for the subsequent construction of a high-precision three-dimensional model of the river channel. The three-dimensional model of the river channel constructed based on the river channel sonar data can accurately express the riverbed topography, water Sonar technology can penetrate water bodies and obtain underwater topographic data, which is especially suitable for turbid rivers with lush aquatic plants. The three-dimensional model of the river constructed based on sonar data can provide important basic data for the prediction of river evolution. Hydrological data analysis based on the three-dimensional model of the river can more accurately calculate the hydraulic parameters of the river cross-sectional area, providing a reliable basis for hydrological simulation and hydrodynamic calculations. Combined with hydrological observation data, it can analyze the characteristics of river runoff and water environment changes, and provide data support for the river evolution curve. Analysis of the river morphology evolution curve based on river hydrological data and the three-dimensional model of the river can more intuitively show the changes in river scouring and siltation, the evolution trend of river flow, and reveal the interaction between hydrological processes and river morphology, which is of great significance for predicting future river evolution trends and formulating scientific and reasonable river management strategies.

[0017] Preferably, step S14 comprises the following steps:

[0018] Step S141: setting nodes for the river area to obtain river node data;

[0019] Step S142: performing a river channel transverse profile analysis on the river channel three-dimensional model based on the river channel node data to obtain river channel node profile data;

[0020] Step S143: acquiring the node flow velocity of the river area based on the river node data to obtain the river node flow velocity data;

[0021] Step S144: calculating the river runoff of the river node data based on the river node profile data and the river node flow velocity data to obtain the river node runoff data;

[0022] Step S145: performing node data integration processing on the river area based on the river node velocity data and the river node runoff data to obtain river hydrological data.

[0023] The present invention can divide a continuous river into several representative sections by reasonably setting nodes in the river area, so as to facilitate refined hydrological data analysis. The setting of nodes needs to take into account factors such as river morphology and water flow characteristics. For example, nodes need to be encrypted in areas where the river bends, confluences, and water flow velocity changes dramatically, so as to improve the accuracy and representativeness of data collection; based on the river node data, a transverse profile analysis of the river three-dimensional model can be performed to obtain the cross-sectional morphological information of the river at each node, such as river width, water depth, and cross-sectional area. This information is the basic data for calculating hydraulic parameters and analyzing water flow structure, and is crucial for a deep understanding of the river hydrological process; obtaining flow velocity data at the river node can reflect the flow at that location. The movement state of the section and accurate flow velocity data are important bases for calculating flow, analyzing hydropower resources and evaluating water environment capacity. The runoff at the river node is calculated by combining the river node profile data and node velocity data. By calculating the flow at the river node, the water exchange and changes between different sections can be analyzed, and the hydraulic characteristics and transportation capacity of the river can be evaluated. The runoff calculation helps to understand the water distribution and movement laws of the river. The velocity data and runoff data of the river node are integrated and processed to obtain a complete river hydrological data set, which can be used to analyze the spatiotemporal changes of the river hydrological situation, evaluate the impact of human activities on the river hydrological process, and provide a scientific basis for water resources management and river governance.

[0024] Preferably, step S15 comprises the following steps:

[0025] Step S151: performing runoff impact intensity analysis on river hydrological data to obtain runoff impact intensity data;

[0026] Step S152: performing runoff impact deformation simulation on the three-dimensional model of the river channel based on the runoff impact force data to obtain runoff impact deformation data;

[0027] Step S153: plotting the runoff impact force data and the runoff impact deformation data to obtain a runoff impact deformation curve;

[0028] Step S154: performing river water level analysis on the river hydrological data to obtain river water level data;

[0029] Step S155: performing water level erosion simulation on the three-dimensional model of the river channel based on the river channel water level data to obtain water level erosion data;

[0030] Step S156: plotting the river water level data and the water level erosion data to obtain a water level erosion curve;

[0031] Step S157: fitting the runoff impact deformation curve and the water level erosion curve to a river channel morphology evolution curve to obtain a river channel morphology evolution curve.

[0032] The present invention can quantitatively evaluate the degree of change of the riverbed morphology caused by runoff by performing runoff impact force analysis on the river hydrological data, and based on this, simulate the runoff impact deformation of the river three-dimensional model, which can help understand how the river responds to the scouring and transportation of water under different flow conditions, and provide a reference for the design of river flood control projects and river flow adjustment; the runoff impact force data and deformation data are plotted to form a runoff impact deformation curve, which can intuitively show the relationship between runoff impact force and riverbed deformation. This curve can serve as an important basis for analyzing the evolution trend of the river and predicting the degree of deformation in the future, and provide a scientific reference for formulating river management strategies; the water level analysis is performed on the river hydrological data, and the water level of the three-dimensional model of the river is simulated based on the river water level data. Erosion simulation can assess the risk of riverbank erosion under different water level conditions, which is of great significance for predicting riverbank collapse, formulating bank protection engineering plans, and ensuring the safety of river flood discharge; the river water level data and water level erosion data are plotted to obtain the water level erosion curve, which can clearly show the relationship between water level changes and the degree of riverbank erosion; this curve can be used as an important reference for the design of river evolution prediction system; the runoff impact deformation curve and the water level erosion curve are fitted to obtain the river morphology evolution curve that comprehensively considers the effects of runoff impact and water level erosion. This curve can more comprehensively and accurately reflect the river evolution process, and provide a more reliable basis for predicting future trends in river morphology changes and formulating scientific and reasonable river management plans.

[0033] Preferably, step S2 comprises the following steps:

[0034] Step S21: acquiring remote sensing image data of the river area to obtain remote sensing image data of the river area;

[0035] Step S22: dividing the river area remote sensing image data into grids around the river to obtain grid remote sensing data around the river;

[0036] Step S23: extracting human activity texture from the grid remote sensing data around the river channel to obtain human activity texture data;

[0037] Step S24: acquiring natural land grids from the grid remote sensing data around the river based on the human activity texture data to obtain natural land grid data;

[0038] Step S25: acquiring human activity grids from the grid remote sensing data around the river based on the human activity feature texture data to obtain human activity grid data.

[0039] The present invention can convert a large-scale, continuous river area into regular grid units by acquiring remote sensing image data of the river area and performing grid division, which is convenient for spatial analysis and data management. High-resolution remote sensing images can provide rich spatial information, providing a data basis for extracting environmental elements around the river and analyzing their spatial distribution characteristics. Human activity texture extraction is performed on the grid remote sensing data around the river, which can capture the texture characteristics of human activities on the remote sensing images. Human activities often leave unique texture marks in land use patterns, building forms, and vegetation coverage. By extracting these texture features, we can provide a basis for distinguishing between natural environments and human activity areas. By acquiring natural land grids based on human activity texture data, we can effectively distinguish between natural environments and human activity areas, and extract natural land information, such as forests and grasslands. This provides data support for analyzing the impact of the natural environment on rivers, such as the impact of vegetation cover on soil erosion and the impact of land use changes on river hydrological processes. By acquiring human activity grids based on human activity feature texture data, we can identify and extract different types of human activity areas, such as towns, roads, and farmlands. This provides data support for analyzing the impact of human activities on rivers, such as the impact of urban expansion on river water quality, the impact of agricultural activities on non-point source pollution, and the impact of water conservancy projects on river morphology.

[0040] Preferably, step S23 includes the following steps:

[0041] Step S231: acquiring grid color features of grid remote sensing data around the river channel to obtain grid remote sensing color feature data;

[0042] Step S232: performing urban area color texture analysis on the grid remote sensing data around the river based on the grid remote sensing color feature data to obtain the urban area color texture;

[0043] Step S233: acquiring texture data of non-urban areas from the grid remote sensing data around the river based on the color texture of the urban area, thereby obtaining texture data of non-urban areas;

[0044] Step S234: extracting regularly distributed agricultural texture features from the texture data of non-urban areas to obtain regularly distributed agricultural texture feature data;

[0045] Step S235: performing agricultural area regular texture analysis on the grid remote sensing data around the river based on the regularly distributed agricultural texture feature data to obtain the agricultural area regular texture;

[0046] Step S236: fitting human activity texture grid data to the color texture of the urban area and the regular texture of the agricultural area to obtain human activity texture data.

[0047] The present invention obtains the color features of the grid remote sensing data around the river channel, and performs color and texture analysis of the urban area based on the data, thereby making full use of the significant features of the urban area in color and texture. Urban areas usually present color combinations and texture patterns that are different from the surrounding natural environment, for example, dense buildings, crisscrossing roads, and sparse vegetation. These features can be used to distinguish urban areas from other land cover types. On the basis of identifying urban areas, the texture features of non-urban areas can be further analyzed to extract regularly distributed agricultural texture features. Agricultural areas, especially large-scale farmland, usually present regular geometric shapes and arrangements, such as rectangular or strip-shaped farmland, which is in sharp contrast to the random texture features of the natural environment and can serve as an important basis for identifying agricultural areas. Regular texture analysis of agricultural areas based on regularly distributed agricultural texture feature data can more accurately identify the boundaries and scope of agricultural areas and avoid misjudging other areas with similar color or texture features as agricultural areas. Fitting the color texture of urban areas and the regular texture of agricultural areas can obtain more complete and accurate human activity texture data. This data not only contains information on urban and agricultural areas, but can also distinguish other types of human activity areas, providing a more refined data basis for subsequent analysis of the impact of human activities on rivers.

[0048] Preferably, step S3 comprises the following steps:

[0049] Step S31: Analyze the vegetation coverage of natural land grid data to obtain the vegetation coverage of natural grids;

[0050] Step S32: analyzing the influencing parameters of the river flow velocity in the river area based on the vegetation coverage rate of the natural grid, and obtaining the influencing parameters of the flow velocity by the natural land;

[0051] Step S33: Analyze the urban cement coverage rate on the human activity grid data to obtain the urban cement coverage rate;

[0052] Step S34: analyzing the parameters affecting river runoff in the river area based on the urban cement coverage rate to obtain parameters affecting runoff in the urban area;

[0053] Step S35: analyzing the parameters of agricultural river flow velocity influencing the river area based on the human activity land grid to obtain agricultural flow velocity parameters;

[0054] Step S36: fitting the human activity impact weights on the runoff parameters affected by the town and the velocity parameters affected by agriculture to obtain the river channel parameters affected by human activities;

[0055] Step S37: Integrate the velocity parameters affected by natural land and the river channel parameters affected by human activities into river channel evolution parameters to obtain river channel evolution parameters.

[0056] The present invention analyzes the vegetation coverage rate of natural land grid data to understand the distribution and density of vegetation in natural land, which helps to evaluate the protective and regulatory effects of natural land on rivers and provides important basic data for subsequent analysis of river flow velocity and evolution; based on the vegetation coverage rate analysis of natural grids, the flow velocity law of river areas can be studied. Vegetation can affect the water flow velocity and direction of rivers and protect riverbeds from erosion, so as to quantify the degree of influence of natural land on river flow velocity and provide important parameters for predicting river evolution; the urban cement coverage rate analysis of human activity grid data helps to understand the impact of urbanization on rivers. The cement coverage rate can reflect the degree of change of human activities on natural land, which is of great significance for the subsequent analysis of the impact of urbanization on river runoff; the focus is on analyzing the impact of urban cement coverage rate on river area runoff The impact of flow dynamics, cement ground will affect the absorption and infiltration of rainwater, increase surface runoff, and thus affect the flow and water level of the river; agricultural activities will also have an impact on the flow velocity of the river. By analyzing the land grid of human activities, the impact of agriculture on the flow velocity and water flow pattern of the river can be evaluated, which helps to fully understand the comprehensive impact of human activities on the river; in order to more comprehensively understand the impact of human activities on the river, the impact of urban cement coverage on runoff and the impact of agricultural activities on flow velocity are comprehensively considered, and the comprehensive impact parameters of human activities on the river are obtained through weight fitting; the velocity parameters of natural land and the river parameters of human activities are integrated to construct the river evolution impact parameters, which can comprehensively reflect the comprehensive impact of natural factors and human activities on river evolution, and provide a scientific basis for river management, ecological restoration and sustainable development.

[0057] Preferably, step S35 includes the following steps:

[0058] Step S351: Identify the regular vegetation coverage rate of the human activity land grid to obtain the regular vegetation coverage rate;

[0059] Step S352: Calculate the amount of land fertilizer used for the regular vegetation coverage rate to obtain the amount of land fertilizer used;

[0060] Step S353: performing nutrient growth analysis on the river area based on the amount of land fertilizer used to obtain river nutrient growth data;

[0061] Step S354: performing river algae growth analysis on the river area based on the river nutrient growth data to obtain river algae growth data;

[0062] Step S355: calculating the amount of aquatic plant reduction in the river area based on the river algae growth data to obtain aquatic plant reduction data;

[0063] Step S356: Analyze the influencing parameters of the river flow velocity in the river area based on the aquatic plant reduction data to obtain the agricultural influencing flow velocity parameters.

[0064] The present invention can distinguish between natural vegetation and artificial vegetation by identifying regular vegetation coverage in human activity land grids, such as crop planting in farmland, and provide a more accurate data basis for subsequent analysis of the impact of agricultural activities on the river environment; based on regular vegetation coverage, the land fertilizer usage in different areas can be estimated, which provides a quantitative indicator for evaluating the input of nutrients to the river from agricultural activities, and is helpful for analyzing the trend of river water quality changes; by using land fertilizer usage data, the impact on the growth of nutrients in the river area can be analyzed, and the risk of eutrophication of river water bodies caused by nitrogen and phosphorus nutrients input caused by agricultural activities can be evaluated, providing a scientific basis for analyzing river evolution; the increase of nutrients in the river leads to Excessive growth of algae can be predicted by analyzing the impact of river nutrients on algae growth, which can help predict potential risks to river ecosystems. Excessive growth of algae can lead to a decrease in water oxygen content, affecting the survival of aquatic organisms. By analyzing the growth data of river algae, the changing trend of dissolved oxygen in the water body can be predicted, and then the reduction in aquatic plants can be calculated, which provides a quantitative indicator for evaluating the impact of agricultural activities on the structure and function of river ecosystems. Based on the data on the reduction of aquatic plants, its impact on river flow velocity is analyzed, and the parameters of agricultural impact on flow velocity are constructed, which can help to deeply understand the impact mechanism of agricultural activities on river hydrological processes, such as changes in water flow resistance and riverbed erosion, and provide data support for the formulation of a comprehensive river evolution.

[0065] Preferably, step S4 comprises the following steps:

[0066] Step S41: Calculate the land type ratio of the grid remote sensing data around the river channel to obtain the ratio of each type of land;

[0067] Step S42: performing data correction on the river channel morphology evolution curve based on the proportion of each type of land and the influencing parameters of the river channel evolution to obtain a corrected river channel evolution curve;

[0068] Step S43: simulating the evolution of hydrological influence on the three-dimensional river channel model based on the modified river channel evolution curve to obtain a model of the evolution of the river channel influenced by hydrology;

[0069] Step S44: Predicting the evolution of the river channel area based on the hydrological impact river channel evolution model to obtain a river channel evolution prediction result.

[0070] The present invention calculates the land type ratio of the grid around the river through remote sensing data, such as forest land, grassland, cultivated land, and construction land, and can quantitatively analyze the influence of different land use types on the evolution of the river, providing a more refined data basis for the subsequent prediction of the evolution of the river morphology; using the obtained land type ratio data, combined with the influencing parameters of the river evolution, the initial river morphology evolution curve is corrected, which can more accurately reflect the influence of different land use methods on the evolution of the river morphology, such as erosion, siltation, and river migration, and improve the reliability of the prediction results; based on the corrected river evolution curve, the three-dimensional river model is used to simulate the evolution of hydrological influence, which can more intuitively show the changing trend of the river morphology at different time scales in the future, such as the width, depth, and curvature of the river, and provide a visualized decision-making basis for river management and ecological restoration; based on the hydrological influence river evolution model, the widening, narrowing, and migration of the river are predicted, and the influence of these changes on the surrounding hydrological characteristics is evaluated. The prediction results can provide important references for river management, flood control planning, ecological protection, and land use, and help decision makers formulate sustainable management strategies.

[0071] Preferably, the present invention further provides a river channel evolution analysis system for executing the river channel evolution analysis method as described above, the river channel evolution analysis system comprising:

[0072] The evolution curve drawing module is used to construct a three-dimensional model of the river channel area to obtain the three-dimensional model of the river channel; perform hydrological data analysis on the river channel area based on the three-dimensional model of the river channel to obtain the river channel hydrological data; perform a river channel morphology evolution curve analysis on the three-dimensional model of the river channel based on the river channel hydrological data to obtain the river channel morphology evolution curve;

[0073] The grid data acquisition module is used to perform grid division of the surrounding remote sensing data of the river area to obtain grid remote sensing data of the surrounding river area; perform natural land grid acquisition on the grid remote sensing data of the surrounding river area to obtain natural land grid data; perform human activity grid acquisition on the grid remote sensing data of the surrounding river area to obtain human activity grid data;

[0074] The evolution impact parameter analysis module is used to analyze the river evolution impact parameters of the river area based on the natural land grid data and the human activity grid data to obtain the river evolution impact parameters;

[0075] The river evolution prediction module is used to correct the river morphology evolution curve based on the grid remote sensing data around the river and the river evolution influencing parameters to obtain the corrected river evolution curve; based on the corrected river evolution curve and the river three-dimensional model, the river area is predicted to evolve and the river evolution prediction result is obtained.

[0076] In summary, the present invention provides a river channel evolution analysis system, which consists of an evolution curve drawing module, a grid data acquisition module, an evolution influencing parameter analysis module, and a river channel evolution prediction module. It can implement any river channel evolution analysis method described in the present invention, and is used to combine the operations between computer programs running on each module to implement any river channel evolution analysis method. The internal structures of the system cooperate with each other, which can greatly reduce duplication of work and manpower investment, and can quickly and effectively provide a more accurate and efficient river channel evolution analysis process, thereby simplifying the operating procedures of the river channel evolution analysis system. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0078] Figure 1 A schematic diagram of the steps of a river channel evolution analysis method of the present invention;

[0079] Figure 2 for Figure 1 Detailed step flow diagram of step S2;

[0080] Figure 3 for Figure 2 Detailed step flow diagram of step S23; DETAILED DESCRIPTION

[0081] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0082] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0083] 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 only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0084] To achieve this, please refer to Figures 1 to 3 The present invention provides a river channel evolution analysis method, comprising the following steps:

[0085] Step S1: constructing a three-dimensional model of a river channel area to obtain a three-dimensional model of the river channel; analyzing hydrological data of the river channel area based on the three-dimensional model of the river channel to obtain river hydrological data; analyzing a river morphology evolution curve of the three-dimensional model of the river channel based on the river hydrological data to obtain a river morphology evolution curve;

[0086] Step S2: gridding the surrounding remote sensing data of the river area to obtain grid remote sensing data of the river area; acquiring natural land grids from the grid remote sensing data of the river area to obtain natural land grid data; acquiring human activity grids from the grid remote sensing data of the river area to obtain human activity grid data;

[0087] Step S3: Analyze the influencing parameters of river channel evolution in the river channel area based on the natural land grid data and the human activity grid data to obtain the influencing parameters of river channel evolution;

[0088] Step S4: Based on the grid remote sensing data around the river channel and the influencing parameters of the river channel evolution, the river channel morphology evolution curve is corrected to obtain a corrected river channel evolution curve; based on the corrected river channel evolution curve and the river channel three-dimensional model, the river channel area is predicted to evolve to obtain a river channel evolution prediction result.

[0089] In the embodiment of the present invention, please refer to Figure 1 FIG. 1 is a schematic diagram of the flow chart of the river channel evolution analysis method of the present invention. In this example, the river channel evolution analysis method includes the following steps:

[0090] Step S1: constructing a three-dimensional model of a river channel area to obtain a three-dimensional model of the river channel; analyzing hydrological data of the river channel area based on the three-dimensional model of the river channel to obtain river hydrological data; analyzing a river morphology evolution curve of the three-dimensional model of the river channel based on the river hydrological data to obtain a river morphology evolution curve;

[0091] In an embodiment of the present invention, a target river section is scanned by using an unmanned aerial vehicle equipped with a laser radar or sonar device to obtain high-precision point cloud data, and then the point cloud data is denoised, classified and spliced ​​to construct a three-dimensional model of the river channel, which can accurately reflect the geometric shape of the river channel, including riverbed elevation, river channel width, and curvature; based on the constructed three-dimensional model of the river channel, combined with historical hydrological data and hydrodynamic models, the water flow velocity, water depth, and flow hydrological parameters of the river channel are simulated and analyzed to obtain river channel hydrological data; using the obtained river channel hydrological data, combined with river channel morphology evolution theory and mathematical models, the scouring and siltation laws of the river channel under different hydrological conditions are analyzed, and a river channel morphology evolution curve is constructed, which can reflect the trend of river channel morphology changing over time.

[0092] Step S2: gridding the surrounding remote sensing data of the river area to obtain grid remote sensing data of the river area; acquiring natural land grids from the grid remote sensing data of the river area to obtain natural land grid data; acquiring human activity grids from the grid remote sensing data of the river area to obtain human activity grid data;

[0093] In an embodiment of the present invention, the remote sensing image data of the area surrounding the river is gridded according to the scope of the study area and the data accuracy requirements to form regular grid units, each grid unit corresponds to a certain geographic coordinate range, and the land cover type in each grid unit is identified and extracted by using the interpretation and classification technology of remote sensing images, for example, forest land, grassland, cultivated land, and construction land are distinguished, thereby obtaining natural land grid data. At the same time, the regional remote sensing data is used to perform texture data feature analysis to obtain human activity texture features, and based on the human activity texture features, the land use types related to human activities, such as towns, villages, roads, and farmlands, are identified and extracted to form human activity grid data.

[0094] Step S3: Analyze the influencing parameters of river channel evolution in the river channel area based on the natural land grid data and the human activity grid data to obtain the influencing parameters of river channel evolution;

[0095] In an embodiment of the present invention, key indicators related to river channel evolution, such as vegetation coverage, soil type, and slope, are extracted based on natural land grid data, and soil and water loss models and riverbank erosion models are used to quantitatively evaluate the degree of influence of natural factors on river channel evolution, and natural land impact parameters are constructed. Key indicators related to river channel evolution, such as urbanization rate and agricultural irrigation area, are extracted based on human activity grid data, and hydrological models are used to quantitatively evaluate the degree of influence of human activities on river channel evolution, and human activity impact parameters are constructed. The natural land impact parameters and human activity impact parameters are integrated to form comprehensive river channel evolution impact parameters.

[0096] Step S4: Based on the grid remote sensing data around the river channel and the influencing parameters of the river channel evolution, the river channel morphology evolution curve is corrected to obtain a corrected river channel evolution curve; based on the corrected river channel evolution curve and the river channel three-dimensional model, the river channel area is predicted to evolve to obtain a river channel evolution prediction result.

[0097] In an embodiment of the present invention, environmental factors related to river channel evolution, such as runoff, runoff velocity, and runoff hydrology, are extracted based on grid remote sensing data around the river channel, and the relationship between these environmental factors and river channel evolution is analyzed. Combined with the obtained river channel evolution influencing parameters, the constructed river channel morphology evolution curve is corrected to enable it to more accurately reflect the impact of future environmental changes and human activities on river channel evolution. Based on the corrected river channel evolution curve, combined with the constructed three-dimensional river channel model, the hydrodynamic model and riverbed evolution model are used to simulate and predict the river channel morphology evolution at different time scales in the future, and obtain river channel evolution prediction results, such as changes in river channel width, changes in riverbed elevation, and river flow evolution trends.

[0098] By constructing a three-dimensional model of the river channel, the present invention can intuitively and accurately express the geometric morphology, spatial structure and terrain characteristics of the river channel, provide basic data for subsequent analysis, and combine with hydrological data analysis to deeply understand the flow, velocity, water level and hydrological characteristics of the river channel, reveal the relationship between the hydrological process and the river channel morphology, and the drawing of the river channel morphology evolution curve can clearly show the historical evolution process of the river channel, and provide a reference basis for predicting future evolution trends; dividing the area around the river channel into remote sensing data grids can realize the refined management and analysis of the study area, and through the classification and processing of grid remote sensing data, natural land and human activity information can be extracted respectively to form a grid data set with multi-source data fusion, which provides a data basis for analyzing the impact of natural environment and human activities on river channel evolution, and also provides data support for the subsequent establishment of a more refined river channel evolution model; based on the natural land and human activity grid Grid data can be used to analyze the degree of influence and mechanism of different factors on river evolution, the relationship between vegetation coverage and riverbank erosion, the impact of land use changes on river runoff, and changes in river morphology. By quantifying each influencing factor, its contribution to river evolution can be more accurately evaluated, providing a scientific basis for formulating more targeted river management measures. Using grid remote sensing data around the river and river evolution influencing parameters, the river morphology evolution curve based on historical data can be corrected to reduce the influence of various complex factors on river hydrological data, such as human activities, so as to finely simulate the river evolution and improve the accuracy of the prediction results. Combining the corrected evolution curve with the three-dimensional river model for evolution prediction can simulate the evolution trend of the river under different scenarios in the future, provide decision support for river management, flood prevention and disaster reduction, and water resources protection, and minimize the negative impact of river evolution.

[0099] Preferably, step S1 comprises the following steps:

[0100] Step S11: performing segmented sonar processing on the river area to obtain segmented sonar data of the area;

[0101] In an embodiment of the present invention, a side scan sonar system is used to scan a river channel area in sections. According to a pre-planned survey line, the measurement along the river channel is controlled, and the sonar device is ensured to be perpendicular to the water surface. Real-time measurement position information and sonar data are recorded at the same time. The scanning range of each survey section should overlap with each other to ensure the integrity of the data. The raw sonar data of each survey section is preprocessed, including noise removal and water depth correction. Commonly used denoising methods include median filtering. Water depth correction needs to consider the influence of sound velocity profile and tidal level changes. The processed sonar data of each survey section is saved as an independent file, that is, regional segmented sonar data is obtained.

[0102] Step S12: merging the regional segmented sonar data to obtain river channel sonar data;

[0103] In an embodiment of the present invention, by using professional sonar data processing software, all regional segmented sonar data are imported, and the data are spatially aligned according to the coordinate information recorded in each data file to eliminate overlap and misalignment between different measurement sections. The aligned data are spliced ​​to form a complete river area sonar data set. During the splicing process, the data in the overlapping areas need to be weighted averaged or other fusion processed to ensure data consistency, and the merged river sonar data are eliminated for outliers and erroneous data to obtain the final river sonar data.

[0104] Step S13: constructing a three-dimensional model of the river channel area based on the river channel sonar data to obtain a three-dimensional model of the river channel;

[0105] In an embodiment of the present invention, the processed river sonar data is imported into the three-dimensional modeling software, and the point cloud data of the riverbed surface is generated according to the water depth information and coordinate information in the sonar data. The point cloud data is subjected to a triangulated mesh surface fitting method to construct a continuous riverbed surface model to improve the accuracy and details of the model. The point cloud data can be smoothed as needed, and the riverbed surface model and the terrain model can be spliced ​​together in combination with the terrain data on both sides of the river to construct a complete three-dimensional model of the river.

[0106] Step S14: performing hydrological data analysis on the river area based on the three-dimensional river model to obtain river hydrological data;

[0107] In an embodiment of the present invention, by utilizing hydraulic analysis software, a constructed three-dimensional model of a river channel is imported, boundary conditions are set according to actual conditions, such as upstream flow and downstream water level, hydraulic simulation calculations are performed, and hydrological data of the river channel, such as water depth, flow velocity, flow, and water level, are obtained.

[0108] Step S15: analyzing the river morphology evolution curve of the three-dimensional river model based on the river hydrological data to obtain the river morphology evolution curve.

[0109] In an embodiment of the present invention, by combining river topographic data and hydrological data from historical periods, analyzing the change indicators of river cross-section, longitudinal section, and riverbed scouring and silting, and using statistical analysis methods, a quantitative description of the river morphology evolution trend is made. For example, a comparison chart of the river cross-section morphology year by year can be drawn, the riverbed scouring and silting amount can be calculated, the river evolution trend can be analyzed, and a river morphology evolution curve can be drawn based on the analysis results.

[0110] The present invention can divide a large river channel area into several small areas by performing segmented sonar processing on the river channel area, and collect and process sonar data separately to effectively improve the efficiency and accuracy of data collection. It is especially suitable for rivers with large water depth changes and complex underwater terrain. Segmented processing also facilitates the use of different sonar parameter settings according to the characteristics of different river sections, thereby further improving the quality of data. By merging the regional segmented sonar data, the sonar data of each river section can be integrated into a unified coordinate system and reference plane to form a complete river channel sonar data set. Data merging requires splicing, correction and fusion processing to eliminate data redundancy and errors and ensure data continuity and consistency, which lays a data foundation for the subsequent construction of a high-precision three-dimensional model of the river channel. The three-dimensional model of the river channel constructed based on the river channel sonar data can accurately express the riverbed topography, water Sonar technology can penetrate water bodies and obtain underwater topographic data, which is especially suitable for turbid rivers with lush aquatic plants. The three-dimensional model of the river constructed based on sonar data can provide important basic data for the prediction of river evolution. Hydrological data analysis based on the three-dimensional model of the river can more accurately calculate the hydraulic parameters of the river cross-sectional area, providing a reliable basis for hydrological simulation and hydrodynamic calculations. Combined with hydrological observation data, it can analyze the characteristics of river runoff and water environment changes, and provide data support for the river evolution curve. Analysis of the river morphology evolution curve based on river hydrological data and the three-dimensional model of the river can more intuitively show the changes in river scouring and siltation, the evolution trend of river flow, and reveal the interaction between hydrological processes and river morphology, which is of great significance for predicting future river evolution trends and formulating scientific and reasonable river management strategies.

[0111] Preferably, step S14 comprises the following steps:

[0112] Step S141: setting nodes for the river area to obtain river node data;

[0113] In an embodiment of the present invention, a series of nodes are set along the direction of the river and the cross-sectional direction in the three-dimensional model of the river according to research needs and river characteristics. The distribution of the nodes should be able to reflect the morphological changes and water flow characteristics of the river. For example, nodes are set at bends, confluences, and sudden changes in cross-sections. Each node needs to record its three-dimensional coordinates and node number information and store them as river node data.

[0114] Step S142: performing a river channel transverse profile analysis on the river channel three-dimensional model based on the river channel node data to obtain river channel node profile data;

[0115] In an embodiment of the present invention, the cross-sectional information of the river channel at each node is extracted using three-dimensional modeling software based on the set river channel node data. The cross-sectional information includes cross-sectional morphology, cross-sectional width, cross-sectional area, and water depth data. Discrete points are selected as needed to express the cross-section, and the cross-sectional information of each node is associated with the node data to form river channel node profile data.

[0116] Step S143: acquiring the node flow velocity of the river area based on the river node data to obtain the river node flow velocity data;

[0117] In an embodiment of the present invention, hydraulic analysis software is used to import a three-dimensional model of a river channel and set boundary conditions to perform hydraulic simulation calculations. During the simulation calculation process, the flow velocity value at each node is extracted, and the flow velocity value of each node is associated with the node data to form river channel node flow velocity data.

[0118] Step S144: calculating the river runoff of the river node data based on the river node profile data and the river node flow velocity data to obtain the river node runoff data;

[0119] In an embodiment of the present invention, the water flow area of ​​each node section is calculated based on the acquired river node profile data, and the runoff of each node is calculated using hydraulic formulas in combination with the acquired river node flow velocity data. The runoff data of each node is associated with the node data to form river node runoff data.

[0120] Step S145: performing node data integration processing on the river area based on the river node velocity data and the river node runoff data to obtain river hydrological data.

[0121] In an embodiment of the present invention, the acquired river node data, river node profile data, river node velocity data, and river node runoff data are integrated to form a node river hydrological data set, and the node river hydrological data set is arranged and fitted in node order to generate a complete river hydrological data set.

[0122] The present invention can divide a continuous river into several representative sections by reasonably setting nodes in the river area, so as to facilitate refined hydrological data analysis. The setting of nodes needs to take into account factors such as river morphology and water flow characteristics. For example, nodes need to be encrypted in areas where the river bends, confluences, and water flow velocity changes dramatically, so as to improve the accuracy and representativeness of data collection; based on the river node data, a transverse profile analysis of the river three-dimensional model can be performed to obtain the cross-sectional morphological information of the river at each node, such as river width, water depth, and cross-sectional area. This information is the basic data for calculating hydraulic parameters and analyzing water flow structure, and is crucial for a deep understanding of the river hydrological process; obtaining flow velocity data at the river node can reflect the flow at that location. The movement state of the section and accurate flow velocity data are important bases for calculating flow, analyzing hydropower resources and evaluating water environment capacity. The runoff at the river node is calculated by combining the river node profile data and node velocity data. By calculating the flow at the river node, the water exchange and changes between different sections can be analyzed, and the hydraulic characteristics and transportation capacity of the river can be evaluated. The runoff calculation helps to understand the water distribution and movement laws of the river. The velocity data and runoff data of the river node are integrated and processed to obtain a complete river hydrological data set, which can be used to analyze the spatiotemporal changes of the river hydrological situation, evaluate the impact of human activities on the river hydrological process, and provide a scientific basis for water resources management and river governance.

[0123] Preferably, step S15 comprises the following steps:

[0124] Step S151: performing runoff impact intensity analysis on river hydrological data to obtain runoff impact intensity data;

[0125] In an embodiment of the present invention, the kinetic energy and scouring force of the river runoff are calculated based on the acquired river hydrological data, especially the flow velocity and flow data, combined with the river section geometric parameters, such as water depth and cross-sectional area. Commonly used calculation formulas include the Xie Cai formula. The calculated runoff impact force data is associated with the river node data to form runoff impact force data.

[0126] Step S152: performing runoff impact deformation simulation on the three-dimensional model of the river channel based on the runoff impact force data to obtain runoff impact deformation data;

[0127] In an embodiment of the present invention, by utilizing a river channel evolution model to import a three-dimensional river channel model, runoff impact force data and other relevant parameters, such as vegetation coverage, the influence of runoff impact force on riverbed scour and siltation at different time scales is simulated, the change in riverbed elevation is calculated, and the simulated riverbed deformation data is associated with time and space information to form runoff impact deformation data.

[0128] Step S153: plotting the runoff impact force data and the runoff impact deformation data to obtain a runoff impact deformation curve;

[0129] In an embodiment of the present invention, a data series of the change in riverbed elevation over time at a specific section or node is extracted based on the runoff impact deformation data, and a runoff impact deformation curve is drawn using data visualization tools, with the horizontal axis being time and the vertical axis being the change in riverbed elevation.

[0130] Step S154: performing river water level analysis on the river hydrological data to obtain river water level data;

[0131] In an embodiment of the present invention, by extracting river water level data at different time and space positions from the acquired river hydrological data, the original data can be interpolated as needed to improve the accuracy and continuity of the data, and the processed water level data can be associated with time and space information to form river water level data.

[0132] Step S155: performing water level erosion simulation on the three-dimensional model of the river channel based on the river channel water level data to obtain water level erosion data;

[0133] In an embodiment of the present invention, by utilizing a river channel evolution model or a hydrodynamic model, a three-dimensional river channel model and river channel water level data are imported to simulate the erosion of the river bank under different water level conditions, calculate the retreat distance or area of ​​the river bank line, and associate the simulated river bank erosion data with time and space information to form water level erosion data.

[0134] Step S156: plotting the river water level data and the water level erosion data to obtain a water level erosion curve;

[0135] In an embodiment of the present invention, a data series showing the change in the riverbank retreat distance or area over time at a specific section or node is extracted based on water level erosion data, and a water level erosion curve is drawn using data visualization tools, with the horizontal axis being time and the vertical axis being the riverbank retreat distance or area.

[0136] Step S157: fitting the runoff impact deformation curve and the water level erosion curve to a river channel morphology evolution curve to obtain a river channel morphology evolution curve.

[0137] In an embodiment of the present invention, by comprehensively considering the runoff impact deformation curve and the water level erosion curve, a comprehensive analysis is conducted on the evolution trend of the river morphology. The curve fitting method can be used to construct a mathematical model that can reflect the law of river morphology evolution. The obtained river morphology evolution curve can reflect the changing trends of river width, river curvature, and riverbed elevation indicators over time.

[0138] The present invention can quantitatively evaluate the degree of change of the riverbed morphology caused by runoff by performing runoff impact force analysis on the river hydrological data, and based on this, simulate the runoff impact deformation of the river three-dimensional model, which can help understand how the river responds to the scouring and transportation of water under different flow conditions, and provide a reference for the design of river flood control projects and river flow adjustment; the runoff impact force data and deformation data are plotted to form a runoff impact deformation curve, which can intuitively show the relationship between runoff impact force and riverbed deformation. This curve can serve as an important basis for analyzing the evolution trend of the river and predicting the degree of deformation in the future, and provide a scientific reference for formulating river management strategies; the water level analysis is performed on the river hydrological data, and the water level of the three-dimensional model of the river is simulated based on the river water level data. Erosion simulation can assess the risk of riverbank erosion under different water level conditions, which is of great significance for predicting riverbank collapse, formulating bank protection engineering plans, and ensuring the safety of river flood discharge; the river water level data and water level erosion data are plotted to obtain the water level erosion curve, which can clearly show the relationship between water level changes and the degree of riverbank erosion; this curve can be used as an important reference for the design of river evolution prediction system; the runoff impact deformation curve and the water level erosion curve are fitted to obtain the river morphology evolution curve that comprehensively considers the effects of runoff impact and water level erosion. This curve can more comprehensively and accurately reflect the river evolution process, and provide a more reliable basis for predicting future trends in river morphology changes and formulating scientific and reasonable river management plans.

[0139] Preferably, step S2 comprises the following steps:

[0140] Step S21: acquiring remote sensing image data of the river area to obtain remote sensing image data of the river area;

[0141] Step S22: dividing the river area remote sensing image data into grids around the river to obtain grid remote sensing data around the river;

[0142] Step S23: extracting human activity texture from the grid remote sensing data around the river channel to obtain human activity texture data;

[0143] Step S24: acquiring natural land grids from the grid remote sensing data around the river based on the human activity texture data to obtain natural land grid data;

[0144] Step S25: acquiring human activity grids from the grid remote sensing data around the river based on the human activity feature texture data to obtain human activity grid data.

[0145] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S2 in FIG. 1 , in this embodiment, step S2 includes the following steps:

[0146] Step S21: acquiring remote sensing image data of the river area to obtain remote sensing image data of the river area;

[0147] In an embodiment of the present invention, by selecting remote sensing image data with appropriate time, spatial resolution and spectral band according to the scope of the study area and data requirements, the remote sensing image data needs to be preprocessed, including radiation correction, geometric correction, and image enhancement, to eliminate sensor errors, terrain effects and atmospheric interference, and improve image quality.

[0148] Step S22: dividing the river area remote sensing image data into grids around the river to obtain grid remote sensing data around the river;

[0149] In an embodiment of the present invention, by utilizing GIS software, a suitable buffer zone distance is set according to the river vector boundary data, an area within a certain range around the river is extracted, and the extracted area around the river is gridded. A suitable grid size can be selected according to research requirements and data accuracy. The grid division method can use a regular grid, and the original remote sensing image data is cropped according to the divided grid to obtain the remote sensing image data corresponding to each grid unit, that is, the grid remote sensing data around the river.

[0150] Step S23: extracting human activity texture from the grid remote sensing data around the river channel to obtain human activity texture data;

[0151] In an embodiment of the present invention, a suitable texture analysis method is selected for the remote sensing image data of each grid unit to extract human activity information. Commonly used texture analysis methods include gray-level co-occurrence matrix and local binary pattern. These methods can quantitatively describe the spatial structural characteristics of the image, such as roughness, contrast, and directionality, which are closely related to the type of human activity. The extracted texture feature parameters are stored as raster data, and each pixel corresponds to a set of texture feature values, that is, human activity texture data is obtained.

[0152] Step S24: acquiring natural land grids from the grid remote sensing data around the river based on the human activity texture data to obtain natural land grid data;

[0153] In an embodiment of the present invention, by utilizing machine learning algorithms, such as support vector machines and random forests, grid remote sensing data around a river channel are classified. Training samples can be obtained based on field survey data and visual interpretation results of high-resolution remote sensing images, and associated with human activity texture data of corresponding grid cells. All grid cells are classified using the trained classification model, and grid cells classified as natural land are identified to obtain natural land grid data.

[0154] Step S25: acquiring human activity grids from the grid remote sensing data around the river based on the human activity feature texture data to obtain human activity grid data.

[0155] In the embodiment of the present invention, all grid units are classified by using a trained classification model, and grid units with classification results of human activities are identified to obtain human activity grid data.

[0156] The present invention can convert a large-scale, continuous river area into regular grid units by acquiring remote sensing image data of the river area and performing grid division, which is convenient for spatial analysis and data management. High-resolution remote sensing images can provide rich spatial information, providing a data basis for extracting environmental elements around the river and analyzing their spatial distribution characteristics. Human activity texture extraction is performed on the grid remote sensing data around the river, which can capture the texture characteristics of human activities on the remote sensing images. Human activities often leave unique texture marks in land use patterns, building forms, and vegetation coverage. By extracting these texture features, we can provide a basis for distinguishing between natural environments and human activity areas. By acquiring natural land grids based on human activity texture data, we can effectively distinguish between natural environments and human activity areas, and extract natural land information, such as forests and grasslands. This provides data support for analyzing the impact of the natural environment on rivers, such as the impact of vegetation cover on soil erosion and the impact of land use changes on river hydrological processes. By acquiring human activity grids based on human activity feature texture data, we can identify and extract different types of human activity areas, such as towns, roads, and farmlands. This provides data support for analyzing the impact of human activities on rivers, such as the impact of urban expansion on river water quality, the impact of agricultural activities on non-point source pollution, and the impact of water conservancy projects on river morphology.

[0157] Preferably, step S23 includes the following steps:

[0158] Step S231: acquiring grid color features of grid remote sensing data around the river channel to obtain grid remote sensing color feature data;

[0159] Step S232: performing urban area color texture analysis on the grid remote sensing data around the river based on the grid remote sensing color feature data to obtain the urban area color texture;

[0160] Step S233: acquiring texture data of non-urban areas from the grid remote sensing data around the river based on the color texture of the urban area, thereby obtaining texture data of non-urban areas;

[0161] Step S234: extracting regularly distributed agricultural texture features from the texture data of non-urban areas to obtain regularly distributed agricultural texture feature data;

[0162] Step S235: performing agricultural area regular texture analysis on the grid remote sensing data around the river based on the regularly distributed agricultural texture feature data to obtain the agricultural area regular texture;

[0163] Step S236: fitting human activity texture grid data to the color texture of the urban area and the regular texture of the agricultural area to obtain human activity texture data.

[0164] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 2 FIG. 2 is a schematic diagram of a detailed step flow chart of step S23 in FIG. 2 . In this embodiment, step S23 includes the following steps:

[0165] Step S231: acquiring grid color features of grid remote sensing data around the river channel to obtain grid remote sensing color feature data;

[0166] In an embodiment of the present invention, color feature information is extracted by remote sensing image data of each grid unit. Commonly used color features include RGB color space, HSV color space, and Lab color space. The mean and standard deviation statistical features of each color channel can be calculated to describe the color features of the grid unit.

[0167] Step S232: performing urban area color texture analysis on the grid remote sensing data around the river based on the grid remote sensing color feature data to obtain the urban area color texture;

[0168] In an embodiment of the present invention, by analyzing the existing urban area remote sensing image sample data, the common laws of color characteristics of urban areas are extracted. Urban areas usually present higher brightness values ​​and lower vegetation index values ​​in the RGB color space. A statistical analysis method can be used to construct a color feature recognition model for urban areas. The model is used to analyze the color feature data of all grid cells to identify grid cells with color texture characteristics of urban areas.

[0169] Step S233: acquiring texture data of non-urban areas from the grid remote sensing data around the river based on the color texture of the urban area, thereby obtaining texture data of non-urban areas;

[0170] In an embodiment of the present invention, the grid cells in the urban area identified are excluded, and the remaining grid cells are non-urban areas. For the grid cells in the non-urban areas, more refined texture features are extracted, such as gray-level co-occurrence matrix and local binary pattern. These methods can quantitatively describe the spatial structural characteristics of different types of land features in the non-urban areas.

[0171] Step S234: extracting regularly distributed agricultural texture features from the texture data of non-urban areas to obtain regularly distributed agricultural texture feature data;

[0172] In an embodiment of the present invention, by analyzing existing remote sensing image sample data of agricultural areas, especially regularly distributed agricultural planting areas, such as farmlands and orchards, the common laws of texture features of such areas are extracted. Regularly distributed agricultural areas usually show high regularity and directionality in texture features. Image segmentation and edge detection methods can be used to extract the boundaries and shape features of agricultural areas, and combined with texture feature parameters for analysis.

[0173] Step S235: performing agricultural area regular texture analysis on the grid remote sensing data around the river based on the regularly distributed agricultural texture feature data to obtain the agricultural area regular texture;

[0174] In an embodiment of the present invention, by utilizing the extracted regularly distributed agricultural texture feature data to construct an agricultural area texture feature recognition model, template matching and machine learning methods can be used to compare and analyze the texture features of the grid units to be identified with the texture features of known agricultural areas to identify the grid units with regular texture features of agricultural areas.

[0175] Step S236: fitting human activity texture grid data to the color texture of the urban area and the regular texture of the agricultural area to obtain human activity texture data.

[0176] In an embodiment of the present invention, human activity texture grid data is formed by merging the identified urban area grid cells and the identified agricultural area grid cells, and the merged data is smoothed to eliminate boundary effects and noise influences.

[0177] The present invention obtains the color features of the grid remote sensing data around the river channel, and performs color and texture analysis of the urban area based on the data, thereby making full use of the significant features of the urban area in color and texture. Urban areas usually present color combinations and texture patterns that are different from the surrounding natural environment. For example, buildings are densely packed, roads are crisscrossed, and vegetation is sparse. These features can be used to distinguish urban areas from other land cover types. On the basis of identifying urban areas, the texture features of non-urban areas can be further analyzed to extract regularly distributed agricultural texture features. Agricultural areas, especially large-scale farmland, usually present regular geometric shapes and arrangements, such as rectangular or strip-shaped farmland, which is in sharp contrast to the random texture features of the natural environment and can serve as an important basis for identifying agricultural areas. Regular texture analysis of agricultural areas based on regularly distributed agricultural texture feature data can more accurately identify the boundaries and scope of agricultural areas and avoid misjudging other areas with similar color or texture features as agricultural areas. Fitting the color texture of urban areas with the regular texture of agricultural areas can obtain more complete and accurate human activity texture data. This data not only contains information on urban and agricultural areas, but can also distinguish other types of human activity areas, providing a more refined data basis for subsequent analysis of the impact of human activities on rivers.

[0178] Preferably, step S3 comprises the following steps:

[0179] Step S31: Analyze the vegetation coverage of natural land grid data to obtain the vegetation coverage of natural grids;

[0180] In an embodiment of the present invention, the vegetation index within the natural land grid unit is extracted by utilizing the spectral information of the remote sensing image. Commonly used vegetation indices include the normalized vegetation index and the enhanced vegetation index. According to the machine learning model, the vegetation index is converted into vegetation coverage. The model is calibrated and verified using high-resolution remote sensing images to obtain the vegetation coverage data of each natural land grid unit.

[0181] Step S32: analyzing the influencing parameters of the river flow velocity in the river area based on the vegetation coverage rate of the natural grid, and obtaining the influencing parameters of the flow velocity by the natural land;

[0182] In an embodiment of the present invention, by analyzing the influence mechanism of vegetation coverage on river flow velocity, such as the obstruction of vegetation to water flow and the impact on riverbed roughness, a hydraulic model can be used to establish a quantitative relationship between vegetation coverage and river flow velocity. Based on the vegetation coverage data of natural land grid units, the degree of obstruction or promotion of river flow velocity by each grid unit is calculated to obtain the parameters of natural land affecting flow velocity.

[0183] Step S33: Analyze the urban cement coverage rate of the human activity grid data to obtain the urban cement coverage rate;

[0184] In an embodiment of the present invention, by utilizing high-resolution remote sensing images or land use data, the area of ​​impervious surfaces in urban areas is extracted, and the ratio of the impervious surface area to the total area of ​​the urban area is used as the urban cement coverage rate to obtain the cement coverage rate data of each urban grid unit.

[0185] Step S34: analyzing the parameters affecting river runoff in the river area based on the urban cement coverage rate to obtain parameters affecting runoff in the urban area;

[0186] In an embodiment of the present invention, by analyzing the impact mechanism of urban cement coverage on river runoff, for example, the increase in impervious surface leads to an increase in surface runoff and a decrease in infiltration, a hydrological model can be used to establish a quantitative relationship between urban cement coverage and river runoff. According to the cement coverage data of urban grid units, the degree of increase in river runoff for each grid unit is calculated to obtain the urban runoff parameters.

[0187] Step S35: analyzing the parameters of agricultural river flow velocity influencing the river area based on the human activity land grid to obtain agricultural flow velocity parameters;

[0188] In an embodiment of the present invention, by analyzing the impact mechanism of agricultural activities on river flow velocity, such as the impact of agricultural activities on aquatic plants, and analyzing the impact of river water volume and flow morphology based on this impact, the hydraulic model can be used in combination with the agricultural planting structure to establish a quantitative relationship between agricultural activities and river flow velocity, and the degree of influence of each grid unit on the river flow velocity is calculated according to the type and area of ​​the agricultural grid unit to obtain the agricultural impact flow velocity parameters.

[0189] Step S36: fitting the human activity impact weights on the runoff parameters affected by the town and the velocity parameters affected by agriculture to obtain the river channel parameters affected by human activities;

[0190] In an embodiment of the present invention, the weight coefficients of the two are determined by using the hierarchical analysis method according to the contribution of the runoff parameters affected by towns and the flow velocity parameters affected by agriculture to the river evolution. The runoff parameters affected by towns and the flow velocity parameters affected by agriculture are multiplied by the corresponding weight coefficients respectively, and then summed up to obtain the river parameters affected by human activities that comprehensively consider the impact of urban and agricultural activities.

[0191] Step S37: Integrate the velocity parameters affected by natural land and the river channel parameters affected by human activities into river channel evolution parameters to obtain river channel evolution parameters.

[0192] In an embodiment of the present invention, by performing spatial superposition analysis on the flow velocity parameters affected by natural land and the river channel parameters affected by human activities, the comprehensive impact value of each grid unit is obtained. The superposition method can be adjusted according to the direction and intensity of different influencing factors, such as weighted superposition, to finally obtain the river channel evolution influencing parameters.

[0193] The present invention analyzes the vegetation coverage rate of natural land grid data to understand the distribution and density of vegetation in natural land, which helps to evaluate the protective and regulatory effects of natural land on rivers and provides important basic data for subsequent analysis of river flow velocity and evolution; based on the vegetation coverage rate analysis of natural grids, the flow velocity law of river areas can be studied. Vegetation can affect the water flow velocity and direction of rivers and protect riverbeds from erosion, so as to quantify the degree of influence of natural land on river flow velocity and provide important parameters for predicting river evolution; the urban cement coverage rate analysis of human activity grid data helps to understand the impact of urbanization on rivers. The cement coverage rate can reflect the degree of change of human activities on natural land, which is of great significance for the subsequent analysis of the impact of urbanization on river runoff; the focus is on analyzing the impact of urban cement coverage rate on river area runoff The impact of flow dynamics, cement ground will affect the absorption and infiltration of rainwater, increase surface runoff, and thus affect the flow and water level of the river; agricultural activities will also have an impact on the flow velocity of the river. By analyzing the land grid of human activities, the impact of agriculture on the flow velocity and water flow pattern of the river can be evaluated, which helps to fully understand the comprehensive impact of human activities on the river; in order to more comprehensively understand the impact of human activities on the river, the impact of urban cement coverage on runoff and the impact of agricultural activities on flow velocity are comprehensively considered, and the comprehensive impact parameters of human activities on the river are obtained through weight fitting; the velocity parameters of natural land and the river parameters of human activities are integrated to construct the river evolution impact parameters, which can comprehensively reflect the comprehensive impact of natural factors and human activities on river evolution, and provide a scientific basis for river management, ecological restoration and sustainable development.

[0194] Preferably, step S35 includes the following steps:

[0195] Step S351: Identify the regular vegetation coverage rate of the human activity land grid to obtain the regular vegetation coverage rate;

[0196] In an embodiment of the present invention, agricultural land is distinguished from human activity land grid data, and areas with regular vegetation coverage, such as farmland and orchards, are further identified. The texture features of remote sensing images and the temporal variation characteristics of vegetation indexes can be used for identification. Farmland usually presents regular geometric shapes and higher vegetation index values ​​during the growing season, and finally regular vegetation coverage data of each agricultural grid unit is obtained.

[0197] Step S352: Calculate the amount of land fertilizer used for the regular vegetation coverage rate to obtain the amount of land fertilizer used;

[0198] In an embodiment of the present invention, a relationship model between regular vegetation coverage and land fertilizer usage is established based on the fertilizer requirements and planting density of different crops. The model is used in combination with the regular vegetation coverage data of each agricultural grid unit to calculate the fertilizer usage of the grid unit.

[0199] Step S353: performing nutrient growth analysis on the river area based on the amount of land fertilizer used to obtain river nutrient growth data;

[0200] In an embodiment of the present invention, by analyzing the impact of fertilizer application on nutrients in river water bodies, such as the input of nitrogen and phosphorus elements, hydrological models and material transport models can be used to simulate changes in nutrient content entering the river water body through surface runoff and underground infiltration after fertilizer application, and ultimately obtain nutrient growth data for each river section or grid unit.

[0201] Step S354: performing river algae growth analysis on the river area based on the river nutrient growth data to obtain river algae growth data;

[0202] In an embodiment of the present invention, by simulating the growth of algae under different nutrient concentrations based on a relationship model between the nutrient content of water bodies and the growth of algae, such as a nutrient limitation curve and an algae growth kinetics model, the differences in nutrient utilization efficiency of different algae species can be taken into account to obtain the growth data of the algae biomass or chlorophyll concentration index for each river section or grid unit.

[0203] Step S355: calculating the amount of aquatic plant reduction in the river area based on the river algae growth data to obtain aquatic plant reduction data;

[0204] In an embodiment of the present invention, by analyzing the competitive inhibitory effect of algae growth on aquatic plants, such as competition for light and nutrients, an ecological model can be used to establish a negative correlation between algae biomass and aquatic plant biomass, and the reduction in aquatic plants in each river section or grid unit can be calculated based on river algae growth data.

[0205] Step S356: Analyze the influencing parameters of the river flow velocity in the river area based on the aquatic plant reduction data to obtain the agricultural influencing flow velocity parameters.

[0206] In an embodiment of the present invention, by analyzing the impact mechanism of aquatic plants on river flow velocity, such as the obstruction of aquatic plants to water flow, a hydraulic model can be used to establish a relationship model between aquatic plant biomass and river flow velocity, and based on the aquatic plant reduction data, the flow velocity change of each river section or grid unit is calculated to obtain the agricultural impact flow velocity parameters.

[0207] The present invention can distinguish between natural vegetation and artificial vegetation by identifying regular vegetation coverage in human activity land grids, such as crop planting in farmland, and provide a more accurate data basis for subsequent analysis of the impact of agricultural activities on the river environment; based on regular vegetation coverage, the land fertilizer usage in different areas can be estimated, which provides a quantitative indicator for evaluating the input of nutrients to the river from agricultural activities, and is helpful for analyzing the trend of river water quality changes; by using land fertilizer usage data, the impact on the growth of nutrients in the river area can be analyzed, and the risk of eutrophication of river water bodies caused by nitrogen and phosphorus nutrients input caused by agricultural activities can be evaluated, providing a scientific basis for analyzing river evolution; the increase of nutrients in the river leads to Excessive growth of algae can be predicted by analyzing the impact of river nutrients on algae growth, which can help predict potential risks to river ecosystems. Excessive growth of algae can lead to a decrease in water oxygen content, affecting the survival of aquatic organisms. By analyzing the growth data of river algae, the changing trend of dissolved oxygen in the water body can be predicted, and then the reduction in aquatic plants can be calculated, which provides a quantitative indicator for evaluating the impact of agricultural activities on the structure and function of river ecosystems. Based on the data on the reduction of aquatic plants, its impact on river flow velocity is analyzed, and the parameters of agricultural impact on flow velocity are constructed, which can help to deeply understand the impact mechanism of agricultural activities on river hydrological processes, such as changes in water flow resistance and riverbed erosion, and provide data support for the formulation of a comprehensive river evolution.

[0208] Preferably, step S4 comprises the following steps:

[0209] Step S41: Calculate the land type ratio of the grid remote sensing data around the river channel to obtain the ratio of each type of land;

[0210] In an embodiment of the present invention, by utilizing land use classification data or remote sensing image classification results, the area proportions of different land use types in each grid unit around the river are counted, and the statistical data on the area proportions of each type of land are associated with the grid data to form proportion data of each type of land.

[0211] Step S42: performing data correction on the river channel morphology evolution curve based on the proportion of each type of land and the influencing parameters of the river channel evolution to obtain a corrected river channel evolution curve;

[0212] In an embodiment of the present invention, by analyzing the impact mechanism and degree of different land use types on river channel evolution, for example, woodland and grassland have a stronger protective effect on river banks than cultivated land, and construction land accelerates river channel erosion. According to the proportion data of each type of land and the influencing parameters of river channel evolution, a correction model is established to correct the initial river channel morphology evolution curve.

[0213] Step S43: simulating the evolution of hydrological influence on the three-dimensional river channel model based on the modified river channel evolution curve to obtain a model of the evolution of the river channel influenced by hydrology;

[0214] In an embodiment of the present invention, the corrected river morphology evolution curve is used as an input parameter and applied to a river evolution model. According to the research objectives and data conditions, model parameters, such as water flow boundary conditions, are set to simulate the influence of hydrological processes on river morphology changes at different time scales, such as changes in riverbed depth, degree of riverbank erosion, and the number of riverbank bends and deformations. Finally, a river evolution model that can reflect the hydrological influence is obtained.

[0215] Step S44: Predicting the evolution of the river channel area based on the hydrological impact river channel evolution model to obtain a river channel evolution prediction result.

[0216] In an embodiment of the present invention, a hydrological impact river channel evolution model is used to predict and analyze river channel evolution. Based on current and historical river channel data, the morphological changes of the river channel at different time scales in the future are simulated and predicted, and the stability, migration rate, alluvial or erosion patterns of the river channel morphology over time are analyzed.

[0217] The present invention calculates the land type ratio of the grid around the river through remote sensing data, such as forest land, grassland, cultivated land, and construction land, and can quantitatively analyze the influence of different land use types on the evolution of the river, providing a more refined data basis for the subsequent prediction of the evolution of the river morphology; using the obtained land type ratio data, combined with the influencing parameters of the river evolution, the initial river morphology evolution curve is corrected, which can more accurately reflect the influence of different land use methods on the evolution of the river morphology, such as erosion, siltation, and river migration, and improve the reliability of the prediction results; based on the corrected river evolution curve, the three-dimensional river model is used to simulate the evolution of hydrological influence, which can more intuitively show the changing trend of the river morphology at different time scales in the future, such as the width, depth, and curvature of the river, and provide a visualized decision-making basis for river management and ecological restoration; based on the hydrological influence river evolution model, the widening, narrowing, and migration of the river are predicted, and the influence of these changes on the surrounding hydrological characteristics is evaluated. The prediction results can provide important references for river management, flood control planning, ecological protection, and land use, and help decision makers formulate sustainable management strategies.

[0218] Preferably, the present invention further provides a river channel evolution analysis system for executing the river channel evolution analysis method as described above, the river channel evolution analysis system comprising:

[0219] The evolution curve drawing module is used to construct a three-dimensional model of the river channel area to obtain the three-dimensional model of the river channel; perform hydrological data analysis on the river channel area based on the three-dimensional model of the river channel to obtain the river channel hydrological data; perform a river channel morphology evolution curve analysis on the three-dimensional model of the river channel based on the river channel hydrological data to obtain the river channel morphology evolution curve;

[0220] The grid data acquisition module is used to perform grid division of the surrounding remote sensing data of the river area to obtain grid remote sensing data of the surrounding river area; perform natural land grid acquisition on the grid remote sensing data of the surrounding river area to obtain natural land grid data; perform human activity grid acquisition on the grid remote sensing data of the surrounding river area to obtain human activity grid data;

[0221] The evolution impact parameter analysis module is used to analyze the river evolution impact parameters of the river area based on the natural land grid data and the human activity grid data to obtain the river evolution impact parameters;

[0222] The river evolution prediction module is used to correct the river morphology evolution curve based on the grid remote sensing data around the river and the river evolution influencing parameters to obtain the corrected river evolution curve; based on the corrected river evolution curve and the river three-dimensional model, the river area is predicted to evolve and the river evolution prediction result is obtained.

[0223] In summary, the present invention provides a river channel evolution analysis system, which consists of an evolution curve drawing module, a grid data acquisition module, an evolution influencing parameter analysis module, and a river channel evolution prediction module. It can implement any river channel evolution analysis method described in the present invention, and is used to combine the operations between computer programs running on each module to implement any river channel evolution analysis method. The internal structures of the system cooperate with each other, which can greatly reduce duplication of work and manpower investment, and can quickly and effectively provide a more accurate and efficient river channel evolution analysis process, thereby simplifying the operating procedures of the river channel evolution analysis system.

[0224] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be 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 present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A river channel evolution analysis method, characterized in that: The following steps are involved: Step S1: construct a three-dimensional model of the river channel area to obtain the three-dimensional model of the river channel; perform hydrological data analysis on the river channel area based on the three-dimensional model of the river channel to obtain river hydrological data; perform a river morphology evolution curve analysis on the three-dimensional model of the river channel based on the river hydrological data to obtain the river morphology evolution curve; wherein step S1 is specifically as follows: Step S11: performing segmented sonar processing on the river area to obtain segmented sonar data of the area; Step S12: merging the regional segmented sonar data to obtain river channel sonar data; Step S13: constructing a three-dimensional model of the river channel area based on the river channel sonar data to obtain a three-dimensional model of the river channel; Step S14: Analyze the hydrological data of the river area based on the three-dimensional model of the river to obtain the river hydrological data; wherein step S14 is specifically as follows: Step S141: setting nodes for the river area to obtain river node data; Step S142: performing a river channel transverse profile analysis on the river channel three-dimensional model based on the river channel node data to obtain river channel node profile data; Step S143: acquiring the node flow velocity of the river area based on the river node data to obtain the river node flow velocity data; Step S144: calculating the river runoff of the river node data based on the river node profile data and the river node flow velocity data to obtain the river node runoff data; Step S145: performing node data integration processing on the river area based on the river node velocity data and the river node runoff data to obtain river hydrological data; Step S15: analyzing the river morphology evolution curve of the three-dimensional model of the river based on the river hydrological data to obtain the river morphology evolution curve; wherein step S15 is specifically as follows: Step S151: analyzing the runoff impact intensity of the river hydrological data to obtain the runoff impact intensity data; Step S152: performing runoff impact deformation simulation on the three-dimensional model of the river channel based on the runoff impact force data to obtain runoff impact deformation data; Step S153: plotting the runoff impact force data and the runoff impact deformation data to obtain a runoff impact deformation curve; Step S154: performing river water level analysis on the river hydrological data to obtain river water level data; Step S155: performing water level erosion simulation on the three-dimensional model of the river channel based on the river channel water level data to obtain water level erosion data; Step S156: plotting the river water level data and the water level erosion data to obtain a water level erosion curve; Step S157: fitting the runoff impact deformation curve and the water level erosion curve to a river channel morphology evolution curve to obtain a river channel morphology evolution curve; Step S2: gridding the surrounding remote sensing data of the river area to obtain grid remote sensing data of the river area; acquiring natural land grids from the grid remote sensing data of the river area to obtain natural land grid data; acquiring human activity grids from the grid remote sensing data of the river area to obtain human activity grid data; Step S3: Analyze the influencing parameters of river channel evolution in the river channel area based on the natural land grid data and the human activity grid data to obtain the influencing parameters of river channel evolution; Step S4: Based on the grid remote sensing data around the river channel and the influencing parameters of the river channel evolution, the river channel morphology evolution curve is corrected to obtain a corrected river channel evolution curve; based on the corrected river channel evolution curve and the river channel three-dimensional model, the river channel area is predicted to evolve to obtain a river channel evolution prediction result.

2. The river channel evolution analysis method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: acquiring remote sensing image data of the river area to obtain remote sensing image data of the river area; Step S22: dividing the river area remote sensing image data into grids around the river to obtain grid remote sensing data around the river; Step S23: extracting human activity texture from the grid remote sensing data around the river channel to obtain human activity texture data; Step S24: acquiring natural land grids from the grid remote sensing data around the river based on the human activity texture data to obtain natural land grid data; Step S25: acquiring human activity grids from the grid remote sensing data around the river based on the human activity feature texture data to obtain human activity grid data.

3. The river channel evolution analysis method according to claim 2, characterized in that: Step S23 includes the following steps: Step S231: acquiring grid color features of grid remote sensing data around the river channel to obtain grid remote sensing color feature data; Step S232: performing urban area color texture analysis on the grid remote sensing data around the river based on the grid remote sensing color feature data to obtain the urban area color texture; Step S233: acquiring texture data of non-urban areas from the grid remote sensing data around the river based on the color texture of the urban area, thereby obtaining texture data of non-urban areas; Step S234: extracting regularly distributed agricultural texture features from the texture data of non-urban areas to obtain regularly distributed agricultural texture feature data; Step S235: performing agricultural area regular texture analysis on the grid remote sensing data around the river based on the regularly distributed agricultural texture feature data to obtain the agricultural area regular texture; Step S236: fitting human activity texture grid data to the color texture of the urban area and the regular texture of the agricultural area to obtain human activity texture data.

4. The river channel evolution analysis method according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: Analyze the vegetation coverage of natural land grid data to obtain the vegetation coverage of natural grids; Step S32: analyzing the influencing parameters of the river flow velocity in the river area based on the vegetation coverage rate of the natural grid, and obtaining the influencing parameters of the flow velocity by the natural land; Step S33: Analyze the urban cement coverage rate on the human activity grid data to obtain the urban cement coverage rate; Step S34: analyzing the parameters affecting river runoff in the river area based on the urban cement coverage rate to obtain parameters affecting runoff in the urban area; Step S35: analyzing the parameters of agricultural river flow velocity influencing the river area based on the human activity land grid to obtain agricultural flow velocity parameters; Step S36: fitting the human activity impact weights on the runoff parameters affected by the town and the velocity parameters affected by agriculture to obtain the river channel parameters affected by human activities; Step S37: Integrate the velocity parameters affected by natural land and the river channel parameters affected by human activities into river channel evolution parameters to obtain river channel evolution parameters.

5. The river channel evolution analysis method according to claim 4, characterized in that: Step S35 includes the following steps: Step S351: Identify the regular vegetation coverage rate of the human activity land grid to obtain the regular vegetation coverage rate; Step S352: Calculate the amount of land fertilizer used for the regular vegetation coverage rate to obtain the amount of land fertilizer used; Step S353: performing nutrient growth analysis on the river area based on the amount of land fertilizer used to obtain river nutrient growth data; Step S354: performing river algae growth analysis on the river area based on the river nutrient growth data to obtain river algae growth data; Step S355: calculating the amount of aquatic plant reduction in the river area based on the river algae growth data to obtain aquatic plant reduction data; Step S356: Analyze the influencing parameters of the river flow velocity in the river area based on the aquatic plant reduction data to obtain the agricultural influencing flow velocity parameters.

6. The river channel evolution analysis method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: Calculate the land type ratio of the grid remote sensing data around the river channel to obtain the ratio of each type of land; Step S42: performing data correction on the river channel morphology evolution curve based on the proportion of each type of land and the influencing parameters of the river channel evolution to obtain a corrected river channel evolution curve; Step S43: simulating the evolution of hydrological influence on the three-dimensional river channel model based on the modified river channel evolution curve to obtain a model of the evolution of the river channel influenced by hydrology; Step S44: Predicting the evolution of the river channel area based on the hydrological impact river channel evolution model to obtain a river channel evolution prediction result.

7. A river channel evolution analysis system, characterized in that: Used to execute the river channel evolution analysis method as claimed in claim 1, the river channel evolution analysis system comprises: The evolution curve drawing module is used to construct a three-dimensional model of the river channel area to obtain the three-dimensional model of the river channel; perform hydrological data analysis on the river channel area based on the three-dimensional model of the river channel to obtain the river channel hydrological data; perform a river channel morphology evolution curve analysis on the three-dimensional model of the river channel based on the river channel hydrological data to obtain the river channel morphology evolution curve; The grid data acquisition module is used to perform grid division of the surrounding remote sensing data of the river area to obtain grid remote sensing data of the surrounding river area; perform natural land grid acquisition on the grid remote sensing data of the surrounding river area to obtain natural land grid data; perform human activity grid acquisition on the grid remote sensing data of the surrounding river area to obtain human activity grid data; The evolution impact parameter analysis module is used to analyze the river evolution impact parameters of the river area based on the natural land grid data and the human activity grid data to obtain the river evolution impact parameters; The river evolution prediction module is used to correct the river morphology evolution curve based on the grid remote sensing data around the river and the river evolution influencing parameters to obtain the corrected river evolution curve; based on the corrected river evolution curve and the river three-dimensional model, the river area is predicted to evolve and the river evolution prediction result is obtained.

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