Intelligent water and soil conservation monitoring and management system

Abstract

The invention provides an intelligent water and soil conservation monitoring and management system, and belongs to the technical field of hydraulic engineering and environment monitoring. The data acquisition layer acquires data in real time by using an acoustic sensor network, machine vision monitoring equipment, Internet of Things monitoring equipment, satellite remote sensing monitoring, unmanned aerial vehicle inspection and other technologies. And the data transmission layer transmits the acquired data to the data processing and analyzing layer by adopting a wireless communication network. The data processing and analysis layer analyzes multi-source data through data fusion and preprocessing, GIS technology application and big data analysis and machine learning. And the decision support and application layer comprises a prevention and control measure generation and optimization unit, a real-time early warning and emergency response unit and a user interaction and decision support unit, provides a water and soil conservation and dike reinforcement strategy for a user, and has a feedback optimization mechanism. By comprehensively applying various monitoring technologies and data analysis methods, the accuracy and timeliness of water and soil loss monitoring are improved, and powerful guarantee is provided for dike safety.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy engineering and environmental monitoring, and in particular to an intelligent soil and water conservation monitoring and management system. Background Art

[0002] In the field of water conservancy engineering and environmental monitoring technology, soil and water conservation monitoring and management have always been a crucial link. Traditional soil and water conservation monitoring methods mainly rely on manual on-site surveys, regular sampling and analysis, and simple equipment monitoring. These methods are not only inefficient, but also difficult to achieve full coverage and real-time monitoring of the monitoring area, resulting in difficulty in timely detection and effective control of soil and water loss.

[0003] With the advancement of science and technology, although some automated monitoring equipment and technologies have emerged, such as single acoustic sensors, machine vision monitoring, and Internet of Things devices, these technologies still have many shortcomings in the application process. For example, although acoustic sensors can detect tiny displacements of soil particles, they are greatly disturbed by environmental noise, and the accuracy of data needs to be improved; although machine vision monitoring equipment can capture the erosion of the soil surface, it is seriously affected by weather and lighting conditions, and has limited processing and analysis capabilities for image data; although Internet of Things devices can collect a variety of environmental parameters in real time, there is a serious data island phenomenon between the devices, making it difficult to achieve effective data fusion and comprehensive analysis.

[0004] In addition, the existing monitoring system also has obvious shortcomings in data processing and analysis. Traditional data processing methods can only analyze a single data source and lack the ability to integrate and process multi-source data, resulting in one-sided and inaccurate monitoring results. At the same time, existing analysis methods are often limited to simple data statistics and visualization, lacking in-depth assessment and prediction of soil erosion risks and the safety status of embankment structures.

[0005] Therefore, in view of the shortcomings of the existing technology, the present invention proposes an intelligent soil and water conservation monitoring and management system, which aims to achieve real-time monitoring and comprehensive evaluation of soil and water loss through the comprehensive use of multiple monitoring technologies and data analysis methods, so as to provide strong protection for embankment safety. Summary of the invention

[0006] The purpose of the present invention is to provide an intelligent soil and water conservation monitoring and management system, which improves the accuracy and timeliness of soil and water loss monitoring by comprehensively using a variety of monitoring technologies and data analysis methods, and provides a strong guarantee for embankment safety.

[0007] To achieve the above objectives, the present invention provides an intelligent soil and water conservation monitoring and management system, comprising:

[0008] The data collection layer is used to collect real-time monitoring data related to the levee, including acoustic sensor network data, machine vision monitoring equipment data, Internet of Things monitoring equipment data, satellite remote sensing monitoring data, and drone inspection data;

[0009] The data transmission layer uses a wireless communication network to transmit the data acquired by the data collection layer to the data processing and analysis layer;

[0010] The data processing and analysis layer includes data fusion and preprocessing units, GIS technology application units, and big data analysis and machine learning units, which are used for fusion processing, spatial association, visualization, risk assessment, and trend prediction of multi-source data;

[0011] The decision support and application layer includes the prevention and control measures generation and optimization unit, the real-time warning and emergency response unit, and the user interaction and decision support unit, which are used to generate prevention and control measures, real-time monitoring and warning, and provide decision support for users.

[0012] Preferably, the operation method of the data fusion and preprocessing unit is as follows:

[0013] S11, cleaning the data collected by the data collection layer, including removing noise data and outliers;

[0014] S12. Normalize and standardize data from different sources;

[0015] S13, using data fusion algorithm to fuse data from different sources to obtain embankment soil and water conservation monitoring data;

[0016] S131. Calculate the reliability weight ω of each data source i :

[0017]

[0018] Among them, α represents the adjustment coefficient, which is used to control the rate of weight change; R i represents the historical accuracy of the ith data source for levee monitoring; i = 1, 2, 3, 4, 5, corresponding to acoustic sensor network data, machine vision monitoring equipment data, Internet of Things monitoring equipment data, satellite remote sensing monitoring data, and drone inspection data, respectively;

[0019] S132. According to the reliability weight of each data source, the data is integrated:

[0020]

[0021] Among them, F represents the integrated levee soil and water conservation monitoring data; X i Represents the standardized embankment monitoring data of the i-th data source.

[0022] Preferably, the operation method of the GIS technology application unit is as follows:

[0023] S21, using GIS software to spatially associate the levee geographic coordinate information obtained from the data collection layer with the fused levee soil and water conservation monitoring data F, and construct a spatial database for levee soil and water conservation monitoring;

[0024] S22. Conduct spatial assessment of soil erosion risk along the levee and identify high-risk areas of the levee;

[0025] S221. Divide the dike into N grid cells. The fused dike soil and water conservation monitoring data of each grid cell n is F. n ;

[0026] S222. Calculate the levee soil loss risk index Z for each grid unit n n :

[0027] Z n =β 1 ×F n +β 2 ×C;

[0028] Among them, β 1 , β 2 All represent weight coefficients; C represents other relevant factors;

[0029] S223, calculate the global spatial autocorrelation coefficient I:

[0030]

[0031] Where N is the total number of grid cells along the dike; W is the sum of the spatial weights between all grid cells along the dike; ω ab represents the spatial weight between grid cells a and b along the levee; It represents the average value of the risk index of all grid cells along the dike.

[0032] Preferably, the operation method of the big data analysis and machine learning unit is as follows:

[0033] S31, taking the time series data as the input of the multi-head attention mechanism to extract local features related to the levee;

[0034] F t '=MultiHead(X t ,X t ,X t )=Concat(head 1 ,head 2 ,…head h )WO ;

[0035] Among them, F t ' represents local features; X t represents time series data, X t =[H t ,E t ,R t ,V t ,S t ,F,I],H t represents the soil moisture of the embankment foundation, E t represents the erosion depth of the dike, R t represents the rainfall near the dike, V t Indicates the vegetation coverage around the embankment, S t Indicates the water flow velocity near the embankment; W O represents the linear transformation matrix of the output; h represents the number of heads; the attention function is as follows:

[0036]

[0037] Where Q, K, and V represent query vector, key vector, and value vector respectively; d k represents the dimension of the key vector;

[0038] S32, extract the local feature F t 'Input into the LSTM layer to learn the long-term dependencies of the time series and predict the risk of levee soil erosion and the safety status of the levee structure;

[0039] Q t =LSTM(F t ');

[0040] Among them, Q t represents the output of the LSTM layer, i.e., the predicted risk of levee soil erosion and the predicted value of levee structure safety status at time t;

[0041] S33, using Q-learning algorithm to optimize the decision-making process of the model;

[0042] Assume state S t is the monitoring data X at the current moment t , action a t is the output Q of the LSTM layer t , then the reward r t as follows:

[0043] r t =-Loss(a t ,Y t );

[0044] Among them, Loss represents the mean square error loss function; Y t Indicates the actual levee soil erosion situation and levee structure safety status;

[0045] Use the Q-learning algorithm to optimize the Q function and select the optimal action based on the current state.

[0046] Preferably, the prevention and control measures generation and optimization unit combines historical prevention and control experience and expert knowledge base, and automatically generates and recommends personalized soil and water conservation and embankment reinforcement strategies based on the output results of the data processing and analysis layer. It also has a feedback optimization mechanism to continuously learn and adjust the recommended strategies based on the prevention and control effect feedback and embankment safety assessment results.

[0047] Preferably, the real-time early warning and emergency response unit monitors soil erosion, embankment structure safety parameters, and meteorological and hydrological conditions in real time. Once an abnormality is detected or a preset threshold is reached, the early warning mechanism is immediately triggered, and early warning information is sent to management personnel and relevant departments via SMS, email, and APP push.

[0048] Preferably, the user interaction and decision support unit provides a user-friendly interface and supports multi-terminal access, including computers, tablets, and mobile phones. The user interface displays monitoring data and analysis results in the form of charts, maps, and three-dimensional models to facilitate user viewing and query.

[0049] Therefore, the present invention adopts the above-mentioned intelligent soil and water conservation monitoring and management system, and the beneficial technical effects are as follows:

[0050] In terms of data collection, the system no longer relies solely on manual on-site surveys and traditional equipment monitoring, but has introduced a variety of high-tech monitoring methods such as acoustic sensor networks, machine vision monitoring equipment, Internet of Things monitoring equipment, satellite remote sensing monitoring, and drone inspections. This diversified data collection method not only greatly improves the real-time and accuracy of data acquisition, but also expands the monitoring scope, making the monitoring work more comprehensive and detailed.

[0051] In terms of data processing and analysis, the present invention achieves in-depth mining and efficient use of multi-source data through data fusion and preprocessing, GIS technology application, big data analysis and machine learning, etc. This not only provides a more scientific and accurate basis for decision-making, but also significantly improves the speed and efficiency of data processing.

[0052] At the application level, the system can automatically generate and recommend personalized soil and water conservation and embankment reinforcement strategies based on the results of data processing and analysis. At the same time, the real-time early warning and emergency response functions can promptly detect and respond to potential safety hazards, providing a strong guarantee for embankment safety. In addition, the design of user interaction and decision support units also enables users to more conveniently view and query monitoring data and analysis results, so as to make more correct decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a structural diagram of an intelligent soil and water conservation monitoring and management system of the present invention;

[0054] Figure 2 This is a flowchart of data fusion and preprocessing;

[0055] Figure 3 It is a flowchart for GIS technology application. DETAILED DESCRIPTION

[0056] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0057] Unless otherwise defined, technical or scientific terms used in the present invention shall have the common meanings understood by one having ordinary skills in the field to which the present invention belongs.

[0058] Embodiment 1

[0059] like Figure 1-Figure 3 The present invention provides an intelligent soil and water conservation monitoring and management system, comprising:

[0060] The data collection layer is used to collect real-time monitoring data related to the embankment, including acoustic sensor network data, machine vision monitoring equipment data, Internet of Things monitoring equipment data, satellite remote sensing monitoring data and drone inspection data.

[0061] The sensor nodes of the acoustic sensor network use highly sensitive piezoelectric acoustic sensors, which can detect weak sound wave signals generated by tiny displacements of soil particles. The density of sensor nodes is adjusted according to the terrain complexity and soil erosion risk level of the monitoring area. The density of sensor nodes in high-risk areas is not less than 10m per 10m. 2 A sensor node.

[0062] Machine vision monitoring equipment includes high-definition cameras and infrared cameras. The resolution of the high-definition cameras is not less than 4K, and can clearly capture tiny erosion gullies on the soil surface and subtle damage to vegetation. The thermal imaging accuracy of the infrared camera reaches 0.1 degrees Celsius, which can accurately monitor soil temperature changes and thermal radiation of vegetation. The frequency of image and video data collection is dynamically adjusted according to monitoring needs, and the collection frequency of key areas is not less than once an hour.

[0063] IoT monitoring equipment includes soil moisture sensors, erosion depth monitors, rain gauges, vegetation coverage monitoring cameras, water flow velocity meters, etc. Each monitoring device has the functions of automatic data collection, storage and wireless transmission, and can send the collected data to the data processing and analysis layer at set time intervals or trigger conditions.

[0064] The satellite remote sensing image resolution used in satellite remote sensing monitoring is not less than 1m, which can clearly identify the main land features and land use changes in the monitored area. The frequency of satellite remote sensing data acquisition is dynamically adjusted according to the soil and water loss risk level of the monitored area. The frequency of data acquisition in high-risk areas is not less than once a month.

[0065] The drone has automatic obstacle avoidance function and long flight endurance, and can fly stably in complex terrain and severe weather conditions. The multispectral camera it carries can simultaneously capture images in multiple bands such as red light, near-infrared light, and green light. The measurement accuracy of the lidar reaches the centimeter level, and it can accurately measure the morphological features such as the depth and width of soil erosion gullies.

[0066] The data transmission layer uses a wireless communication network to transmit the data acquired by the data collection layer to the data processing and analysis layer.

[0067] The data processing and analysis layer, including data fusion and preprocessing unit, GIS technology application unit and big data analysis and machine learning unit, is used for fusion processing, spatial association, visualization, risk assessment and trend prediction of multi-source data.

[0068] The operation method of the data fusion and preprocessing unit is as follows:

[0069] S11, cleaning the data collected by the data collection layer, including removing noise data and outliers;

[0070] S12. Normalize and standardize data from different sources;

[0071] S13, using data fusion algorithm to fuse data from different sources to obtain embankment soil and water conservation monitoring data;

[0072] S131. Calculate the reliability weight ω of each data source i :

[0073]

[0074] Among them, α represents the adjustment coefficient, which is used to control the rate of weight change; R irepresents the historical accuracy of the ith data source for levee monitoring; i = 1, 2, 3, 4, 5, corresponding to acoustic sensor network data, machine vision monitoring equipment data, Internet of Things monitoring equipment data, satellite remote sensing monitoring data, and drone inspection data, respectively;

[0075] S132. According to the reliability weight of each data source, the data is integrated:

[0076]

[0077] Among them, F represents the integrated levee soil and water conservation monitoring data; X i Represents the standardized embankment monitoring data of the i-th data source.

[0078] The operation method of GIS technology application unit is as follows:

[0079] S21, using GIS software to spatially associate the levee geographic coordinate information obtained from the data collection layer with the fused levee soil and water conservation monitoring data F, and construct a spatial database for levee soil and water conservation monitoring;

[0080] Data preprocessing and format unification: Before spatially associating the levee geographic coordinate information obtained from the data collection layer with the fused levee soil and water conservation monitoring data, the data needs to be preprocessed. For the geographic coordinate information, ensure that it uses a unified coordinate system (WGS-84 coordinate system), and standardize the data format so that it can be seamlessly connected with the monitoring data. For the fused monitoring data, check the integrity of the data and reasonably interpolate the missing data (using the time series interpolation method) to ensure the continuity and integrity of the data.

[0081] Selection and implementation of spatial association methods: Use spatial association tools in GIS software, such as spatial join or spatial overlay analysis, to achieve the association between geographic coordinate information and monitoring data. Taking spatial join as an example, the monitoring data points can be matched with the nearest levee geographic coordinate points, and the attribute information of the monitoring data can be associated with the corresponding geographic coordinate points, thereby building a spatial database for levee soil and water conservation monitoring. During the association process, a reasonable matching distance threshold needs to be set to ensure the accuracy of the association and avoid incorrect association due to geographic coordinate errors.

[0082] Database construction and management: The constructed spatial database should have efficient data storage, query and management functions. Use a relational database management system, such as PostgreSQL combined with PostGIS extension, to store spatial data and attribute data in an integrated manner. Rationally design the database table structure, store geographic coordinate information, monitoring data and associated spatial data in different data tables, and establish associations between tables through foreign key constraints and other means. At the same time, to improve data query efficiency, index key fields, such as the ID of geographic coordinate points and the timestamp of monitoring data, to quickly retrieve and analyze data.

[0083] S22. Conduct spatial assessment of soil erosion risk along the levee and identify high-risk areas of the levee;

[0084] S221. Divide the dike into N grid cells. The fused dike soil and water conservation monitoring data of each grid cell n is F. n ;

[0085] S222. Calculate the levee soil loss risk index Z for each grid unit n n :

[0086] Z n =β 1 ×F n +β 2 ×C;

[0087] Among them, β 1 , β 2 All represent weight coefficients; C represents other related factors; other related factors are determined based on experience, for example, topographic factors: including slope, slope direction, slope length, etc. The slope affects the intensity and speed of soil erosion. The greater the slope, the stronger the scouring force of the water flow, and the higher the risk of soil erosion; the slope direction affects the intensity and time of solar radiation, which in turn affects vegetation growth and soil moisture conditions. The southern slope usually has lower vegetation coverage than the northern slope, and the soil moisture evaporates faster, so the risk of soil erosion is relatively high; the slope length also affects the convergence and scouring of water flow, and long slopes are more prone to soil erosion.

[0088] Soil characteristics, vegetation coverage, human activity, meteorological and hydrological factors, etc. are common knowledge and will not be elaborated here.

[0089] S223, calculate the global spatial autocorrelation coefficient I:

[0090]

[0091] Where N is the total number of grid cells along the dike; W is the sum of the spatial weights between all grid cells along the dike; ω abrepresents the spatial weight between grid cells a and b along the levee; It represents the average value of the risk index of all grid cells along the dike.

[0092] When I is significantly greater than 0, it indicates that there is an obvious spatial clustering of the risk of soil erosion on the levee, and the high-risk areas and low-risk areas are clustered together; when I is significantly less than 0, it indicates that the risk of soil erosion on the levee is discretely distributed; when I is close to 0, it indicates that the spatial distribution of the risk of soil erosion on the levee is random.

[0093] The operation methods of the Big Data Analysis and Machine Learning Unit are as follows:

[0094] S31, taking the time series data as the input of the multi-head attention mechanism to extract local features related to the levee;

[0095] F t '=MultiHead(X t ,X t ,X t )=Concat(head 1 ,head 2 ,…head h )W O ;

[0096] Among them, F t ' represents local features; X t represents time series data, X t =[H t ,E t ,R t ,V t ,S t ,F,I],H t represents the soil moisture of the embankment foundation, E t represents the erosion depth of the dike, R t represents the rainfall near the dike, V t Indicates the vegetation coverage around the embankment, S t Indicates the water flow velocity near the embankment; W O represents the linear transformation matrix of the output; h represents the number of heads; the attention function is as follows:

[0097]

[0098] Where Q, K, and V represent query vector, key vector, and value vector respectively; d k represents the dimension of the key vector;

[0099] S32, extract the local feature F t'Input into the LSTM layer to learn the long-term dependencies of the time series and predict the risk of levee soil erosion and the safety status of the levee structure;

[0100] Q t =LSTM(F t ');

[0101] Among them, Q t represents the output of the LSTM layer, i.e., the predicted risk of levee soil erosion and the predicted value of levee structure safety status at time t;

[0102] S33, using Q-learning algorithm to optimize the decision-making process of the model;

[0103] Assume state S t is the monitoring data X at the current moment t , action a t is the output Q of the LSTM layer t , then the reward r t as follows:

[0104] r t =-Loss(a t ,Y t );

[0105] Among them, Loss represents the mean square error loss function; Y t Indicates the actual levee soil erosion situation and levee structure safety status;

[0106] The Q-learning algorithm is used to optimize the Q function, which can select the optimal action according to the current state to maximize the cumulative reward, thereby optimizing the prediction decision process.

[0107] The decision support and application layer includes the prevention and control measures generation and optimization unit, the real-time warning and emergency response unit, and the user interaction and decision support unit, which are used to generate prevention and control measures, real-time monitoring and warning, and provide decision support for users.

[0108] The prevention and control measures generation and optimization unit combines historical prevention and control experience and expert knowledge base, and automatically generates and recommends personalized soil and water conservation and embankment reinforcement strategies based on the output results of the data processing and analysis layer. It also has a feedback optimization mechanism to continuously learn and adjust the recommended strategies based on the prevention and control effect feedback and embankment safety assessment results.

[0109] Historical prevention experience and expert knowledge base construction:

[0110] Data collection and collation: Extensively collect historical soil and water conservation cases, including data on the implementation of soil and water loss prevention projects in different regions and of different types, and effect evaluation. At the same time, invite experts in the fields of soil and water conservation, water conservancy engineering, etc. to summarize and sort out their rich practical experience to form a systematic expert knowledge system.

[0111] Knowledge base construction and update: Based on the collected historical data and expert knowledge, a knowledge base of prevention and control measures is constructed. The knowledge base uses a structured data storage method to classify and store prevention and control measures according to different soil erosion types, topography, soil characteristics and other factors, and annotates key information such as applicable conditions, implementation effects, and cost-effectiveness for each prevention and control measure. With the continuous accumulation of new data and the updating of expert knowledge during the operation of the system, the knowledge base is regularly optimized and expanded to ensure the accuracy and practicality of its content.

[0112] Personalized prevention and treatment strategy generation:

[0113] Data-driven strategy recommendation: Based on the monitoring data and risk assessment results of levee soil and water conservation output by the data processing and analysis layer, machine learning algorithms such as decision trees and random forests are used to mine and analyze historical prevention and control experiences, and to find historical cases similar to the current monitoring situation and their corresponding prevention and control measures. At the same time, combined with the rules and suggestions in the expert knowledge base, the geographical location, topography, soil characteristics, vegetation coverage and other factors of the levee are comprehensively considered to generate personalized soil and water conservation and levee reinforcement strategies for users.

[0114] Multi-objective optimization: When generating prevention and control strategies, we must not only consider the prevention and control effects, but also take into account factors such as cost, construction difficulty, environmental impact, etc. Multi-objective optimization algorithms, such as genetic algorithms and particle swarm optimization algorithms, are used to comprehensively evaluate and optimize prevention and control measures, providing users with a set of prevention and control plans that achieve a good balance between different objectives for users to choose according to actual conditions.

[0115] Feedback optimization mechanism:

[0116] Monitoring and evaluation of prevention and control effects: After implementing prevention and control measures, the monitoring equipment at the data collection layer continuously monitors the changes in indicators such as soil erosion and structural safety parameters of the levee, compares and analyzes the monitoring data with the data before the implementation of prevention and control measures, and evaluates the actual effect of the prevention and control measures. At the same time, experts are regularly organized to conduct on-site inspections and evaluations of prevention and control projects, score and evaluate the prevention and control effects from a professional perspective, and feed the evaluation results back to the system.

[0117] Model update and strategy adjustment: Update and optimize the machine learning model based on the monitoring and evaluation results of the prevention and control effects. If it is found that the effect of certain prevention and control measures does not meet expectations, the reasons may be due to unreasonable weight settings of certain factors in the model, or there are factors that are not considered. Based on this, adjust the parameters and structure of the model, retrain the model, and generate new prevention and control strategies for users based on the updated model. In addition, the feedback information on the prevention and control effects is updated to the expert knowledge base in a timely manner, and the content of the knowledge base is continuously enriched and improved to improve the accuracy and reliability of the prevention and control strategies generated by the system.

[0118] The real-time early warning and emergency response unit monitors soil erosion, embankment structure safety parameters, and meteorological and hydrological conditions in real time. Once an abnormality is detected or the preset threshold is reached, the early warning mechanism is immediately triggered and early warning information is sent to management personnel and relevant departments via SMS, email, and APP push.

[0119] Real-time monitoring of soil erosion:

[0120] Multi-source data fusion monitoring: Comprehensively utilize the data collected in real time by acoustic sensor networks, machine vision monitoring equipment, Internet of Things monitoring equipment, etc. to conduct all-round and multi-level monitoring of the soil and water loss of the levee. The acoustic sensor network monitors the tiny displacement of soil particles and the change of sound wave signals in real time, and judges the initial signs of soil erosion by analyzing the frequency, intensity and other characteristics of the signal; the machine vision monitoring equipment regularly takes images of the surface of the levee, and uses image processing technology such as edge detection and texture analysis to identify the shape, depth, width and other information of the soil erosion ditch, as well as the changes in vegetation coverage; the Internet of Things monitoring equipment collects data such as soil moisture, erosion depth, rainfall, etc. in real time to provide a basis for the quantitative analysis of soil and water loss.

[0121] Data processing and analysis: The collected multi-source data is transmitted to the data processing and analysis layer for data fusion and preprocessing. Data fusion algorithms such as Kalman filtering and particle filtering are used to fuse data from different sources, eliminate noise and interference between data, and improve data accuracy and reliability. At the same time, GIS technology is used to spatially associate monitoring data with the geographic coordinate information of the levee, build a spatial database for soil erosion monitoring, and realize the visualization and spatial analysis of levee soil erosion. By setting reasonable soil erosion risk thresholds, such as soil erosion modulus, erosion depth change rate, etc., when the monitoring data exceeds the threshold, the system automatically identifies high-risk areas for soil erosion and issues early warning signals.

[0122] Real-time monitoring of embankment structure safety parameters:

[0123] Structural deformation monitoring: Install displacement sensors, inclinometers and other monitoring equipment at key locations of the levee to monitor the levee's horizontal displacement, vertical settlement, tilt angle and other structural deformation parameters in real time. By analyzing the changing trends of these parameters, it is possible to determine whether the levee has potential safety hazards such as landslides and collapses. For example, when the horizontal displacement of the levee exceeds a certain range and shows an accelerating trend, it indicates that the levee may be threatened by soil sliding; when the vertical settlement of the levee is uneven and the settlement difference is large, it may lead to uneven settlement of the levee structure, affecting the stability and safety of the levee.

[0124] Seepage monitoring: Use piezometers, flowmeters and other equipment to monitor parameters such as seepage pressure, seepage volume, and seepage velocity inside the levee. Seepage is one of the main factors that lead to seepage damage to levees. By real-time monitoring of seepage parameters, it is possible to promptly detect whether there are leakage channels, pipe bursts and other dangerous conditions inside the levee. For example, when the seepage pressure suddenly increases and the seepage volume increases significantly, it may be that a new leakage channel has appeared inside the levee, and immediate measures need to be taken to deal with it; when the seepage velocity exceeds a certain limit, it indicates that the seepage has a strong scouring effect on the levee soil, which may cause seepage deformation and damage to the levee.

[0125] Material performance monitoring: Use non-destructive testing technologies, such as ultrasonic testing and geological radar, to regularly monitor changes in the physical and mechanical properties of embankment materials, such as soil density, moisture content, and shear strength. Changes in the performance of embankment materials will directly affect the structural safety of the embankment. By monitoring these parameters, aging and softening of embankment materials can be discovered in a timely manner, providing a basis for the maintenance and reinforcement of the embankment. For example, when the moisture content of the soil is too high, resulting in a decrease in shear strength, the anti-sliding stability of the embankment will decrease, making landslide accidents more likely to occur.

[0126] Real-time monitoring of meteorological and hydrological conditions:

[0127] Meteorological monitoring: Meteorological monitoring stations are installed in the areas surrounding the levees to collect meteorological data such as rainfall, rainfall intensity, wind speed, wind direction, temperature, and humidity in real time. Rainfall is one of the main natural factors that cause soil erosion and levee danger. By monitoring rainfall and rainfall intensity, the risk of soil erosion and the flood control pressure of the levees can be estimated in advance. For example, when heavy rain or long-term continuous rainfall is forecast, the system will issue an early warning to remind relevant departments to make flood control preparations. The impact of wind speed and wind direction on levees is mainly reflected in the effect of wind and waves. Higher wind speeds and specific wind directions will intensify the scouring of the levees by the river. Therefore, it is also necessary to monitor the changes in wind speed and wind direction in real time so that wind and wave prevention measures can be taken in time.

[0128] Hydrological monitoring: Hydrological monitoring stations are set up upstream and downstream of rivers and near embankments to monitor hydrological parameters such as water level, flow, flow rate, and sediment content in real time. The height of the water level is directly related to the flood control safety of the embankment. When the water level exceeds the warning level, the system will immediately issue an alarm and predict the possible flood risk based on the speed and trend of the water level rise. Changes in flow and flow rate will affect the scouring force of the river on the embankment. When the flow increases and the flow rate accelerates, the impact of the water flow on the embankment will increase, which is likely to cause soil erosion and structural damage to the embankment. Monitoring of sediment content helps to understand the sediment transport of the river. When the sediment content is too high, it may cause siltation of the riverbed, change the hydrological characteristics of the river, and have an adverse effect on the safety of the embankment.

[0129] The user interaction and decision support unit provides a friendly user interface and supports multi-terminal access, including computers, tablets, and mobile phones. The user interface displays monitoring data and analysis results in the form of charts, maps, and three-dimensional models, making it convenient for users to view and query.

[0130] It is worth noting that the contents not elaborated in detail in the present invention are all prior art and are well known to those skilled in the art.

[0131] Therefore, the present invention adopts the above-mentioned intelligent soil and water conservation monitoring and management system, and improves the accuracy and timeliness of soil and water loss monitoring by comprehensively using multiple monitoring technologies and data analysis methods, providing a strong guarantee for embankment safety.

[0132] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. An intelligent soil and water conservation monitoring and management system, characterized in that: include: The data collection layer is used to collect real-time monitoring data related to the levee, including acoustic sensor network data, machine vision monitoring equipment data, Internet of Things monitoring equipment data, satellite remote sensing monitoring data, and drone inspection data; The data transmission layer uses a wireless communication network to transmit the data acquired by the data collection layer to the data processing and analysis layer; The data processing and analysis layer includes data fusion and preprocessing units, GIS technology application units, and big data analysis and machine learning units, which are used for fusion processing, spatial association, visualization, risk assessment, and trend prediction of multi-source data; The decision support and application layer includes the prevention and control measures generation and optimization unit, the real-time warning and emergency response unit, and the user interaction and decision support unit, which are used to generate prevention and control measures, real-time monitoring and warning, and provide decision support for users.

2. The intelligent soil and water conservation monitoring and management system according to claim 1 is characterized in that: The operation method of the data fusion and preprocessing unit is as follows: S11, cleaning the data collected by the data collection layer, including removing noise data and outliers; S12. Normalize and standardize data from different sources; S13, using data fusion algorithm to fuse data from different sources to obtain embankment soil and water conservation monitoring data; S131. Calculate the reliability weight ω of each data source i : Among them, α represents the adjustment coefficient, which is used to control the rate of weight change; R i represents the historical accuracy of the ith data source for levee monitoring; i = 1, 2, 3, 4, 5, corresponding to acoustic sensor network data, machine vision monitoring equipment data, Internet of Things monitoring equipment data, satellite remote sensing monitoring data, and drone inspection data, respectively; S132. According to the reliability weight of each data source, the data is integrated: Among them, F represents the integrated levee soil and water conservation monitoring data; X i Represents the standardized embankment monitoring data of the i-th data source.

3. The intelligent soil and water conservation monitoring and management system according to claim 2 is characterized in that: The operation method of GIS technology application unit is as follows: S21, using GIS software to spatially associate the levee geographic coordinate information obtained from the data collection layer with the fused levee soil and water conservation monitoring data F, and construct a spatial database for levee soil and water conservation monitoring; S22. Conduct spatial assessment of soil erosion risk along the levee and identify high-risk areas of the levee; S221. Divide the dike into N grid cells. The fused dike soil and water conservation monitoring data of each grid cell n is F. n ; S222. Calculate the risk index Z of levee soil loss for each grid unit n n : WITH n =β1×F n +β2×C; Among them, β1 and β2 represent weight coefficients; C represents other relevant factors; S223, calculate the global spatial autocorrelation coefficient I: Where N is the total number of grid cells along the dike; W is the sum of the spatial weights between all grid cells along the dike; ω ab represents the spatial weight between grid cells a and b along the levee; Z represents the average risk index of all grid cells along the levee.

4. The intelligent soil and water conservation monitoring and management system according to claim 3 is characterized in that: The operation methods of the Big Data Analysis and Machine Learning Unit are as follows: S31, taking the time series data as the input of the multi-head attention mechanism to extract local features related to the levee; F t '=MultiHead(X t ,X t ,X t )=Concat(head1,head2,…head h )W O ; Among them, F t ' represents local features; X t represents time series data, X t =[H t ,E t ,R t ,V t ,S t ,F,I],H t represents the soil moisture of the embankment foundation, E t represents the erosion depth of the dike, R t represents the rainfall near the dike, V t Indicates the vegetation coverage around the embankment, S t Indicates the water flow velocity near the embankment; W O represents the linear transformation matrix of the output; h represents the number of heads; the attention function is as follows: Where Q, K, and V represent query vector, key vector, and value vector respectively; d k represents the dimension of the key vector; S32, extract the local feature F t 'Input into the LSTM layer to learn the long-term dependencies of the time series and predict the risk of levee soil erosion and the safety status of the levee structure; Q t =LSTM(F t '); Among them, Q t represents the output of the LSTM layer, i.e., the predicted levee soil erosion risk and levee structure safety status at time t; S33, using Q-learning algorithm to optimize the decision-making process of the model; Assume state S t is the monitoring data X at the current moment t , action a t is the output Q of the LSTM layer t , then the reward r t as follows: r t =-Loss(a t ,Y t ); Among them, Loss represents the mean square error loss function; Y t Indicates the actual levee soil erosion situation and levee structure safety status; Use the Q-learning algorithm to optimize the Q function and select the optimal action based on the current state.

5. The intelligent soil and water conservation monitoring and management system according to claim 1 is characterized in that: The prevention and control measures generation and optimization unit combines historical prevention and control experience and expert knowledge base, and automatically generates and recommends personalized soil and water conservation and embankment reinforcement strategies based on the output results of the data processing and analysis layer. It also has a feedback optimization mechanism to continuously learn and adjust the recommended strategies based on the prevention and control effect feedback and embankment safety assessment results.

6. The intelligent soil and water conservation monitoring and management system according to claim 1 is characterized in that: The real-time early warning and emergency response unit monitors soil erosion, embankment structure safety parameters, and meteorological and hydrological conditions in real time. Once an abnormality is detected or the preset threshold is reached, the early warning mechanism is immediately triggered and early warning information is sent to management personnel and relevant departments via SMS, email, and APP push.

7. The intelligent soil and water conservation monitoring and management system according to claim 1 is characterized in that: The user interaction and decision support unit provides a friendly user interface and supports multi-terminal access, including computers, tablets, and mobile phones. The user interface displays monitoring data and analysis results in the form of charts, maps, and three-dimensional models, making it convenient for users to view and query.

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