Water conservancy slope ecological management method and system based on multi-source sensing data

Abstract

The invention relates to the technical field of information processing, in particular to a water conservancy slope ecological management method and system based on multi-source sensing data. The method comprises the following steps: collecting meteorological monitoring data, soil hydrological monitoring data, vegetation coverage monitoring data, topographic surveying and mapping data and water and soil loss monitoring data as original slope ecological environment data; performing data pre-registration processing on the original slope ecological environment data to obtain standardized slope ecological environment data; performing erosion-runoff coupling process simulation by using the standardized slope ecological environment data to obtain water and soil erosion dynamic parameters; performing water and soil loss trend prediction based on the water and soil erosion kinetic parameters, and generating water and soil loss risk assessment data; and designing an ecological management scheme based on the water and soil loss risk assessment data. By dynamically adjusting and optimizing the ecological management scheme, the ecological state of the slope is monitored in real time, and continuous improvement and self-adaptive optimization of ecological management of the water conservancy slope are achieved.

Description

Technical Field

[0001] The present invention relates to the field of information processing technology, and in particular to a method and system for water conservancy slope ecological management based on multi-source sensor data. Background Art

[0002] Early governance methods mainly rely on engineering measures, such as retaining walls and drainage ditches, which can stabilize slopes in the short term, but have limited effects on ecological restoration. With the development of the concept of ecological restoration, vegetation slope protection technology has been gradually promoted, and the stability of slope soil has been enhanced by planting suitable plants. Traditional slope governance methods have many shortcomings in data collection, processing and governance optimization, which makes it difficult to accurately predict and dynamically adjust the governance effect. In the process of slope governance, the collected data usually comes from different sensor equipment, such as meteorological monitoring stations, soil moisture sensors, and drone remote sensing systems. However, due to the large differences in data formats, time scales, and spatial resolutions of different sensors, there is a lack of unified data preprocessing and correction standards, which makes data fusion difficult, affecting the accuracy and reliability of subsequent analysis. The current ecological governance scheme mainly relies on expert experience and static model deduction, which makes it difficult to achieve adaptive optimization for different slope environments. Especially for slope vegetation sowing, drainage facility regulation, and precision irrigation, the existing methods lack a data-driven dynamic feedback mechanism, which makes it difficult for governance measures to accurately match actual ecological needs. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a method and system for water conservancy slope ecological management based on multi-source sensor data to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a method for water conservancy slope ecological management based on multi-source sensor data includes the following steps: Step S1: collecting meteorological monitoring data, soil hydrological monitoring data, vegetation coverage monitoring data, topographic mapping data and soil erosion monitoring data as original slope ecological environment data; Step S2: performing data pre-registration processing on the original slope ecological environment data to obtain standardized slope ecological environment data; Step S3: using standardized slope ecological environment data to simulate the erosion-runoff coupling process, and obtaining soil and water erosion dynamics parameters; based on the soil and water erosion dynamics parameters, predicting the soil and water loss trend, and generating soil and water loss risk assessment data; Step S4: Designing an ecological governance plan based on soil and water loss risk assessment data; Step S5: Execute slope vegetation seeding simulation and record initial vegetation coverage data; implement dynamic regulation of slope drainage facilities and record optimized slope water flow regulation data; carry out slope vegetation precision irrigation simulation and record slope vegetation irrigation data; implement water conservancy slope ecological monitoring based on initial vegetation coverage data, optimized slope water flow regulation data and vegetation growth monitoring data to obtain ecological management monitoring data; Step S6: Evaluate the governance effect of the ecological governance monitoring data and generate ecological governance optimization parameters; optimize the ecological governance plan based on the ecological governance optimization parameters.

[0005] The present invention can comprehensively obtain the dynamic change information of the slope ecological environment by collecting meteorological monitoring data, soil hydrological monitoring data, vegetation coverage monitoring data, topographic mapping data and soil erosion monitoring data, improve the integrity and representativeness of the data, and provide a reliable basis for subsequent data processing and analysis. The original slope ecological environment data is pre-registered, which can effectively eliminate the format, time scale and spatial resolution differences between different data sources, establish a unified data standard, improve the accuracy and consistency of data fusion, and reduce the analysis deviation caused by data errors. Using standardized slope ecological environment data to simulate the erosion-runoff coupling process, it is possible to accurately calculate the soil and water erosion dynamic parameters, identify the key driving factors of soil and water erosion, and predict the soil and water erosion trend based on the dynamic parameters, generate soil and water erosion risk assessment data with accurate spatial distribution and strong timeliness, and provide a scientific basis for the formulation of governance plans. Designing ecological governance plans based on soil and water erosion risk assessment data can formulate differentiated and refined governance measures for the erosion characteristics and ecological needs of different slope environments, and enhance the pertinence and effectiveness of governance. By performing slope vegetation seeding simulation, the ecological restoration effect of different vegetation configurations can be evaluated in advance, the vegetation configuration plan can be optimized, and the vegetation coverage efficiency can be improved; by implementing dynamic regulation of slope drainage facilities, the operation status of drainage facilities can be adjusted according to real-time hydrological monitoring data, the slope water flow path can be optimized, the slope runoff erosion intensity can be reduced, and the soil and water conservation capacity can be improved; by carrying out precision irrigation simulation of slope vegetation, precise irrigation can be implemented according to the vegetation water demand and soil moisture conditions, the efficiency of water resource utilization can be improved, the healthy growth of vegetation can be promoted, and the soil fixation capacity of vegetation can be enhanced; by implementing water conservancy slope ecological monitoring based on initial vegetation coverage data, optimized slope water flow regulation data, and vegetation growth monitoring data, dynamic feedback regulation of ecological governance measures can be realized to ensure that the governance effect meets the ecological restoration expectations. The governance effect evaluation of ecological governance monitoring data and the generation of ecological governance optimization parameters can be carried out to evaluate the effectiveness of governance measures by comparing the soil and water loss conditions, vegetation recovery conditions, and hydrological change trends before and after governance, and the ecological governance plan can be adjusted based on the optimization parameters to ensure that the governance measures always meet the dynamic needs of the slope ecological environment and realize the refinement, intelligence, and sustainability of water conservancy slope ecological governance.

[0006] Preferably, the present invention further provides a system for ecological management of water conservancy slopes based on multi-source sensor data, which is used to execute the above-mentioned method for ecological management of water conservancy slopes based on multi-source sensor data. The system for ecological management of water conservancy slopes based on multi-source sensor data comprises: Multi-source environmental data acquisition module, used to collect meteorological monitoring data, soil hydrological monitoring data, vegetation coverage monitoring data, topographic mapping data and soil erosion monitoring data as original slope ecological environment data; The data standardization preprocessing module is used to perform data pre-registration processing on the original slope ecological environment data to obtain standardized slope ecological environment data; The erosion dynamics simulation and risk assessment module is used to simulate the erosion-runoff coupling process using standardized slope ecological environment data to obtain soil and water erosion dynamics parameters; predict soil and water loss trends based on soil and water erosion dynamics parameters to generate soil and water loss risk assessment data; Ecological governance program planning module, used to design ecological governance programs based on soil and water loss risk assessment data; Dynamic control and collaborative monitoring module, used to perform slope vegetation seeding simulation and record initial vegetation coverage data; implement dynamic control of slope drainage facilities and record optimized slope water flow control data; carry out slope vegetation precision irrigation simulation and record slope vegetation irrigation data; implement water conservancy slope ecological monitoring based on initial vegetation coverage data, optimized slope water flow control data and vegetation growth monitoring data to obtain ecological management monitoring data; The governance effectiveness feedback optimization module is used to evaluate the governance effect of ecological governance monitoring data and generate ecological governance optimization parameters; and optimize the ecological governance plan based on the ecological governance optimization parameters.

[0007] The present invention can comprehensively collect multi-dimensional data on meteorology, soil hydrology, vegetation coverage, topographic mapping and soil erosion, realize all-round monitoring of the slope ecological environment, improve the integrity and representativeness of the data, and provide high-precision data support for subsequent governance. Through data pre-registration processing, the differences in format, time scale and spatial resolution between different data sources are eliminated, the accuracy and consistency of data fusion are improved, and the deviation of governance schemes caused by data errors is reduced. Standardized slope ecological environment data is used to simulate the erosion-runoff coupling process, accurately obtain soil and water erosion dynamic parameters, identify key factors affecting the erosion process, and predict soil and water erosion trends based on dynamic parameters. Generate high-precision soil and water erosion risk assessment data to provide a scientific basis for the formulation of governance schemes. According to the soil and water erosion risk assessment data, a targeted governance scheme is formulated, combined with the actual erosion characteristics of the slope and the needs of ecological restoration, the configuration of governance measures is optimized, and the accuracy and feasibility of governance are enhanced. Through slope vegetation seeding simulation, dynamic regulation of slope drainage facilities and precision irrigation simulation, dynamic optimization of water conservancy slope management measures is achieved, vegetation coverage is increased, slope water flow paths are optimized, soil moisture regulation capacity is enhanced, and real-time feedback regulation is carried out in combination with water conservancy slope ecological monitoring data to ensure that management measures meet ecological restoration needs. Through management effect evaluation, the actual role of ecological management measures is quantified, and management plans are iteratively optimized based on optimization parameters to achieve precision, intelligence and dynamic adaptability of water conservancy slope management, and improve the long-term stability and ecological sustainability of management. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] 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: Figure 1 A schematic diagram of the steps of a method for ecological management of water conservancy slopes based on multi-source sensor data according to the present invention; Figure 2 for Figure 1 Detailed flow diagram of step S1 in FIG. Figure 3 for Figure 1 Detailed flow chart of step S2 in FIG. DETAILED DESCRIPTION

[0009] 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.

[0010] 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.

[0011] 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.

[0012] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for water conservancy slope ecological management based on multi-source sensor data, the method comprising the following steps: Step S1: collecting meteorological monitoring data, soil hydrological monitoring data, vegetation coverage monitoring data, topographic mapping data and soil erosion monitoring data as original slope ecological environment data; Step S2: performing data pre-registration processing on the original slope ecological environment data to obtain standardized slope ecological environment data; Step S3: using standardized slope ecological environment data to simulate the erosion-runoff coupling process, and obtaining soil and water erosion dynamics parameters; based on the soil and water erosion dynamics parameters, predicting the soil and water loss trend, and generating soil and water loss risk assessment data; Step S4: Designing an ecological governance plan based on soil and water loss risk assessment data; Step S5: Execute slope vegetation seeding simulation and record initial vegetation coverage data; implement dynamic regulation of slope drainage facilities and record optimized slope water flow regulation data; carry out slope vegetation precision irrigation simulation and record slope vegetation irrigation data; implement water conservancy slope ecological monitoring based on initial vegetation coverage data, optimized slope water flow regulation data and vegetation growth monitoring data to obtain ecological management monitoring data; Step S6: Evaluate the governance effect of the ecological governance monitoring data and generate ecological governance optimization parameters; optimize the ecological governance plan based on the ecological governance optimization parameters.

[0013] In the embodiment of the present invention, reference Figure 1As shown, it is a schematic diagram of the steps of a method for ecological management of water conservancy slopes based on multi-source sensor data of the present invention. In this example, the method for ecological management of water conservancy slopes based on multi-source sensor data includes the following steps: Step S1: collecting meteorological monitoring data, soil hydrological monitoring data, vegetation coverage monitoring data, topographic mapping data and soil erosion monitoring data as original slope ecological environment data; The embodiment of the present invention utilizes meteorological monitoring equipment, including temperature and humidity sensors, wind speed and direction meters, and precipitation monitors, to continuously collect data on the atmospheric temperature, relative humidity, wind speed, wind direction, and precipitation in the target slope area, and the recording time interval is set to 10 minutes to ensure that short-term changes can be accurately captured; soil hydrological monitoring equipment, including soil moisture sensors, groundwater level monitors, and infiltration rate measuring devices, are used to set up monitoring points at different heights of the slope to obtain soil moisture content, groundwater level, and precipitation infiltration rate data, wherein the buried depths of soil moisture sensors are divided into three layers: 0-10cm, 10-30cm, and 30-50cm, to reflect the moisture distribution characteristics of the soil profile; an unmanned aerial vehicle remote sensing system equipped with a multispectral imager is used to perform high-resolution imaging of the slope vegetation coverage, and aerial imaging is performed at fixed times every day. The remote sensing data were measured to obtain vegetation coverage data at different time periods, and calibrated with the vegetation growth status of the ground measurement points to improve the accuracy of the remote sensing data. A 3D laser scanner and GPS mapping equipment were used to map the slope terrain with high precision, obtain slope, aspect and elevation data, and establish a digital elevation model of the slope. The surveying and mapping data collection interval was set to 15 days to monitor the trend of terrain changes. A soil and water loss monitoring site was set up, and runoff plots, scour ditch monitoring equipment and sediment content sensors were installed to monitor the surface runoff, sediment content and erosion gully morphology of the slope in real time. An automatic sampling device was used to regularly collect eroded sediment samples to analyze their particle composition and source. All collected data were stored through the data acquisition terminal and synchronized to the data processing center using wired and wireless transmission methods as original slope ecological environment data.

[0014] Step S2: performing data pre-registration processing on the original slope ecological environment data to obtain standardized slope ecological environment data; The embodiment of the present invention uses a data preprocessing server to perform data pre-registration processing on the original slope ecological environment data. First, the meteorological monitoring data is time-series aligned, resampled at a uniform time interval (10 minutes), linear interpolation is used to fill in missing data, and mean filtering is used to smooth abnormal fluctuations. The soil hydrological monitoring data is spatially calibrated, and a bilinear interpolation method is used for spatial matching based on the GPS coordinate information of the buried position of each sensor. The data is hierarchically classified according to the measured depth to ensure the consistency of the spatial distribution of the data. The vegetation coverage monitoring data is image-aligned, and an affine transformation method is used to geometrically calibrate the image based on the geographic coordinate information of the remote sensing image. The coordinate data of the ground control points are used for error correction to keep it consistent with the coordinate system of the topographic mapping data. The topographic mapping data are reprojected and uniformly converted into the WGS-84 coordinate system, and the height error of the laser scanning point cloud data is corrected using the adjustment algorithm to ensure the accuracy of the topographic data. The runoff and sediment content of the soil and water loss monitoring data are synchronously calibrated, and the time delay is calculated using the cross-correlation analysis method, and time alignment is performed to ensure the spatiotemporal consistency of the monitoring data. All pre-processed data are stored in the database in a unified format and standardized, including unit conversion, data normalization, and classified storage, to form standardized slope ecological environment data.

[0015] Step S3: using standardized slope ecological environment data to simulate the erosion-runoff coupling process, and obtaining soil and water erosion dynamics parameters; based on the soil and water erosion dynamics parameters, predicting the soil and water loss trend, and generating soil and water loss risk assessment data; The embodiment of the present invention uses a high-performance computing server to simulate the erosion-runoff coupling process of standardized slope ecological environment data. First, the rainfall intensity data in the meteorological monitoring data is statistically segmented in time, the average rainfall is calculated at 10-minute intervals, and the rainfall kinetic energy is calculated in combination with the wind speed data to evaluate the impact of rainfall on slope soil erosion; based on the soil moisture content and permeability coefficient data in the soil hydrological monitoring data, the Green-Ampt infiltration formula is used to calculate the slope infiltration rate, and the dynamic changes of soil moisture at different slope positions are analyzed in combination with the slope gradient data; the elevation point cloud data in the topographic mapping data is used to calculate the slope water flow path using the D8 algorithm, and the slope gradient and slope length are calculated based on the slope gradient and slope length. Calculate the slope flow velocity to determine the runoff confluence process; combine the vegetation root distribution data and vegetation coverage data in the vegetation coverage monitoring data to calculate the vegetation interception of rainfall and the contribution of the root system to enhance the soil erosion resistance, and calculate the erosion sediment yield based on the sediment transport data in the soil and water loss monitoring data, and use the modified universal soil loss equation (RUSLE) to calculate the slope water erosion rate; combine the water flow path simulation results and the erosion sediment yield calculation results, use the momentum conservation equation to analyze the slope erosion gully development trend, and combine the slope runoff flow characteristics to calculate the slope shear stress to obtain the soil and water erosion dynamics parameters; all calculation results are stored in the database, and the soil and water erosion intensity distribution map is output in raster format.

[0016] Step S4: Designing an ecological governance plan based on soil and water loss risk assessment data; The embodiment of the present invention designs an ecological management scheme based on soil and water loss risk assessment data. First, according to the slope erosion intensity distribution information in the soil and water loss risk assessment data, combined with the slope, slope direction and elevation data in the terrain mapping data, the GIS spatial analysis method is used to divide the water conservancy slope management zone, and the management measures are determined according to the erosion types of different zones; for high erosion risk areas, combined with the soil erosion resistance parameters in the soil hydrological monitoring data, deep-rooted slope protection plants are selected, and according to the vegetation adaptability parameters in the vegetation coverage monitoring data, a slope vegetation sowing scheme is formulated, and the vegetation sowing density and sowing position are determined; combined with the slope water flow path analysis results, the drainage facility layout position is determined, the drainage ditch width and depth are calculated according to the slope flow velocity and flow, and the drainage material is selected to form a dynamic control scheme for slope drainage facilities; according to the slope shear stress and slope moisture content changes in the soil and water erosion dynamics parameters, the slope precision irrigation scheme is determined, and the irrigation flow and irrigation cycle are calculated according to the vegetation water demand, and the slope vegetation precision irrigation scheme is formulated; the ecological management scheme data is stored in a geographic database, and a management zoning map and a construction parameter table are generated.

[0017] Step S5: Execute slope vegetation seeding simulation and record initial vegetation coverage data; implement dynamic regulation of slope drainage facilities and record optimized slope water flow regulation data; carry out slope vegetation precision irrigation simulation and record slope vegetation irrigation data; implement water conservancy slope ecological monitoring based on initial vegetation coverage data, optimized slope water flow regulation data and vegetation growth monitoring data to obtain ecological management monitoring data; When the embodiment of the present invention performs slope vegetation seeding simulation, according to the slope vegetation seeding plan in the ecological management plan, unmanned aerial vehicle seeding equipment or ground seeding machinery is used to sow seeds in high erosion risk areas, and the initial vegetation coverage is monitored by a multi-spectral remote sensing instrument, and the initial vegetation coverage data is recorded; when the slope drainage facilities are dynamically controlled, according to the drainage facilities layout plan in the ecological management plan, drainage ditches are laid out in the low-lying areas of the slope, the foot of the slope and the area where water flow converges on the slope, and the width, depth and slope of the drainage ditch are measured by a laser rangefinder and a total station. Drainage facilities are constructed using prefabricated concrete parts or crushed stone cushion layers, and flow sensors and rainfall sensors are installed to monitor the drainage capacity, and the optimized slope water flow regulation is recorded. control data; when conducting precision irrigation simulation of slope vegetation, according to the precision irrigation plan of slope vegetation, use soil moisture sensors, evapotranspiration monitors and meteorological monitoring equipment to obtain soil moisture content and vegetation water demand, control sprinkler equipment or drip irrigation network through intelligent irrigation system to implement precision irrigation, and record slope vegetation irrigation data; when implementing water conservancy slope ecological monitoring based on initial vegetation coverage data, optimized slope water flow regulation data and vegetation growth monitoring data, use multi-spectral remote sensing instruments, drone aerial photography and ground vegetation growth monitoring equipment to regularly collect vegetation coverage, vegetation growth status and slope water flow dynamics information, and combine soil and water loss monitoring data to analyze slope erosion, and finally obtain ecological management monitoring data.

[0018] Step S6: Evaluate the governance effect of the ecological governance monitoring data and generate ecological governance optimization parameters; optimize the ecological governance plan based on the ecological governance optimization parameters.

[0019] When evaluating the governance effect of ecological governance monitoring data, the embodiment of the present invention uses ground-based laser radar, drone aerial photography and remote sensing image data to obtain slope terrain changes, vegetation coverage, water flow path changes and soil moisture dynamic data based on the ecological governance monitoring data. The soil and water loss monitoring equipment is used to obtain changes in erosion gully depth, sediment content and slope sediment transport after rainfall. The relationship between rainfall intensity and soil and water loss is analyzed in combination with meteorological monitoring data, and the changes in soil and water loss before and after governance are compared to generate a governance effect evaluation report. In the process of governance effect evaluation, a hyperspectral imager is used to monitor the vegetation growth status. , analyze the changing trends of vegetation coverage and growth vitality, and evaluate the soil moisture regulation capacity in combination with soil hydrological monitoring data, measure the slope water collection capacity and drainage efficiency of drainage facilities through slope runoff monitoring equipment, and comprehensively evaluate the changes in slope hydrodynamic characteristics; based on the management effect evaluation data, extract ecological management optimization parameters, including optimal vegetation seeding density, slope runoff control threshold, slope water flow diversion path optimization parameters and irrigation cycle adjustment parameters, and combine with the erosion-runoff coupling process simulation results to optimize the slope ecological management plan, adjust the vegetation configuration, slope drainage facility layout and irrigation strategy.

[0020] The present invention can comprehensively obtain the dynamic change information of the slope ecological environment by collecting meteorological monitoring data, soil hydrological monitoring data, vegetation coverage monitoring data, topographic mapping data and soil erosion monitoring data, improve the integrity and representativeness of the data, and provide a reliable basis for subsequent data processing and analysis. The original slope ecological environment data is pre-registered, which can effectively eliminate the format, time scale and spatial resolution differences between different data sources, establish a unified data standard, improve the accuracy and consistency of data fusion, and reduce the analysis deviation caused by data errors. Using standardized slope ecological environment data to simulate the erosion-runoff coupling process, it is possible to accurately calculate the soil and water erosion dynamic parameters, identify the key driving factors of soil and water erosion, and predict the soil and water erosion trend based on the dynamic parameters, generate soil and water erosion risk assessment data with accurate spatial distribution and strong timeliness, and provide a scientific basis for the formulation of governance plans. Designing ecological governance plans based on soil and water erosion risk assessment data can formulate differentiated and refined governance measures for the erosion characteristics and ecological needs of different slope environments, and enhance the pertinence and effectiveness of governance. By performing slope vegetation seeding simulation, the ecological restoration effect of different vegetation configurations can be evaluated in advance, the vegetation configuration plan can be optimized, and the vegetation coverage efficiency can be improved; by implementing dynamic regulation of slope drainage facilities, the operation status of drainage facilities can be adjusted according to real-time hydrological monitoring data, the slope water flow path can be optimized, the slope runoff erosion intensity can be reduced, and the soil and water conservation capacity can be improved; by carrying out precision irrigation simulation of slope vegetation, precise irrigation can be implemented according to the vegetation water demand and soil moisture conditions, the efficiency of water resource utilization can be improved, the healthy growth of vegetation can be promoted, and the soil fixation capacity of vegetation can be enhanced; by implementing water conservancy slope ecological monitoring based on initial vegetation coverage data, optimized slope water flow regulation data, and vegetation growth monitoring data, dynamic feedback regulation of ecological governance measures can be realized to ensure that the governance effect meets the ecological restoration expectations. The governance effect evaluation of ecological governance monitoring data and the generation of ecological governance optimization parameters can be carried out to evaluate the effectiveness of governance measures by comparing the soil and water loss conditions, vegetation recovery conditions, and hydrological change trends before and after governance, and the ecological governance plan can be adjusted based on the optimization parameters to ensure that the governance measures always meet the dynamic needs of the slope ecological environment and realize the refinement, intelligence, and sustainability of water conservancy slope ecological governance.

[0021] Preferably, step S1 comprises the following steps: Step S11: deploying ground meteorological sensors in the water conservancy slope, and using the ground meteorological sensors to collect rainfall, wind speed and air humidity as meteorological monitoring data; Step S12: deploying soil moisture sensors and soil temperature sensors in the water conservancy slope, using the soil moisture sensors to monitor soil moisture, and using the soil temperature sensors to monitor soil temperature, to obtain soil hydrological monitoring data; Step S13: deploying an unmanned aerial vehicle remote sensing system and a hyperspectral imager in the water conservancy slope, using the unmanned aerial vehicle remote sensing system to perform remote sensing monitoring of vegetation coverage, and using the hyperspectral imager to perform vegetation health index monitoring, to obtain vegetation coverage monitoring data; Step S14: deploying LiDAR scanning equipment in the water conservancy slope, and using the LiDAR scanning equipment to perform high-precision scanning of slope, slope length and micro-topographic features to obtain topographic mapping data; Step S15: deploying soil and water loss monitoring stations and rainfall erosion sensing equipment in the water conservancy slope, using the soil and water loss monitoring stations to monitor the sediment content and runoff in real time, and using the rainfall erosion sensing equipment to monitor the erosion rate in real time, to obtain soil and water loss monitoring data; Step S16: Record the meteorological monitoring data, soil hydrological monitoring data, vegetation coverage monitoring data, topographic surveying data and soil erosion monitoring data as original slope ecological environment data.

[0022] As an embodiment of the present invention, refer to Figure 2 FIG. 1 is a schematic diagram of a detailed flow chart of step S1. In the embodiment of the present invention, step S1 includes the following steps: Step S11: deploying ground meteorological sensors in the water conservancy slope, and using the ground meteorological sensors to collect rainfall, wind speed and air humidity as meteorological monitoring data; Step S12: deploying soil moisture sensors and soil temperature sensors in the water conservancy slope, using the soil moisture sensors to monitor soil moisture, and using the soil temperature sensors to monitor soil temperature, to obtain soil hydrological monitoring data; Step S13: deploying an unmanned aerial vehicle remote sensing system and a hyperspectral imager in the water conservancy slope, using the unmanned aerial vehicle remote sensing system to perform remote sensing monitoring of vegetation coverage, and using the hyperspectral imager to perform vegetation health index monitoring, to obtain vegetation coverage monitoring data; Step S14: deploying LiDAR scanning equipment in the water conservancy slope, and using the LiDAR scanning equipment to perform high-precision scanning of slope, slope length and micro-topographic features to obtain topographic mapping data; Step S15: deploying soil and water loss monitoring stations and rainfall erosion sensing equipment in the water conservancy slope, using the soil and water loss monitoring stations to monitor the sediment content and runoff in real time, and using the rainfall erosion sensing equipment to monitor the erosion rate in real time, to obtain soil and water loss monitoring data; Step S16: Record the meteorological monitoring data, soil hydrological monitoring data, vegetation coverage monitoring data, topographic surveying data and soil erosion monitoring data as original slope ecological environment data.

[0023] The embodiment of the present invention adopts multi-source sensors to perform comprehensive monitoring of the ecological status of water conservancy slopes. First, five ground meteorological sensors were fixedly installed at the four corners and the middle of the water conservancy slope. The sensor height was 2 meters from the ground, the collection frequency was 10 minutes / time, and the accuracy requirements were rainfall ±0.5mm, wind speed ±0.3m / s, and relative humidity ±3%RH. All data were transmitted to the data collector through the RS-485 interface to form meteorological monitoring data; secondly, a monitoring point was arranged every 20 meters × 20 meters along the slope, and three soil moisture sensors were buried at each monitoring point at depths of 10cm, 30cm, and 50cm. At the same time, three soil temperature sensors were buried at the same depth. The collection frequency was 30 minutes / time. All data were transmitted to the server in real time through the GPRS module to form soil hydrological monitoring data; thirdly, a multispectral camera was used to carry out a drone, the flight altitude was set to 120 meters, the flight speed was 4m / s, the route overlap was 75%, and a full coverage flight was performed every 7 days. The collected images were processed by software to calculate the NDVI value to construct a vegetation coverage distribution map. At the same time, a hyperspectral imager was used to monitor the vegetation health index in 5 fixed sample plots. The spectral range is 400-1000nm and the resolution is 5nm. The two data are fused to form vegetation coverage monitoring data. Then, 5 fixed scanning stations are set up around the slope using LiDAR scanning equipment. The scanning resolution is set to 0.05° and the point cloud density is not less than 1000 points / m². A 3D model is generated after point cloud registration. The slope (accuracy ±0.5°), slope length (accuracy ±0.1m) and micro-topography feature data are extracted to form topographic mapping data. Finally, 3 HDPE runoff plots (each with an area of ​​5m²) are set at the foot of the slope. Each plot is equipped with a rain gauge and a 7524 automatic sampler. The sampling interval is once every 15 minutes after the rainfall event is triggered. At the same time, 4 erosion needles are installed in each runoff plot. The measuring needles are inserted vertically into the soil surface and exposed 10mm above the ground. The erosion rate is calculated by measuring the change in the exposed height of the measuring needle after each rainfall. The two data are combined to form soil and water loss monitoring data. All monitoring data are transmitted to the data center through the 4G network and synchronized with a unified time tag to form the original slope ecological environment data.

[0024] The present invention deploys ground meteorological sensors to accurately obtain rainfall, wind speed and air humidity, thereby improving the timeliness and accuracy of meteorological monitoring data. The combination of soil moisture sensors and soil temperature sensors ensures the refinement of soil hydrological monitoring data and provides a basis for water regulation. The drone remote sensing system and the hyperspectral imager jointly monitor vegetation coverage and health index to achieve large-scale high-precision vegetation monitoring. LiDAR scanning equipment accurately measures slope, slope length and micro-topography features to improve the accuracy of topographic mapping data. Soil and water loss monitoring stations and rainfall erosion sensing equipment monitor sediment content, runoff and erosion rate in real time, providing accurate data support for soil and water loss assessment, and ensuring the scientificity and feasibility of ecological governance plans.

[0025] Preferably, step S2 comprises the following steps: Step S21: Time-align the slope ecological environment data to obtain time-consistent ecological environment data; Step S22: unifying the time-consistent ecological environment data into a standard data format to obtain formatted ecological environment data; Step S23: removing outliers from the formatted ecological environment data to obtain denoised ecological environment data, wherein the outliers are specifically abnormal data caused by sensor errors, signal interference, and acquisition failures; Step S24: completing missing data of the denoised ecological environment data to obtain complete ecological environment data; Step S25: normalize and standardize the complete ecological environment data to obtain standardized slope ecological environment data.

[0026] As an embodiment of the present invention, refer to Figure 3 FIG. 2 is a schematic diagram of a detailed flow chart of step S2. In the embodiment of the present invention, step S2 includes the following steps: Step S21: Time-align the slope ecological environment data to obtain time-consistent ecological environment data; Step S22: unifying the time-consistent ecological environment data into a standard data format to obtain formatted ecological environment data; Step S23: removing outliers from the formatted ecological environment data to obtain denoised ecological environment data, wherein the outliers are specifically abnormal data caused by sensor errors, signal interference, and acquisition failures; Step S24: completing missing data of the denoised ecological environment data to obtain complete ecological environment data; Step S25: normalize and standardize the complete ecological environment data to obtain standardized slope ecological environment data.

[0027] The embodiment of the present invention first pre-processes the slope ecological environment data collected from multiple sources, and uniformly interpolates the sensor data with different collection frequencies (meteorological data 10 minutes / time, soil data 30 minutes / time, vegetation data 7 days / time, terrain data 15 days / time, and soil erosion data recorded according to rainfall events) into a time series with a 30-minute interval through the TimeSync program running on the Linux server, so as to align the data timestamps and generate time-consistent ecological environment data; then, the DataFormat conversion tool is used to convert the proprietary formats output by various sensors (meteorological sensor XML format, soil sensor CSV format, image data TIFF format, point cloud data LAS format, soil erosion data SQL format) are all converted into a unified HDF5 format. The data of each monitoring point is organized into a data group to form formatted ecological environment data. Then the three sigma principle is used to remove outliers, that is, when the measured value exceeds the range of ±3 times the standard deviation of the mean value, it is judged as an outlier. The specific implementation is to calculate the mean and standard deviation of each monitoring indicator with a sliding window of 5 days, and mark the data points in the meteorological data with instantaneous wind speed exceeding 30m / s, rainfall mutation exceeding 50mm / h, and humidity jump exceeding 30% / h. The data points with water content changes of more than 40% within 24 hours in the soil moisture sensor data are recorded and deleted to form denoised ecological environment data; then, for the data gaps after the outliers are removed, the linear interpolation method is used to fill the continuous missing data segments of less than 3 hours, the spline interpolation method is used to fill the missing data segments of 3-12 hours, and the spatial correlation of adjacent monitoring points is used to fill the missing data of more than 12 hours. The specific operation is to calculate the Pearson correlation coefficient between the target monitoring point and the 5 nearest monitoring points around it, and select the weighted average of the data of the 3 points with the highest correlation coefficient (the weight is the correlation coefficient value) as the missing The supplementary value of the data is used to obtain the complete ecological environment data; finally, the complete data is normalized. The specific method is to apply the formula (x-min) / (max-min) to each data sequence x to normalize the value to the interval [0,1], and then adjust each data to a standard normal distribution with a mean of 0 and a standard deviation of 1 through the z-score standardization processing formula (x-mean) / std. The mean and standard deviation of each monitoring indicator are calculated by statistically analyzing the historical data of the whole year. The normalization and standardization parameters of each indicator are recorded in the metadata file for subsequent reverse conversion, and finally standardized slope ecological environment data are formed.

[0028] The present invention ensures the synchronization of multi-source ecological and environmental data through time alignment, improves the consistency of data time series, and avoids the influence of time deviation on the analysis results. Format unification converts the formats of different data sources to improve data compatibility and processing efficiency. Outlier elimination effectively removes abnormal data caused by sensor errors, signal interference and acquisition failures, and improves the accuracy and reliability of data. Missing data completion ensures data integrity and avoids the impact of missing data on subsequent analysis. Normalization and standardization processing eliminates differences in different data scales and units, ensures balanced data distribution, improves the accuracy of data analysis and modeling in the process of water conservancy slope ecological management, and provides high-quality data support for scientific management decisions.

[0029] Preferably, the erosion-runoff coupling process simulation using standardized slope ecological environment data in step S3 includes: The rainfall erosivity factor is extracted from the standardized slope ecological environment data to obtain the spatiotemporal distribution factor, where the spatiotemporal distribution factor is positive and ranges from 0 to 20000 MJ·mm / (ha·h·a); The soil texture characteristics were parameterized based on the standardized slope ecological environment data to obtain the soil erodibility factor, which was within the range of 0.01-0.5 t·ha·h / (ha·MJ·mm); When the soil erodibility factor is less than 0.01 t·ha·h / (ha·MJ·mm), it indicates that the soil is almost inerodible rock; when the soil erodibility factor is greater than 0.5 t·ha·h / (ha·MJ·mm), it indicates that the soil structure is loose and unreasonable. The slope length and slope factor of terrain characteristics are calculated using standardized slope ecological environment data to obtain the slope length and slope factor; The vegetation coverage monitoring data in the standardized slope ecological environment data is converted into vegetation protection factors to obtain vegetation coverage and management factors; The vegetation cover and management factor is a ratio value, which indicates the mitigation effect of vegetation cover on erosion and ranges from 0 to 1; When the vegetation cover and management factor is equal to 0, it indicates an ideal vegetation cover that completely suppresses erosion, and when the vegetation cover and management factor is equal to 1, it indicates that the bare surface is unprotected; Based on the standardized slope ecological environment data, the water flow direction in the water flow convergence area is calculated to obtain the slope water flow path network; Based on the slope water flow path network, the comprehensive erosion model was parameterized using the spatiotemporal distribution factor, soil erodibility factor, slope length and slope gradient factor, and vegetation coverage and management factor to obtain a soil and water erosion prediction model. Based on the soil and water erosion prediction model, the erosion rate of each area of ​​the slope is calculated to obtain spatially differentiated erosion rate data within the range of 0-500t / (ha·a); Obtain measured erosion data, compare and analyze the measured erosion data with spatially differentiated erosion rate data, use the comparative analysis results to calibrate the soil and water erosion prediction model, and generate soil and water erosion dynamics parameters.

[0030] The embodiment of the present invention first uses the rainfall records in the standardized slope ecological environment data to calculate the rainfall erosivity factor R through the EI30 method, that is, for each rainfall event, the product of the rainfall kinetic energy per unit time and the maximum rainfall intensity in 30 minutes is accumulated. The specific calculation formula is R=∑(E·I30) / 100, where E is the kinetic energy per unit rainfall (MJ / ha·mm), I30 is the maximum rainfall intensity in 30 minutes (mm / h), and the calculation result is a positive value and is controlled between 0-20000. MJ·mm / (ha·h·a). If the calculated value exceeds the upper limit, the upper limit of 20000 MJ·mm / (ha·h·a) is taken to finally form the spatiotemporal distribution factor. Then, the soil texture data is extracted from the standardized slope ecological environment data, and the Wischmeier formula K=2.8×10^-7×M^1.14×(12-OM)+4.3×10^-3×(s-2)+3.3×10^-3×(p-3) is used to calculate the soil erodibility factor, where M is the particle size parameter (% silt + % very fine sand) × (100-% clay), OM is the organic matter content (%), s is the soil structure level (1-4), p is the soil permeability level (1-6), and the calculated value is strictly controlled in the range of 0.01-0.5 t·ha·h / (ha·MJ·mm). A value lower than 0.01 t·ha·h / (ha·MJ·mm) indicates an almost inerodible rock state, and a value higher than 0.5 t·ha·h / (ha·MJ·mm) indicates a loose and Unreasonable soil structure. For both boundary conditions, soil properties were reconfirmed by manual on-site sampling. Then, based on the topographic mapping data obtained by LiDAR, the slope length and slope factor LS were calculated using the unit grid method. The formula is LS=(λ / 22.13)^m×(65.41sin²θ+4.56sinθ+0.065), where λ is the slope length (m), θ is the slope (rad), and m is the slope length index (depending on the slope, usually 0.2-0.5). The calculated result is limited to 0.1-50, if the calculated value exceeds the limited range, take the critical value; then use the NDVI value in the vegetation cover monitoring data to calculate the vegetation cover and management factor through the formula C=exp[-α×NDVI / (β-NDVI)], where α and β are fitting parameters (determined based on the field verification sample data, generally α=2, β=1), and the calculation result must be in the range of 0-1, 0 represents the ideal vegetation cover that completely suppresses erosion, and 1 represents the bare surface without protection; then use the high-precision DEM data generated by LiDAR, The D8 algorithm is used to calculate the flow direction of each grid cell, that is, to compare the elevation difference between the central grid and the surrounding 8 grids to determine the direction of the steepest drop of the water flow. The flow direction value must be one of 1, 2, 4, 8, 16, 32, 64 or 128. Based on the determined flow direction value, a water flow accumulation distribution map is constructed to form a slope water flow path network. After that, the above-mentioned factor data are resampled and aligned according to 10m×10m grids, and substituted into the universal soil loss equation USLE: A=R×K×LS×C×P to calculate the erosion model of each grid cell. A, where P is the soil and water conservation measure factor (initial setting is 1), forms a soil and water erosion prediction model; the model calculates the erosion rate of each area of ​​the slope under three typical hydrological conditions: dry season, normal water season and flood season, and obtains spatially differentiated erosion rate data. The calculation result range is strictly controlled within 0-500t / (ha·a). If it exceeds 500t / (ha·a), it is recorded as 500t / (ha·a) and marked as an extremely high-risk area; finally, the measured erosion data is directly obtained from the soil and water loss monitoring station and compared with the prediction model. The results of the calculations were compared, and the root mean square error (RMSE) and the Nash efficiency coefficient (NSE) were calculated. The RMSE was required to be less than 50% of the measured erosion average value, and the NSE was required to be greater than 0.6. The weight coefficients of each factor were optimized through multiple iterations (calibration coefficients γR, γK, γLS, and γC were introduced for the four factors R, K, LS, and C, respectively, with a value range of 0.8-1.2), until the model prediction values ​​and the measured values ​​reached the required consistency, and finally the calibrated soil and water erosion dynamics parameters were generated, including the calibration coefficients and spatial distribution characteristics of each factor.

[0031] The present invention accurately reflects the impact of precipitation on soil erosion through rainfall erosivity factor extraction, and enhances the adaptability of the model to the temporal and spatial variability of rainfall. The calculation of soil erodibility factor clarifies the anti-erosion capacity of different soil types and provides a basis for reasonable control measures. The calculation of slope length and slope factor quantifies the impact of terrain on erosion and improves the accuracy of terrain adaptability analysis. The conversion of vegetation coverage and management factors reflects the protective effect of vegetation on erosion and provides support for the optimal layout of vegetation. The construction of the slope water flow path network accurately depicts the characteristics of water flow convergence and improves the reliability of hydrodynamic analysis. A soil and water erosion prediction model is constructed based on multiple factors to improve the accuracy of erosion rate prediction. The model is calibrated by measured data, and the erosion dynamics parameters are optimized to ensure the scientificity and accuracy of soil and water loss assessment.

[0032] It is particularly important that the calculation of the water flow direction in the water flow convergence area based on the standardized slope ecological environment data includes: Extract slope elevation information from standardized slope ecological environment data; Use slope elevation information to build a digital elevation model (DEM) and generate slope terrain data; The D8 flow direction algorithm is used to analyze the flow direction of the slope terrain data, calculate the flow direction of each unit on the slope, and obtain the preliminary flow direction distribution; Calculate flow accumulation based on preliminary water flow direction distribution and generate a slope catchment area distribution map; Conduct water flow path analysis on the slope water catchment area distribution map, extract the slope water flow convergence skeleton line, and form a preliminary slope water flow path network; The preliminary slope water flow path network is optimized, isolated flow units are eliminated, and the water flow path is smoothed to obtain the final slope water flow path network.

[0033] The embodiment of the present invention extracts slope elevation information from standardized slope ecological environment data, uses high-precision laser radar (LiDAR) point cloud data and global navigation satellite system (GNSS) measurement data for interpolation calculation, generates elevation grid data with a resolution of 0.5m×0.5m, and performs error correction to ensure that the elevation accuracy reaches ±0.1m; based on the corrected elevation grid data, an inverse distance weighted interpolation (IDW) method is used to construct a digital elevation model (DEM), generate slope terrain data, and use the second-order finite difference method to calculate the slope gradient and slope direction to ensure the complete expression of the slope terrain characteristics; the slope terrain data is analyzed for flow direction, and the D8 water flow direction algorithm is used to calculate the flow direction according to the elevation value of each grid cell, determine the flow path of the water flow along the steepest descent direction of the slope, and store the water flow in integer coding. Direction matrix, where the coding value range is 1-8, corresponding to eight main directions respectively; flow accumulation calculation is performed based on the water flow direction matrix. According to the unidirectional flow principle, the number of confluence units is accumulated along the flow direction starting from the highest point of the slope to generate a slope catchment area distribution map, and the threshold is set to 100 confluence units to screen the main catchment area and eliminate scattered invalid water flow units; water flow path analysis is performed on the slope catchment area distribution map, and the main water flow path is extracted using the connected domain segmentation method. The topological structure is constructed based on the connectivity of the water flow path to form a preliminary slope water flow path network; the preliminary slope water flow path network is optimized, and the isolated flow direction units with an area of ​​less than 10m² are eliminated using the morphological opening operation method. At the same time, the water flow path is smoothed using the bilinear interpolation method to reduce the path discreteness, and finally a coherent and stable slope water flow path network is generated.

[0034] The present invention accurately depicts the terrain characteristics of the slope surface through the extraction of slope elevation information and the construction of a digital elevation model, providing a basis for subsequent water flow analysis. The D8 water flow direction algorithm ensures the accuracy of the slope water flow path calculation and improves the accuracy of the flow direction analysis. The flow accumulation calculation and the generation of the slope catchment area distribution map accurately reflect the convergence of slope water flow and provide data support for soil and water loss risk assessment. The water flow path analysis extracts the water flow convergence skeleton line, clarifies the main convergence path of the slope water flow, and improves the integrity of the hydrological analysis. The water flow path network is optimized, isolated flow units are eliminated, and the water flow path is smoothed to improve the stability and accuracy of the slope hydrodynamic simulation, providing a scientific basis for the ecological management of water conservancy slopes.

[0035] Preferably, the prediction of soil and water loss trend based on soil and water erosion dynamics parameters in step S3 includes: Multi-dimensional characteristic classification of soil and water erosion dynamic parameters was performed to obtain the spatiotemporal distribution data of erosion intensity; Based on the standardized slope ecological environment data and the spatiotemporal distribution data of erosion intensity, multi-scale dynamic modeling of erosion-runoff-terrain feedback was carried out to obtain the slope erosion evolution model; Obtain historical meteorological data, and use the historical meteorological data to perform spatiotemporal extrapolation prediction of slope erosion intensity on the slope erosion evolution model to obtain a map of erosion intensity changes; Perform threshold segmentation on the erosion intensity change map to obtain erosion risk level zoning data; Based on the vegetation coverage monitoring data and erosion risk level zoning data in the standardized slope ecological environment data, the interactive response analysis between vegetation protection and hydrological erosion mechanism was carried out to generate a soil and water loss risk association matrix; The soil and water loss risk association matrix is ​​superimposed in time and space, and the risk priority of each slope area is ranked to generate soil and water loss risk assessment data.

[0036] The embodiment of the present invention first classifies the soil and water erosion dynamics parameters according to the three dimensions of spatial position, erosion depth and time scale. Specifically, the K-means clustering algorithm is used to divide the slope area into five types of erosion sensitive areas, each of which contains spatially continuous grid units. The clustering characteristics include calibrated R factor, K factor, LS factor, C factor and measured erosion rate. The clustering results are further subdivided into 15 spatiotemporal erosion types according to seasonal changes (dry season, normal water season, flood season) to form spatiotemporal distribution data of erosion intensity; then, combined with the elevation, slope, soil properties and vegetation distribution data in the standardized slope ecological environment data, the sediment transport continuity equation ∂h / ∂t+∂(uh) / ∂x+∂(vh) / ∂y=iD is used to construct an erosion dynamics model. , where h is the water depth, u and v are the flow velocities in the x and y directions, i is the rainfall infiltration difference, D is the erosion-sedimentation flux, the erosion flux calculation formula is D=Dc(τ-τc), the sedimentation flux calculation formula is D=ω(Tc-T), τ is the shear stress, τc is the critical shear stress, ω is the sedimentation velocity, T is the sediment transport capacity, and Tc is the actual sediment transport volume. All parameters are extracted from the aforementioned spatiotemporal distribution data of erosion intensity. The spatial resolution is set to 5m×5m, and the time step is set to 10 minutes to form a slope erosion evolution model. Then, the historical meteorological data of the past 10 years, including daily rainfall, rainfall intensity, and rainstorm frequency, are extracted from the meteorological database. 100 sets of rainfall scenarios for the next 5 years are randomly generated by the Monte Carlo method, and these rainfall scenarios are input into the slope erosion evolution model. The surface erosion evolution model is used to simulate and calculate the erosion development process under each scenario, and the probability distribution of different degrees of erosion in each grid unit in the next five years is obtained by superposition calculation, forming an erosion intensity change map; then, four risk level thresholds are set for the erosion intensity change map according to the expected erosion rate: low risk (<50t / (ha·a)), medium risk (50-150t / (ha·a)), high risk (150-300t / (ha·a)), and extremely high risk (>300t / (ha·a)), and threshold segmentation is performed to divide the slope area into four risk level areas to generate erosion risk level zoning data; then, the vegetation type, coverage, root distribution depth in the vegetation coverage monitoring data are correlated with the erosion risk level zoning The data were spatially superimposed and analyzed, and a 10m×10m analysis unit was established. The correlation coefficient between vegetation coverage and erosion risk was calculated for each unit, and a 4×5 correlation matrix (4 risk levels×5 main vegetation types) was constructed. Each element value in the matrix represents the protection efficiency index (0-100) of a specific vegetation type for a specific risk level area. The index was obtained through regression analysis of measured data to form a soil and water loss risk correlation matrix. Finally, the soil and water loss risk correlation matrix was weighted and superimposed according to the two dimensions of time (dry season, normal water season, flood season) and space (top of slope, middle of slope, foot of slope), with the weights being time dimension (0.6 for flood season, 0.3 for normal water season, 0.1 for dry season) and space dimension (0.4 for top of slope, 0.4 for middle of slope, 0.4 for foot of slope).2) Calculate the comprehensive risk index and divide the slope area into four priority management areas based on the comprehensive risk index: emergency management area (risk index>80), priority management area (risk index 60-80), secondary priority management area (risk index 40-60), general management area (risk index <40). Each management area contains a group of spatially adjacent grid cells to form soil and water loss risk assessment data.

[0037] The present invention improves the ability to finely express the spatiotemporal distribution data of erosion intensity through multi-dimensional feature classification of soil and water erosion dynamic parameters. The multi-scale dynamic modeling of erosion-runoff-terrain feedback enhances the accuracy of the simulation of the slope erosion evolution process. The historical meteorological data is used to perform spatiotemporal extrapolation prediction of the slope erosion evolution model to improve the scientific nature of the deduction of the erosion intensity change trend. The threshold segmentation process ensures the rationality and operability of the erosion risk level zoning. Based on the interactive analysis of vegetation coverage monitoring data and erosion risk level data, the impact of vegetation protection on hydrological erosion is accurately characterized. The spatiotemporal superposition operation of the risk association matrix optimizes the spatial accuracy of soil and water loss risk assessment data and provides a scientific basis for the governance plan.

[0038] It is particularly important that the interactive response analysis of vegetation protection and hydrological erosion mechanism based on the vegetation coverage monitoring data and erosion risk level zoning data in the standardized slope ecological environment data includes: Extract vegetation coverage monitoring data from standardized slope ecological environment data; The vegetation coverage monitoring data was calculated using the Normalized Difference Vegetation Index (NDVI) formula, and vegetation coverage raster data was generated with a resolution of 0.01. Classify the vegetation coverage raster data, set the vegetation coverage threshold, and generate a vegetation coverage type distribution map; The classification standards of the classification process are specifically bare land (NDVI less than 0.2), low-cover vegetation (NDVI less than 0.4 and greater than or equal to 0.2), medium-cover vegetation (NDVI less than 0.6 and greater than or equal to 0.4), and high-cover vegetation (NDVI greater than or equal to 0.6); Based on the erosion risk level zoning data, the threshold of the erosion intensity classification standard is set to generate an erosion risk zoning map; The erosion risk levels are low risk (0-50 t / (ha·a)), medium risk (50-150 t / (ha·a)), high risk (150-300 t / (ha·a)), and very high risk (greater than 300 t / (ha·a)). Perform spatial superposition operations on the vegetation cover type distribution map and the erosion risk zoning map, calculate the spatial proportion of each vegetation type in each erosion risk level area, and generate a vegetation cover-erosion intensity interaction matrix; The statistical regression analysis method was used to calculate the influence weight of vegetation coverage on erosion risk changes, and a multivariate correlation model was constructed to obtain the coupling relationship between vegetation protection and hydrological erosion mechanism. Based on the coupling relationship, the analytic hierarchy process (AHP) was used to normalize the weights of various influencing factors, calculate the comprehensive impact index of different vegetation types on each erosion risk level, and generate a soil and water loss risk association matrix.

[0039] The embodiment of the present invention extracts vegetation coverage monitoring data from standardized slope ecological environment data, uses multispectral remote sensing images to obtain red light band (Red) and near infrared band (NIR) reflectance data, and uses the normalized difference vegetation index (NDVI) formula to calculate the vegetation coverage index of each grid unit, wherein the NDVI calculation formula is NDVI = (NIR - Red) / (NIR + Red), and the calculated NDVI value is used to generate vegetation coverage raster data at a resolution of 0.01, and the invalid data area is interpolated and supplemented to ensure data integrity; the vegetation coverage raster data is classified and processed, and the vegetation coverage threshold is set, and the area with NDVI value less than 0.2 is marked as bare land, the area with NDVI value between 0.2 and 0.4 is marked as low coverage vegetation, the area with NDVI value between 0.4 and 0.6 is marked as medium coverage vegetation, and the area with NDVI value greater than or equal to 0.6 is marked as high coverage vegetation, and finally the vegetation coverage is generated. The coverage type distribution map was prepared, and the classification results were vectorized for subsequent spatial analysis and calculation. Based on the erosion risk level zoning data, the erosion intensity classification standard was set, and the areas with erosion rates between 0 and 50 t / (ha·a) were designated as low-risk areas, the areas with erosion rates between 50 and 150 t / (ha·a) were designated as medium-risk areas, the areas with erosion rates between 150 and 300 t / (ha·a) were designated as high-risk areas, and the areas with erosion rates greater than 300 t / (ha·a) were designated as extremely high-risk areas. The spatial interpolation method was used to generate the erosion risk classification system. The vegetation coverage type distribution map and the erosion risk zoning map were spatially superimposed, and the spatial proportion of different vegetation types in each erosion risk level area was calculated based on the zoning statistical method. The calculation results were stored in the form of a matrix to generate a vegetation coverage-erosion intensity interaction matrix, in which the matrix rows represent different vegetation coverage types, the columns represent different erosion risk levels, and the value of each matrix unit represents the spatial proportion of the corresponding vegetation type in the risk level area. The statistical regression analysis method was used to calculate the influence weight of vegetation coverage on erosion risk changes based on the interaction matrix data, and the multiple linear regression method was used to analyze the quantitative relationship between vegetation coverage, slope, soil erodibility environmental variables and erosion risk. A multivariate correlation model was constructed based on the regression coefficient to describe the coupling relationship between vegetation protection and hydrological erosion mechanism. Based on the coupling relationship, the hierarchical analysis method (AHP) was used to calculate the influence weight of each environmental factor, and a judgment matrix was constructed. The weight calculation results were verified using the consistency test (CR≤0.1). Subsequently, the comprehensive influence index of different vegetation types on each erosion risk level was calculated based on the normalization process, and finally a soil and water loss risk association matrix was generated.

[0040] The present invention achieves accurate quantification of coverage of different vegetation types and improves the recognition accuracy of vegetation spatial distribution by extracting vegetation coverage monitoring data and calculating the normalized difference vegetation index (NDVI). The vegetation coverage threshold is set and classified to ensure the scientificity and operability of the vegetation type distribution map. The erosion intensity classification standard is set according to the erosion risk level data, the erosion risk zoning is optimized, and the accuracy of risk assessment is improved. The spatial superposition operation constructs the vegetation coverage-erosion intensity interaction matrix to intuitively reflect the impact of different vegetation types on erosion risk. Through statistical regression analysis and multivariate correlation modeling, the mechanism of action of vegetation protection on hydrological erosion is quantified. The analytic hierarchy process (AHP) normalizes the weights of influencing factors to improve the comprehensive assessment accuracy of different vegetation types for each erosion risk level and optimize the soil and water loss risk management strategy.

[0041] Preferably, step S4 comprises the following steps: Step S41: Analyze the vegetation adaptability, soil stability and climate conditions of the slope area based on the soil and water loss risk assessment data and the preset ecological restoration database to generate vegetation restoration parameter data; Step S42: The vegetation adaptability analysis is specifically that the vegetation adaptability index is limited to a range of 0.6-1. When the vegetation adaptability index is less than 0.6, it indicates that the vegetation has a weak ability to survive under the environmental conditions. Step S43: Optimizing suitable vegetation types and planting density based on vegetation restoration parameter data to generate an optimal vegetation configuration plan; Step S44: the planting density in the optimal vegetation configuration scheme is 1000-3000 plants / m² for herbaceous plants, 30-100 plants / 100m² for shrubs, and 50-150 plants / mu for trees; Step S45: optimizing the layout position and drainage capacity of slope drainage facilities based on the soil and water loss risk assessment data and the slope water flow path network, and generating an optimized design plan for drainage facilities; Step S46: optimizing the slope irrigation strategy, irrigation frequency and water distribution based on the soil and water loss risk assessment data to obtain a precise vegetation irrigation plan; Step S47: Integrate the optimal vegetation configuration plan, the drainage facility optimization design plan and the vegetation precision irrigation plan to obtain a comprehensive ecological management plan.

[0042] The embodiment of the present invention uses a soil erosion model to calculate the soil and water loss risk index, and performs matching analysis based on the collected slope soil pH value, organic matter content, porosity parameters and drought resistance, root development degree, and growth rate indicators in the established vegetation characteristic database to calculate the vegetation adaptability index. When the vegetation adaptability index value is between 0.6 and 1, it is selected as suitable vegetation for the slope. Then, a nonlinear programming algorithm is used to construct a vegetation configuration optimization model, and the objective function is to maximize the slope stability to determine the optimal vegetation configuration scheme with a herbaceous plant planting density of 2500 plants / m², a shrub planting density of 75 plants / 100m², and a tree planting density of 100 plants / mu. Subsequently, the slope terrain is analyzed through a digital elevation model, and the D8 algorithm is used to identify the slope water flow path network, and intercepting ditches are arranged in the runoff collection area. According to the calculation, The cross-sectional dimensions of the intercepting ditch are determined to be 0.5 meters wide and 0.4 meters deep based on the runoff volume. Drainage ditches are set at the slope corners with a drainage capacity of 25 liters per second. Then, a soil moisture dynamic model is established based on the slope soil moisture sensor data. When the soil moisture content is lower than 25%, the irrigation system is started. The irrigation water volume in the herbaceous plant area is 5 liters per square meter, the irrigation water volume in the shrub area is 8 liters per square meter, and the irrigation water volume in the tree area is 15 liters per square meter. The irrigation frequency is adjusted according to the season to once every three days in summer and once every seven days in winter. Finally, the optimal vegetation configuration plan, drainage facility optimization design plan and vegetation precision irrigation plan are integrated through spatial overlay analysis technology to construct a comprehensive ecological management information system including vegetation type spatial distribution layer, drainage facility layout layer and irrigation system control parameter layer, forming a complete comprehensive ecological management plan for water conservancy slopes.

[0043] The present invention combines soil and water loss risk assessment data with an ecological restoration database to accurately analyze the vegetation adaptability, soil stability and climatic conditions of the slope area, thereby improving the pertinence of vegetation restoration. Set the vegetation adaptability index threshold, optimize vegetation types and planting density, and ensure the scientific rationality of vegetation configuration. According to the soil and water loss risk assessment and the slope water flow path network, optimize the layout location and drainage capacity of drainage facilities, improve the efficiency of water flow regulation, and reduce the risk of erosion. Optimize the slope irrigation strategy, irrigation frequency and water distribution to achieve precision irrigation and improve the efficiency of water resource utilization. Integrate the optimal vegetation configuration, drainage facility optimization and precision irrigation plan to form a comprehensive ecological management plan to improve the stability, sustainability and adaptability of slope management.

[0044] Preferably, the performing of slope vegetation seeding simulation and recording of initial vegetation coverage data in step S5 includes: Based on the comprehensive ecological management plan, the slope vegetation restoration area is calibrated to obtain the vegetation restoration calibration data; Based on the vegetation restoration calibration data, the sowing range, density and optimal growth area of ​​each vegetation type are planned to obtain vegetation sowing planning data; Based on the vegetation seeding planning data, the vegetation seeding simulation is carried out on the slope to generate the initial vegetation coverage data.

[0045] The embodiment of the present invention first uses an RTK-GPS surveying instrument to perform high-precision coordinate measurement of the slope area, determines the specific size parameters of the slope surface by a total station, and constructs a digital surface model of the slope in combination with the three-dimensional point cloud data obtained by the oblique photogrammetry of the drone, and divides the slope surface into 10m×10m grid units. In each grid unit, the vegetation restoration level is evaluated according to the physical and chemical properties of the soil, the slope, and the light conditions. The evaluation levels are divided into Level I (vegetation coverage target is greater than 85%), Level II (vegetation coverage target is 70%-85%), and Level III (vegetation coverage target is 50%-70%), and the vegetation restoration calibration data is generated; then, based on the vegetation restoration calibration data, the slope is divided into three areas: upper, middle, and lower. The upper area (within 2 meters of the top of the slope) is planned to be planted with trees with well-developed root systems, with a planting density of 120 plants / mu, and the middle area is planned to be planted with a mixture of shrubs and herbs, with a shrub planting density of 85 plants / 100m², and the herbaceous plants are planned to be planted according to the planting density of 100 plants / 100m². The strip distribution is 5 meters wide, with a planting density of 2,800 plants / m². High-density herbaceous plants are planned in the lower area (within 3 meters of the slope foot), with a planting density of 3,000 plants / m². The optimal growth area of ​​each vegetation is determined by slope microtopography analysis to form vegetation seeding planning data. Finally, based on the vegetation seeding planning data, a slope runoff simulation system is established using the principles of computational fluid dynamics. The seed loss rate under different rainfall intensities (light rain 15mm / h, moderate rain 25mm / h, heavy rain 40mm / h) is simulated using the discrete element method. It is calculated that the burial depth of tree seeds is 3-5cm, the burial depth of shrub seeds is 2-3cm, the covering thickness of herbaceous plant seeds is 0.5-1cm, and the seed germination time is set to 7-15 days. The initial growth state of vegetation in each grid unit is determined by the Monte Carlo sampling method, and the initial vegetation coverage data containing the number of vegetation plants, coverage rate, and growth height prediction values ​​in each grid are generated.

[0046] The present invention determines the slope vegetation restoration area through a comprehensive ecological management plan, accurately calibrates the vegetation restoration range, and improves the targeted management. The vegetation restoration calibration data is used to optimize the sowing range, density and optimal growth area of ​​each vegetation type to ensure the scientific nature and ecological adaptability of the vegetation configuration. The slope vegetation sowing simulation is performed based on the vegetation sowing planning data to generate initial vegetation coverage data, providing basic data support for subsequent vegetation growth monitoring and ecological management optimization, and improving the slope vegetation restoration effect and ecological stability.

[0047] Preferably, the step S6 of evaluating the ecological governance monitoring data and generating ecological governance optimization parameters includes: Conduct spatiotemporal analysis of vegetation cover changes on ecological governance monitoring data to obtain vegetation recovery rate assessment data; Based on the vegetation recovery rate assessment data, the growth effects of various vegetation types were compared to obtain the vegetation adaptability evaluation results; Based on the results of vegetation adaptability evaluation, the hydrological element response relationship analysis of ecological management monitoring data was carried out to obtain vegetation-hydrology coupling characteristic data; Based on the vegetation-hydrology coupling characteristic data, the slope water balance status is evaluated and the slope water regulation efficiency index is obtained; The slope moisture control efficiency index is used to quantitatively analyze the erosion changes in the ecological management monitoring data to obtain the erosion control rate data; Based on the erosion control rate data, the spatial differentiation evaluation of the treatment effect of each area of ​​the slope was carried out to obtain the erosion control spatial distribution map; The spatial distribution map of erosion control and the input data of governance resources are coupled and analyzed, and the cost-effectiveness of each governance measure is compared to obtain the priority ranking of the benefits of the measures; Based on the priority ranking of measures and benefits, the key control parameter sensitivity analysis of ecological governance monitoring data was carried out to obtain the parameter sensitivity matrix; Based on the parameter sensitivity matrix, the key parameters of the comprehensive ecological governance plan are identified to obtain a set of key influencing factors; Optimize and simulate the set of key influencing factors to generate ecological governance optimization parameters.

[0048] After collecting slope ecological management monitoring data through the deployed multi-source sensor network, the embodiment of the present invention first uses drone multispectral images to obtain vegetation normalized difference index (NDVI), combines ground vegetation sample survey data, and uses change detection algorithm to calculate the vegetation coverage change value of each 10m×10m grid unit before management and 30 days, 60 days, and 90 days after management. When the grid vegetation coverage reaches 75% of the initial target value, it is determined to be a qualified area, and grid vegetation recovery rate evaluation data is generated; then each vegetation type is grouped according to herbaceous plants, shrubs, and trees, and the groups on different slopes are compared through one-way analysis of variance. The growth height, root expansion range, and coverage rate under different degrees (15°, 25°, and 35°) were compared. The species with statistically significant differences (P<0.05) were marked as dominant species. The five plants with the strongest drought tolerance and the five plants with the strongest soil fixation ability were recorded to form the vegetation adaptability evaluation results. Then, based on the rainfall and runoff data of the hydrological observation station and the real-time monitoring data of the soil moisture sensor, the grey correlation analysis method was used to calculate the correlation between vegetation coverage and evapotranspiration, runoff coefficient, and infiltration rate. The elements with a correlation greater than 0.85 were determined to be strongly coupled elements, and the vegetation-hydrology coupling characteristic data were constructed. Then, according to the layout The data of 15 soil moisture monitoring points set at different positions on the slope were used to generate the slope moisture distribution map using the Kriging spatial interpolation method. The ratio of actual evapotranspiration to potential evapotranspiration in each area was calculated as the water use efficiency. The areas with a ratio greater than 0.65 were marked as high-efficiency areas, and the areas with a ratio less than 0.4 were marked as low-efficiency areas, forming a slope moisture regulation efficiency index. The soil erosion data measured by the rainfall erosion force meter before, during, and after treatment were used to calculate the erosion control rate = (initial erosion - current erosion) / initial erosion × 100%. The erosion control rate data was analyzed using the ArcGIS spatial analysis tool Generate an erosion control spatial distribution map with a grid resolution of 1m×1m; superimpose the erosion control spatial distribution map with governance resource input data (including vegetation planting cost, drainage facility construction cost, and irrigation system maintenance cost), calculate the unit cost-effectiveness of each governance measure through input-output ratio analysis, and rank the measures from high to low in terms of benefits, with shrub + herbaceous mixed planting, drainage ditch optimization configuration, and micro-topography transformation as the top three priority measures; apply the Morris global sensitivity analysis method to the governance monitoring data based on the ranking results, and calculate the sensitivity coefficients of 18 control parameters to the erosion control rate. The sensitivity coefficients are greater than 0.5 parameters constitute the parameter sensitivity matrix; the five parameters with the highest sensitivity coefficients are extracted from the parameter sensitivity matrix as the key influencing factor set (including shrub coverage, herbaceous plant planting density, drainage ditch spacing, irrigation water volume, and soil conditioner dosage); finally, the key influencing factors are combined and optimized through the orthogonal experimental design method, and three levels are set for testing. The optimal combination is determined through the erosion control rate response surface analysis: shrub coverage 40%, herbaceous plant planting density 2700 plants / m², drainage ditch spacing 15m, irrigation water volume is adjusted in real time according to soil moisture content at 3-7 liters / m², and soil conditioner dosage is 0.5kg / m², forming the ecological governance optimization parameters. .

[0049] The present invention evaluates the vegetation recovery rate through spatiotemporal analysis of vegetation cover changes and accurately monitors the ecological restoration process. The growth effects of different vegetation types are compared and analyzed to improve the scientific nature of vegetation configuration. Combined with the analysis of hydrological element response, vegetation-hydrology coupling characteristic data is constructed to optimize the slope moisture control strategy. The water balance is evaluated to improve the efficiency of water resource utilization, and the erosion control rate is calculated through erosion change analysis to quantify the governance effect. Based on spatial differentiation evaluation, weak governance areas are identified to optimize resource allocation. The economy of governance measures is improved through cost-benefit analysis, and key influencing factors are identified through sensitivity analysis, governance parameters are optimized, and the accuracy and stability of slope ecological governance are improved.

[0050] Preferably, the step S6 of optimizing the ecological governance scheme based on the ecological governance optimization parameters includes: Adjust the parameters of the comprehensive ecological governance plan based on the ecological governance optimization parameters to obtain the optimized ecological governance plan; Re-implement the slope ecological management measures based on the optimized ecological management plan and obtain the re-management execution data; Based on the re-management execution data, the ecological status of the slope is monitored in real time to obtain the re-management monitoring data; Based on the re-governance monitoring data, the adaptability and long-term effects of the optimized ecological governance plan are optimized to form a self-adaptive optimization governance process.

[0051] The embodiment of the present invention firstly accurately adjusts the parameters in the comprehensive ecological management plan, specifically including increasing the shrub coverage from 35% of the original plan to 40%, increasing the herbaceous plant planting density from 2500 plants / m² to 2700 plants / m², reducing the drainage ditch spacing from 20m to 15m, and changing the irrigation system to automatically adjust the irrigation water volume within the range of 3-7 liters / m² according to the real-time data of the soil moisture sensor, and fixing the amount of soil conditioner at 0.5kg / m². The construction drawings are regenerated through the spatial analysis tool of the geographic information system to form an optimized ecological management plan; then, according to the optimized ecological The ecological management plan adjusted the implementation content of the project. First, the terrain was slightly modified. A 0.5m wide and 0.4m deep intercepting ditch was set every 15m on the slope. A hydraulic excavator was used to dig a planting trough with a depth of 30cm. 0.5kg / m² of organic soil conditioner was applied to each vegetation restoration calibration area and mixed thoroughly. Then, vegetation was configured according to the adjusted planting density. Mechanical spraying technology was used to spray herbaceous plant seeds, shrub seedlings were planted at fixed points manually, and trees were planted in the upper area of ​​the slope. At the same time, an automatic soil moisture monitoring system and intelligent irrigation control equipment were installed to complete the data recording of the re-management execution. ; Then, the slope ecological status data is collected in real time through the multi-source sensor network (including 15 soil moisture sensors, 5 runoff monitors, 3 meteorological parameter collectors, and 2 soil erosion observation devices). The data collection frequency is set to once every 4 hours for moisture data, once a day for runoff data, once an hour for meteorological data, and once a week for erosion data. The data is transmitted to the data center in real time through the 4G wireless transmission module. After data cleaning and outlier removal, the re-management monitoring data is formed; finally, an adaptive optimization control system is established based on the re-management monitoring data. Through the set key indicators The threshold (vegetation coverage is lower than 70% of the target value, soil erosion exceeds 25% of the initial value, soil moisture is lower than 25% or higher than 80%) triggers the early warning mechanism. When the data in a certain area of ​​the slope is monitored to be abnormal, it automatically generates suggestions for adjustment of management measures, including replanting vegetation, adjusting irrigation parameters, reinforcing drainage facilities, and adding soil conditioners. The adjusted management measures are implemented to form a complete set of "monitoring-analysis-adjustment-re-monitoring" closed-loop control process, realize the adaptive optimization of slope ecological management, and generate adaptive optimization management process documents as a technical guide for long-term management and maintenance of slopes.

[0052] The present invention adjusts the governance scheme through ecological governance optimization parameters to improve the pertinence and adaptability of governance measures. Re-implement the optimized governance measures to ensure the continuous improvement of the slope ecological restoration effect. Real-time monitoring is carried out based on the re-governance execution data to dynamically obtain the ecological status of the slope and ensure the accuracy of the governance process. Combined with the monitoring data, the optimization plan is evaluated for long-term effects to ensure the sustainability of ecological governance measures. Through adaptive optimization and adjustment, an adaptive optimization governance process is formed to achieve dynamic feedback and continuous optimization of slope governance, thereby improving the efficiency and stability of ecological restoration.

[0053] Preferably, the present invention further provides a system for ecological management of water conservancy slopes based on multi-source sensor data, which is used to execute the above-mentioned method for ecological management of water conservancy slopes based on multi-source sensor data. The system for ecological management of water conservancy slopes based on multi-source sensor data comprises: Multi-source environmental data acquisition module, used to collect meteorological monitoring data, soil hydrological monitoring data, vegetation coverage monitoring data, topographic mapping data and soil erosion monitoring data as original slope ecological environment data; The data standardization preprocessing module is used to perform data pre-registration processing on the original slope ecological environment data to obtain standardized slope ecological environment data; The erosion dynamics simulation and risk assessment module is used to simulate the erosion-runoff coupling process using standardized slope ecological environment data to obtain soil and water erosion dynamics parameters; predict soil and water loss trends based on soil and water erosion dynamics parameters to generate soil and water loss risk assessment data; Ecological governance program planning module, used to design ecological governance programs based on soil and water loss risk assessment data; Dynamic control and collaborative monitoring module, used to perform slope vegetation seeding simulation and record initial vegetation coverage data; implement dynamic control of slope drainage facilities and record optimized slope water flow control data; carry out slope vegetation precision irrigation simulation and record slope vegetation irrigation data; implement water conservancy slope ecological monitoring based on initial vegetation coverage data, optimized slope water flow control data and vegetation growth monitoring data to obtain ecological management monitoring data; The governance effectiveness feedback optimization module is used to evaluate the governance effect of ecological governance monitoring data and generate ecological governance optimization parameters; and optimize the ecological governance plan based on the ecological governance optimization parameters.

[0054] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is not limited by the above description, so it is intended to include all changes within the meaning and scope of the equivalent elements of the application documents in the present invention.

[0055] 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 method for water conservancy slope ecological management based on multi-source sensor data, characterized in that: The following steps are involved: Step S1: collecting meteorological monitoring data, soil hydrological monitoring data, vegetation coverage monitoring data, topographic mapping data and soil erosion monitoring data as original slope ecological environment data; Step S2: performing data pre-registration processing on the original slope ecological environment data to obtain standardized slope ecological environment data; Step S3: using standardized slope ecological environment data to simulate the erosion-runoff coupling process, and obtaining soil and water erosion dynamics parameters; based on the soil and water erosion dynamics parameters, predicting the soil and water loss trend, and generating soil and water loss risk assessment data; Step S4: Designing an ecological governance plan based on soil and water loss risk assessment data; Step S5: Execute slope vegetation seeding simulation and record initial vegetation coverage data; implement dynamic regulation of slope drainage facilities and record optimized slope water flow regulation data; carry out slope vegetation precision irrigation simulation and record slope vegetation irrigation data; implement water conservancy slope ecological monitoring based on initial vegetation coverage data, optimized slope water flow regulation data and vegetation growth monitoring data to obtain ecological management monitoring data; Step S6: Evaluate the ecological governance effect of the ecological governance monitoring data and generate ecological governance optimization parameters; Optimize ecological governance plans based on ecological governance optimization parameters.

2. The method for water conservancy slope ecological management based on multi-source sensor data according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: deploying ground meteorological sensors in the water conservancy slope, and using the ground meteorological sensors to collect rainfall, wind speed and air humidity as meteorological monitoring data; Step S12: deploying soil moisture sensors and soil temperature sensors in the water conservancy slope, using the soil moisture sensors to monitor soil moisture, and using the soil temperature sensors to monitor soil temperature, to obtain soil hydrological monitoring data; Step S13: deploying an unmanned aerial vehicle remote sensing system and a hyperspectral imager in the water conservancy slope, using the unmanned aerial vehicle remote sensing system to perform remote sensing monitoring of vegetation coverage, and using the hyperspectral imager to perform vegetation health index monitoring, to obtain vegetation coverage monitoring data; Step S14: deploying LiDAR scanning equipment in the water conservancy slope, and using the LiDAR scanning equipment to perform high-precision scanning of slope, slope length and micro-topographic features to obtain topographic mapping data; Step S15: deploying soil and water loss monitoring stations and rainfall erosion sensing equipment in the water conservancy slope, using the soil and water loss monitoring stations to monitor the sediment content and runoff in real time, and using the rainfall erosion sensing equipment to monitor the erosion rate in real time, to obtain soil and water loss monitoring data; Step S16: Record the meteorological monitoring data, soil hydrological monitoring data, vegetation coverage monitoring data, topographic surveying data and soil erosion monitoring data as original slope ecological environment data.

3. The method for water conservancy slope ecological management based on multi-source sensor data according to claim 2 is characterized in that: Step S2 includes the following steps: Step S21: Time-align the slope ecological environment data to obtain time-consistent ecological environment data; Step S22: unifying the time-consistent ecological environment data into a standard data format to obtain formatted ecological environment data; Step S23: removing outliers from the formatted ecological environment data to obtain denoised ecological environment data, wherein the outliers are specifically abnormal data caused by sensor errors, signal interference, and acquisition failures; Step S24: completing missing data of the denoised ecological environment data to obtain complete ecological environment data; Step S25: normalize and standardize the complete ecological environment data to obtain standardized slope ecological environment data.

4. The method for water conservancy slope ecological management based on multi-source sensor data according to claim 3 is characterized in that: The erosion-runoff coupling process simulation using the standardized slope ecological environment data in step S3 includes: The rainfall erosivity factor is extracted from the standardized slope ecological environment data to obtain the spatiotemporal distribution factor, where the spatiotemporal distribution factor is positive and ranges from 0 to 20000 MJ·mm / (ha·h·a); The soil texture characteristics were parameterized based on the standardized slope ecological environment data to obtain the soil erodibility factor, which was within the range of 0.01-0.5 t·ha·h / (ha·MJ·mm); When the soil erodibility factor is less than 0.01 t·ha·h / (ha·MJ·mm), it indicates that the soil is almost inerodible rock; when the soil erodibility factor is greater than 0.5 t·ha·h / (ha·MJ·mm), it indicates that the soil structure is loose and unreasonable. The slope length and slope factor of terrain characteristics are calculated using standardized slope ecological environment data to obtain the slope length and slope factor; The vegetation coverage monitoring data in the standardized slope ecological environment data is converted into vegetation protection factors to obtain vegetation coverage and management factors; The vegetation cover and management factor is a ratio value, which indicates the mitigation effect of vegetation cover on erosion and ranges from 0 to 1; When the vegetation cover and management factor is equal to 0, it indicates an ideal vegetation cover that completely suppresses erosion, and when the vegetation cover and management factor is equal to 1, it indicates that the bare surface is unprotected; Based on the standardized slope ecological environment data, the water flow direction in the water flow convergence area is calculated to obtain the slope water flow path network; Based on the slope water flow path network, the comprehensive erosion model was parameterized using the spatiotemporal distribution factor, soil erodibility factor, slope length and slope gradient factor, and vegetation coverage and management factor to obtain a soil and water erosion prediction model. Based on the soil and water erosion prediction model, the erosion rate of each area of ​​the slope is calculated to obtain spatially differentiated erosion rate data within the range of 0-500t / (ha·a); Obtain measured erosion data, compare and analyze the measured erosion data with spatially differentiated erosion rate data, use the comparative analysis results to calibrate the soil and water erosion prediction model, and generate soil and water erosion dynamics parameters.

5. The method for water conservancy slope ecological management based on multi-source sensor data according to claim 4 is characterized in that: The prediction of soil and water loss trend based on soil and water erosion dynamics parameters in step S3 includes: Multi-dimensional characteristic classification of soil and water erosion dynamic parameters was performed to obtain the spatiotemporal distribution data of erosion intensity; Based on the standardized slope ecological environment data and the spatiotemporal distribution data of erosion intensity, multi-scale dynamic modeling of erosion-runoff-terrain feedback was carried out to obtain the slope erosion evolution model; Obtain historical meteorological data, and use the historical meteorological data to perform spatiotemporal extrapolation prediction of slope erosion intensity on the slope erosion evolution model to obtain a map of erosion intensity changes; Perform threshold segmentation on the erosion intensity change map to obtain erosion risk level zoning data; Based on the vegetation coverage monitoring data and erosion risk level zoning data in the standardized slope ecological environment data, the interactive response analysis between vegetation protection and hydrological erosion mechanism was carried out to generate a soil and water loss risk association matrix; The soil and water loss risk association matrix is ​​superimposed in time and space, and the risk priority of each slope area is ranked to generate soil and water loss risk assessment data.

6. The method for water conservancy slope ecological management based on multi-source sensor data according to claim 5 is characterized in that: Step S4 includes the following steps: Step S41: Analyze the vegetation adaptability, soil stability and climate conditions of the slope area based on the soil and water loss risk assessment data and the preset ecological restoration database to generate vegetation restoration parameter data; Step S42: The vegetation adaptability analysis is specifically that the vegetation adaptability index is limited to a range of 0.6-1. When the vegetation adaptability index is less than 0.6, it indicates that the vegetation has a weak ability to survive under the environmental conditions. Step S43: Optimizing suitable vegetation types and planting density based on vegetation restoration parameter data to generate an optimal vegetation configuration plan; Step S44: the planting density in the optimal vegetation configuration scheme is 1000-3000 plants / m² for herbaceous plants, 30-100 plants / 100m² for shrubs, and 50-150 plants / mu for trees; Step S45: optimizing the layout position and drainage capacity of slope drainage facilities based on the soil and water loss risk assessment data and the slope water flow path network, and generating an optimized design plan for drainage facilities; Step S46: optimizing the slope irrigation strategy, irrigation frequency and water distribution based on the soil and water loss risk assessment data to obtain a precise vegetation irrigation plan; Step S47: Integrate the optimal vegetation configuration plan, drainage facility optimization design plan and vegetation precision irrigation plan to obtain a comprehensive ecological management plan.

7. The method for water conservancy slope ecological management based on multi-source sensor data according to claim 6 is characterized in that: The execution of slope vegetation seeding simulation and recording of initial vegetation coverage data in step S5 includes: Based on the comprehensive ecological management plan, the slope vegetation restoration area is calibrated to obtain the vegetation restoration calibration data; Based on the vegetation restoration calibration data, the sowing range, density and optimal growth area of ​​each vegetation type are planned to obtain vegetation sowing planning data; Based on the vegetation seeding planning data, the vegetation seeding simulation is carried out on the slope to generate the initial vegetation coverage data.

8. The method for water conservancy slope ecological management based on multi-source sensor data according to claim 7 is characterized in that: The step S6 of evaluating the ecological governance monitoring data and generating ecological governance optimization parameters includes: Conduct spatiotemporal analysis of vegetation cover changes on ecological governance monitoring data to obtain vegetation recovery rate assessment data; Based on the vegetation recovery rate assessment data, the growth effects of various vegetation types were compared to obtain the vegetation adaptability evaluation results; Based on the results of vegetation adaptability evaluation, the hydrological element response relationship analysis of ecological management monitoring data was carried out to obtain vegetation-hydrology coupling characteristic data; Based on the vegetation-hydrology coupling characteristic data, the slope water balance status is evaluated and the slope water regulation efficiency index is obtained; The slope moisture control efficiency index is used to quantitatively analyze the erosion changes in the ecological management monitoring data to obtain the erosion control rate data; Based on the erosion control rate data, the spatial differentiation evaluation of the treatment effect of each area of ​​the slope was carried out to obtain the erosion control spatial distribution map; The spatial distribution map of erosion control and the input data of governance resources are coupled and analyzed, and the cost-effectiveness of each governance measure is compared to obtain the priority ranking of the benefits of the measures; Based on the priority ranking of measures and benefits, the key control parameter sensitivity analysis of ecological governance monitoring data was carried out to obtain the parameter sensitivity matrix; Based on the parameter sensitivity matrix, the key parameters of the comprehensive ecological governance plan are identified to obtain a set of key influencing factors; Optimize and simulate the set of key influencing factors to generate ecological governance optimization parameters.

9. The method for water conservancy slope ecological management based on multi-source sensor data according to claim 8 is characterized in that: The optimization of the ecological governance scheme based on the ecological governance optimization parameters in step S6 includes: Adjust the parameters of the comprehensive ecological governance plan based on the ecological governance optimization parameters to obtain the optimized ecological governance plan; Re-implement the slope ecological management measures based on the optimized ecological management plan and obtain the re-management execution data; Based on the re-management execution data, the ecological status of the slope is monitored in real time to obtain the re-management monitoring data; Based on the re-governance monitoring data, the adaptability and long-term effects of the optimized ecological governance plan are optimized to form a self-adaptive optimization governance process.

10. A system for water conservancy slope ecological management based on multi-source sensor data, characterized in that: Used to execute the method for water conservancy slope ecological management based on multi-source sensor data as claimed in claim 1, the water conservancy slope ecological management system based on multi-source sensor data includes: Multi-source environmental data acquisition module, used to collect meteorological monitoring data, soil hydrological monitoring data, vegetation coverage monitoring data, topographic mapping data and soil erosion monitoring data as original slope ecological environment data; The data standardization preprocessing module is used to perform data pre-registration processing on the original slope ecological environment data to obtain standardized slope ecological environment data; The erosion dynamics simulation and risk assessment module is used to simulate the erosion-runoff coupling process using standardized slope ecological environment data to obtain soil and water erosion dynamics parameters; predict soil and water loss trends based on soil and water erosion dynamics parameters to generate soil and water loss risk assessment data; Ecological governance program planning module, used to design ecological governance programs based on soil and water loss risk assessment data; Dynamic control and collaborative monitoring module, used to perform slope vegetation seeding simulation and record initial vegetation coverage data; implement dynamic control of slope drainage facilities and record optimized slope water flow control data; carry out slope vegetation precision irrigation simulation and record slope vegetation irrigation data; implement water conservancy slope ecological monitoring based on initial vegetation coverage data, optimized slope water flow control data and vegetation growth monitoring data to obtain ecological management monitoring data; The governance effectiveness feedback optimization module is used to evaluate the governance effect of ecological governance monitoring data and generate ecological governance optimization parameters; and optimize the ecological governance plan based on the ecological governance optimization parameters.

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