River channel safety monitoring method and system

Abstract

The invention provides a riverway safety monitoring method and system, and relates to the technical field of riverway flow monitoring, and the method comprises the steps: obtaining riverbed elevation distribution data of a target monitoring section area, dividing the target monitoring section area into a plurality of flow measurement subareas, obtaining the section parameters of each flow measurement subarea, and determining the flow measurement subareas; a radar flow velocity sensor for collecting river water flow velocity data is arranged in each flow measurement subarea, and a radar water level sensor for measuring first river water level data is arranged in any flow measurement subarea; constructing a hydrodynamic model, and inputting river water flow velocity data to obtain average flow velocity data of the corresponding flow measurement partitions; calculating second river water level data of the remaining flow measurement subareas according to the first river water level data and the riverbed elevation distribution data, calculating instantaneous flow and accumulated flow according to the section parameters, the first river water level data, the second river water level data and the average flow velocity data, and sending out an alarm signal when the accumulated flow exceeds a preset flow threshold. The accuracy of the monitoring result can be improved.

Description

Technical Field

[0001] This application relates to the technical field of river flow monitoring, and in particular to a river safety monitoring method and system. Background Art

[0002] As an important carrier of water resources, rivers play an irreplaceable and crucial role in many aspects such as flood control, irrigation, shipping, water supply, and ecological maintenance. The safety status of rivers is directly related to the life and property safety of people in surrounding areas, economic development, and the stability of the ecological environment.

[0003] Traditional river monitoring methods often rely on manual measurement or relatively simple equipment, such as the buoy method. There are many problems in the data collection process of traditional river monitoring methods. Manual measurement is not only inefficient but also greatly affected by human factors, making it difficult to ensure the accuracy and consistency of data. Simple equipment has a limited monitoring range and usually can only obtain water flow data at a specific position or limited area of the river, unable to comprehensively and meticulously monitor the entire monitoring section area, resulting in a one-sided understanding of the river water flow situation and making it difficult to accurately reflect the overall water flow dynamics of the river. Summary of the Invention

[0004] In order to improve the accuracy of river safety monitoring results, this application provides a river safety monitoring method and system.

[0005] In a first aspect, this application provides a river safety monitoring method, adopting the following technical solution: A river safety monitoring method includes the following steps: Obtain the riverbed elevation distribution data of the target monitoring section area, divide the target monitoring section area into multiple flow measurement sub-areas, obtain the section parameters of each flow measurement sub-area, a radar velocity sensor for collecting river water velocity data is set in each flow measurement sub-area, and a radar water level sensor for measuring the first river water level data is set in any one of the flow measurement sub-areas; Construct a hydrodynamic model, input the river water velocity data of each flow measurement sub-area into the hydrodynamic model, and obtain the average velocity data of each flow measurement sub-area; Calculate the second river water level data of the remaining flow measurement sub-areas according to the first river water level data and the riverbed elevation distribution data; calculate the instantaneous flow according to the section parameters, the first river water level data, the second river water level data, and the average velocity data, calculate the cumulative flow according to the instantaneous flow, and send an alarm signal when the cumulative flow exceeds the preset flow threshold.

[0006] This application obtains the riverbed elevation distribution data of the target monitoring cross-section area and divides it into multiple flow measurement zones. Subsequently, this application constructs a hydrodynamic model, inputs the river flow velocity data into the model, and obtains the average flow velocity data of the corresponding flow measurement zones. The hydrodynamic model can comprehensively consider various physical characteristics of the river, process and analyze the flow velocity data collected by the sensors, obtain a more representative average flow velocity, and thus reduce the large deviation of the flow measurement data caused by the uneven flow velocity and irregular cross-section of the river, making the analysis of the water flow conditions at different positions of the river more accurate.

[0007] This application also sets a radar water level sensor in any one of the flow measurement zones to measure the first river water level data, and then calculates the second river water level data of the remaining flow measurement zones according to the first river water level data and the riverbed elevation distribution data. Through the above technical solution, this application can deduce the water level conditions of the flow measurement zones where no water level sensor is set, expand the monitoring range of the water level data, make the water level information of the entire monitoring area more complete, solve the problem that the water level cannot be comprehensively monitored due to the limitation of the number of sensors, achieve full coverage of the water level data in the monitoring area, and provide complete water level parameters for flow calculation.

[0008] Subsequently, this application calculates the instantaneous flow rate according to the cross-section parameters, the first river water level data, the second river water level data, and the average flow velocity data, and calculates the cumulative flow rate according to the instantaneous flow rate. By adopting the above solution, this application can accurately grasp the flow rate conditions of the river at different time periods. When the cumulative flow rate exceeds the preset flow rate threshold, an alarm signal is sent to timely remind relevant personnel to take measures. This application improves the traditional method of measuring the water flow velocity at a certain position in the target monitoring cross-section area by dividing the target monitoring cross-section area into multiple flow measurement zones, measuring the river flow velocity data of each flow measurement zone, obtaining the average flow velocity data based on these river flow velocity data, and finally calculating the cumulative flow rate based on the above data, making the calculation result more accurate and improving the accuracy of river channel safety monitoring to a certain extent.

[0009] Optionally, the method further includes: Based on all the river flow velocity data, the surface flow velocity data of the target monitoring cross-section area is calculated by using the interpolation method, the vertical flow velocity data of each position in the target monitoring cross-section area is calculated according to the preset flow velocity distribution calculation model, and a vertical flow velocity data change curve is drawn according to the vertical flow velocity data. The horizontal axis of the vertical flow velocity data change curve is the shortest distance to any river bank, and the vertical axis is the vertical flow velocity data; Calculate the first derivative of the vertical flow velocity data change curve with respect to distance, discretize the first derivative to obtain the discretization result, normalize the discretization result to obtain the first data, calculate the vertical average value based on the first data and the vertical flow velocity data, and update the vertical average value to the river flow velocity data.

[0010] Based on all the river flow velocity data, the present application uses the interpolation method to calculate the surface flow velocity data of the target monitoring section area. The interpolation method can utilize the known river flow velocity data to reasonably estimate the surface flow velocity at other positions in the monitoring area, enabling a more comprehensive understanding of the surface flow velocity distribution of the entire section area, helping to more accurately grasp the water flow conditions on the river surface, and improving the accuracy of overall monitoring and analysis.

[0011] Subsequently, the present application calculates the vertical flow velocity data at each position in the target monitoring section area according to the flow velocity distribution calculation model. The flow velocity distribution calculation model comprehensively considers the physical characteristics of the river and the principles of water flow dynamics, and can calculate the vertical flow velocity at different positions. By adopting the above solution, the present application can extend the single surface flow velocity to multiple vertical levels, more realistically reflecting the internal water flow structure of the river, and helping to more deeply understand the characteristics of the vertical flow velocity distribution of the river.

[0012] Subsequently, the present application draws a vertical flow velocity data change curve based on the vertical flow velocity data and calculates the first derivative value of the vertical flow velocity data change curve. The first derivative can reflect the change rate of the vertical flow velocity. Subsequently, the present application discretizes the first derivative value, converts the continuous derivative values into discrete data points to obtain the first derivative value, and then normalizes the first derivative value to obtain the first data, thereby converting the discretization result into the range of 0 - 1. Finally, the present application calculates the vertical average value based on the first data and the vertical flow velocity data. The first data reflects the characteristics of the vertical flow velocity change. Combining the vertical flow velocity data to calculate the vertical average value can comprehensively consider the magnitude and change of the flow velocity itself, making the calculated vertical average value more accurately represent the overall level of the entire vertical flow velocity, providing a more accurate and comprehensive basis for updating the river flow velocity data. By adopting the above solution, the present application can optimize and update the river flow velocity data to make the data more conform to the actual flow velocity characteristics of the river.

[0013] Optionally, the method further includes: Set at least one reference point, multiple working base points, and multiple observation points on the riverbanks on both sides of the target monitoring section area. Construct a three-dimensional coordinate system with the position of the reference point as the coordinate origin, and determine the original three-dimensional coordinates of each working base point and the original three-dimensional coordinates of each observation point; Select at least one reference point and at least one working reference point to construct a monitoring reference network, retest the three-dimensional coordinates of each working reference point after a preset time, obtain the remeasured three-dimensional coordinates of each working reference point, and obtain the displacement change amount in the horizontal direction and the displacement change amount in the vertical direction of each working reference point based on the original three-dimensional coordinates and the remeasured three-dimensional coordinates of each working reference point; The three-dimensional displacement of each working base point is calculated according to the displacement change in the horizontal direction and the displacement change in the vertical direction of each working base point, and the three-dimensional displacement of each working base point is judged in turn whether it is greater than the preset three-dimensional displacement threshold. If so, a working base point alarm signal is issued; if not, no processing is performed.

[0014] This application sets benchmark points, working base points and observation points on the river banks on both sides of the target monitoring section area, and constructs a three-dimensional coordinate system with the benchmark points as the coordinate origin, determines the original three-dimensional coordinates of each point, and realizes the precise spatial positioning of key points in the monitoring area. It can clearly describe the positional relationship of each point in three-dimensional space, which helps to fully understand the spatial layout of the monitoring area.

[0015] Subsequently, the present application constructs the benchmark point and at least one working base point as a monitoring benchmark network to form a relatively stable monitoring system. The benchmark point serves as the reference benchmark of the entire monitoring system, and the working base point serves as the key node of monitoring. Through the above combination, long-term and stable monitoring of the displacement changes in the monitoring area can be achieved. After a preset time, the three-dimensional coordinates of each working base point are retested to obtain the re-measured three-dimensional coordinates. By comparing with the original three-dimensional coordinates, the displacement changes of the working base point in the horizontal and vertical directions can be captured. This regular re-measurement method can timely discover the slight displacement of the riverbed, provide dynamic data for evaluating the stability of the riverbed, and help to discover potential safety hazards in advance. Subsequently, the present application calculates the three-dimensional displacement according to the displacement changes of each working base point in the horizontal and vertical directions, and judges in turn whether the three-dimensional displacement of each working base point is greater than the preset three-dimensional displacement threshold, and sends an alarm signal or does not process it according to the judgment result. The present application adopts a threshold-based alarm mechanism to timely identify the working base point with abnormal displacement, send an alarm signal in advance, and remind relevant personnel to take measures to deal with it, thereby ensuring the safety of the monitoring area and the surrounding environment.

[0016] Optionally, when the three-dimensional displacement of the working base point is not greater than a preset three-dimensional displacement threshold, the method further includes: Acquire the remeasured three-dimensional coordinates of the observation point, wherein the remeasured three-dimensional coordinates of the observation point and the remeasured three-dimensional coordinates of the working base point have the same timestamp; The remeasured three-dimensional coordinates of the observation point are updated based on the displacement changes in the horizontal direction and the vertical direction of each working base point to obtain the absolute three-dimensional coordinates of the observation point. The three-dimensional displacement of each observation point is calculated according to the original three-dimensional coordinates and the absolute three-dimensional coordinates of the observation point, and it is determined in turn whether the three-dimensional displacement of each observation point is greater than the preset three-dimensional displacement threshold. If so, an observation point alarm signal is issued; if not, no processing is performed.

[0017] When the three-dimensional displacement of the working base point is not greater than the preset three-dimensional displacement threshold, the present application obtains the re-measured three-dimensional coordinates of the observation point, and the re-measured three-dimensional coordinates of the observation point have the same timestamp as the re-measured three-dimensional coordinates of the working base point. The data acquisition method with the same timestamp enables the working base point and the observation point data to be collected at the same time, eliminating the measurement error caused by the time difference, making the subsequent displacement calculation and analysis based on these data more accurate and reliable, and can truly reflect the positional relationship between the working base point and the observation point at the same time point.

[0018] Subsequently, the present application updates the remeasured three-dimensional coordinates of the observation point based on the displacement change of each working base point in the horizontal direction and the displacement change in the vertical direction, and obtains the absolute three-dimensional coordinates of the observation point. By updating the coordinates of the observation point by considering the displacement change of the working base point, the relative coordinates of the observation point can be converted into absolute coordinates, so that the position information of the observation point can be accurately represented in a unified coordinate system, reducing the influence of the displacement of the working base point on the position description of the observation point, and making the position information of the observation point more accurate.

[0019] Subsequently, the present application calculates the three-dimensional displacement of each observation point based on the original three-dimensional coordinates and absolute three-dimensional coordinates of the observation point. Through the above scheme, the present application can accurately calculate the displacement of the observation point in three-dimensional space, so as to fully reflect the displacement changes of the observation point in the horizontal and vertical directions, and then judge in turn whether the three-dimensional displacement of each observation point is greater than the preset three-dimensional displacement threshold, and take corresponding measures according to the judgment results. The present application realizes the real-time monitoring and early warning function of the displacement of the observation point, and can timely discover potential safety hazards.

[0020] Optionally, when the three-dimensional displacement of the observation point is not greater than a preset three-dimensional displacement threshold, the method further includes: Construct a statistical analysis model, input the re-measured three-dimensional coordinates of each working base point and the three-dimensional displacement of each working base point into the statistical analysis model in sequence, and obtain the predicted value of the three-dimensional displacement of each working base point at the next moment; input the absolute three-dimensional coordinates of each observation point and the three-dimensional displacement of each observation point into the statistical analysis model in sequence, and obtain the predicted value of the three-dimensional displacement of each observation point at the next moment; Determine whether the predicted value of the three-dimensional displacement of each working base point at the next moment and the predicted value of the three-dimensional displacement of each observation point at the next moment are consistent with expectations. If so, no processing is performed; if not, an early warning signal is output.

[0021] This application constructs a statistical analysis model and inputs the remeasured three-dimensional coordinates and three-dimensional displacement of the working base point or observation point into the model to obtain the predicted value of the three-dimensional displacement of each working base point or each observation point at the next moment. The above scheme mines the potential laws and trends in the data through the statistical analysis model. The statistical analysis model can learn the displacement change patterns of different base points and observation points under different coordinates and displacement conditions.

[0022] Subsequently, the present application predicts the three-dimensional displacement of the working base point and the observation point at the next moment respectively, and determines whether the predicted values ​​of the three-dimensional displacement of each working base point and the observation point at the next moment are in line with expectations. It can quickly identify those displacement changes that deviate from the normal range, and realize a preliminary estimate of future displacement changes. Compared with relying solely on real-time monitoring data, the introduction of predicted values ​​can discover potential abnormal displacement trends in advance.

[0023] Optionally, the method further comprises: Obtain the historical three-dimensional displacement of the observation point and the real-time three-dimensional displacement at the current moment, arrange the historical three-dimensional displacement in the order of acquisition time, obtain the historical three-dimensional displacement sequence, and draw the historical three-dimensional displacement curve based on the historical three-dimensional displacement sequence; Establish a safety monitoring model for fitting the historical three-dimensional displacement curve, and obtain the theoretical three-dimensional displacement at the current moment based on the safety monitoring model; It is determined whether the difference between the real-time three-dimensional displacement and the theoretical three-dimensional displacement is greater than the preset difference threshold. If so, a deviation alarm signal is output; if not, no processing is performed.

[0024] The present application obtains the historical three-dimensional displacement of the observation point and arranges it in chronological order of acquisition time to form a historical three-dimensional displacement sequence. Subsequently, the present application draws a historical three-dimensional displacement curve based on the historical three-dimensional displacement sequence, and establishes a safety monitoring model for fitting the historical three-dimensional displacement curve. By fitting, the present application can find a mathematical expression to describe the characteristics of the historical displacement curve, so as to better understand the law of displacement change of the observation point. By establishing a safety monitoring model for fitting the historical three-dimensional displacement curve, the present application can predict the theoretical three-dimensional displacement at the current moment based on historical data. The safety monitoring model provides a theoretical three-dimensional displacement for the current moment by learning the law of historical displacement changes. Subsequently, the present application promptly discovers abnormal displacement of the observation point by judging whether the difference between the real-time three-dimensional displacement and the theoretical three-dimensional displacement is greater than a preset threshold.

[0025] Optionally, the method further includes: obtaining three-dimensional point cloud data of the river channel by using a lidar carried by a drone, constructing a three-dimensional point cloud model of the river channel based on the three-dimensional point cloud data, formulating an initial inspection route of the drone according to the three-dimensional point cloud model and the inspection target by using a path planning algorithm, controlling the drone to inspect the river channel according to the initial inspection route, collecting meteorological data and water quality data during the inspection process, and uploading the meteorological data and water quality data to the cloud.

[0026] This application uses a lidar carried by a drone to obtain three-dimensional point cloud data of the river channel. The lidar can quickly and accurately obtain the three-dimensional spatial information of the river channel surface. Then, based on the three-dimensional point cloud data, a three-dimensional point cloud model of the river channel is constructed, converting the abstract river channel data into an intuitive three-dimensional visualization model. The three-dimensional point cloud model can clearly display the morphology, structure, and spatial relationships of different regions of the river channel, enabling relevant personnel to more intuitively observe and analyze the characteristics of the river channel. Subsequently, according to the three-dimensional point cloud model and the inspection target, this application uses a path planning algorithm to formulate the initial inspection route of the drone. The three-dimensional point cloud model provides detailed terrain information of the river channel. Combining with the inspection target (such as specific areas, key facilities, etc.), the path planning algorithm can comprehensively consider factors such as flight distance, obstacle avoidance, and task priority to formulate a reasonable and efficient inspection route, enabling the drone to comprehensively and accurately cover the inspection area and improving the inspection efficiency and quality.

[0027] Subsequently, control the drone to inspect the river channel according to the initial inspection route. This application can dynamically monitor the river channel during real-time flight. The drone can quickly reach the designated area and timely obtain the latest information of the river channel, and can quickly respond to some emergencies (such as floods, pollution incidents, etc.), providing timely data support for emergency disposal. During the inspection process, sensors carried by the drone are used to collect meteorological data and water quality data, realizing the comprehensive monitoring of the river channel environment. The collection of meteorological data (such as wind speed, wind direction, temperature, humidity, etc.) and water quality data (such as pH value, dissolved oxygen, turbidity, etc.) can comprehensively understand the ecological environment status of the river channel and provide multi-faceted data basis for river channel management, water resource protection, etc.

[0028] Optionally, the method further includes: using sensors carried by an unmanned ship to sense and model the water surface and boundary environment of the water area to obtain a river channel model, marking the water area contour and the positions of obstacles in the river channel model to obtain a marked river channel model, dividing the river channel into multiple sub-regions based on the marked river channel model, and performing water area cleaning in each sub-region respectively.

[0029] This application uses sensors carried by unmanned boats to sense and model the water surface and boundary environment of water areas. The sensors can obtain various information of water areas in real time and accurately, such as water depth, water flow velocity, water surface width, and boundary terrain, etc. Compared with traditional manual measurement methods, the sensors of unmanned boats have higher efficiency and accuracy, can cover a wider water area range, and provide a reliable data basis for constructing an accurate river channel model.

[0030] Subsequently, this application obtains a river channel model based on the sensed data. This model intuitively shows the overall shape and structure of the water area. Then, this application marks the water area contour and the positions of obstacles in the river channel model, making the information of the river channel model richer and clearer. The marking of the water area contour can clearly define the scope of the water area, while the marking of the obstacle positions helps to identify potential dangerous areas in the water area or factors affecting operations such as navigation and cleaning. Then, this application divides the river channel into multiple sub-areas based on the marked river channel model, decomposing the originally complex river channel cleaning task into multiple relatively independent small tasks. This way of task decomposition makes the cleaning operation more orderly and efficient, facilitating the arrangement of cleaning equipment and personnel, and improving the flexibility and operability of the operation. By cleaning the water area in each sub-area respectively, this application can adopt appropriate cleaning methods and equipment according to the specific conditions of each sub-area (such as pollution degree, obstacle distribution, etc.) to achieve precise cleaning. This targeted cleaning method can more effectively remove pollutants in the water area, improve the cleaning effect, and reduce waste of resources.

[0031] In a second aspect, this application provides a river channel safety monitoring system, adopting the following technical solution: A river channel safety monitoring system includes: a processor and a memory, Program code is stored in the memory; When the processor calls the program code in the memory, it executes the steps of the method described in the first aspect.

[0032] In summary, this application includes at least one of the following beneficial technical effects: 1. This application improves the traditional method of measuring water velocity at a certain position in the target monitoring section area by dividing the target monitoring section area into multiple flow measurement sub-areas, measuring the river water velocity data of each flow measurement sub-area, obtaining average velocity data based on these river water velocity data, and finally calculating the cumulative flow based on the above data, making the calculation result more accurate and improving the accuracy of river channel safety monitoring to a certain extent.

[0033] 2. This application can accurately calculate the displacement of the observation point in three-dimensional space, thus comprehensively reflecting the displacement changes of the observation point in the horizontal and vertical directions. Then, it sequentially determines whether the three-dimensional displacement of each observation point is greater than the preset three-dimensional displacement threshold, and takes corresponding measures according to the judgment results. This application realizes the real-time monitoring and early warning functions for the displacement of the observation point, and can timely detect potential safety hazards. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 is the flowchart of data acquisition in S11 to flow rate calculation and alarm in Embodiment 1 of this application; Figure 2 is the flowchart of data acquisition in S11 to data update in S14 in Embodiment 1 of this application; Figure 3 is the flowchart of Embodiment 2 of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] The following is a further detailed description of this application in conjunction with Figures 1 to 3 to this application.

[0036] Embodiment 1: This embodiment discloses a river safety monitoring method. Referring to Figure 1 , the method includes: S11 data acquisition, S12 modeling, S13 flow rate calculation and alarm. In this embodiment, first, the riverbed elevation distribution data of the target monitoring section area is obtained, and the target monitoring section area is divided into multiple flow measurement partitions. The cross-section parameters of each flow measurement partition are obtained, and radar velocity sensors for collecting river water velocity data are set in each flow measurement partition, and a radar water level sensor for measuring the first river water level data is set in any one of the flow measurement partitions. Then, a hydrodynamic model is constructed, and the average velocity data of the corresponding flow measurement partition is obtained by inputting the river water velocity data. Then, the second river water level data of the remaining flow measurement partitions is calculated based on the first river water level data and the riverbed elevation distribution data, and then the instantaneous flow rate and cumulative flow rate are calculated according to the cross-section parameters, the first river water level data, the second river water level data, and the average velocity data. Finally, an alarm signal is sent when the cumulative flow rate exceeds the preset flow rate threshold. The execution process of each step in this embodiment is as follows: S11 data acquisition, the riverbed elevation distribution data acquisition work is carried out in the target monitoring section area. The riverbed elevation distribution data includes the riverbed elevation data of each position in the target monitoring section area. The acquisition method of the riverbed elevation data is as follows: The multibeam sounding device emits multiple sound beams to the bottom of the water and receives the reflected sound signals, so as to quickly and accurately obtain the elevation data of different positions at the bottom of the water, and then construct the three-dimensional distribution data of the riverbed elevation.

[0037] In other embodiments, the elevation data of each point of the riverbed in the target monitoring section area can also be measured by a single-beam sounding instrument.

[0038] The target monitoring cross-section area is divided into multiple flow measurement sub-areas. For example, if the river channel has different topographic features such as shoals or deep troughs, the river channel can be divided into different areas according to these features; or according to the changes in water flow velocity and direction, the areas with similar water flow characteristics are divided into the same flow measurement sub-area. In this embodiment, the confluence of the river channels is used as the target monitoring cross-section area, and the target monitoring cross-section area is divided into multiple flow measurement areas.

[0039] For each divided flow measurement sub-area, its cross-section parameters are obtained in detail. These parameters include cross-section shape, cross-section area, etc. The cross-section shape can be determined by field measurement combined with the riverbed elevation distribution data. For example, an unmanned aerial vehicle equipped with a high-definition camera is used to photograph the river channel cross-section, and then by combining the riverbed elevation distribution data, the contour information of the cross-section is extracted, and thus the shape of each flow measurement sub-area is obtained. The cross-section area can be calculated by measuring the coordinates of each point of the cross-section and using methods such as mathematical integration.

[0040] Radar flow velocity sensors for collecting river water flow velocity data are installed in each flow measurement sub-area. The radar flow velocity sensors work based on the Doppler effect principle. It emits electromagnetic waves to the water surface and receives the reflected signals, and calculates the river water flow velocity data by analyzing the frequency change of the reflected signals.

[0041] At the same time, in any one of the flow measurement sub-areas, a radar water level sensor for measuring the first river water level data is installed. The radar water level sensor also uses electromagnetic wave technology. By measuring the time interval from the emission to the reception of the electromagnetic wave and combining the propagation speed of the electromagnetic wave, the first river water level data is calculated.

[0042] S12 Modeling. The hydrodynamic model is an important means to simulate the law of river water flow movement and obtain more accurate water flow information.

[0043] The hydrodynamic model of this embodiment is based on the basic principles of fluid mechanics, considering various factors such as the topography, roughness, and water flow boundary conditions of the river channel, and describes the movement process of water flow in the river channel through mathematical equations. The river water flow velocity data is input into the hydrodynamic model to obtain the average flow velocity data of the corresponding flow measurement sub-area of the river water flow velocity data.

[0044] The calculation model of the hydrodynamic model of this embodiment is as follows: ; Wherein, is the river water flow velocity data of the i-th flow measurement sub-area, is the average flow velocity data for the i-th flow measurement sub-region. a and b are empirical coefficients, which are affected by channel morphology (rectangular, trapezoidal, parabolic, etc.), flow conditions (laminar flow, turbulent flow), riverbed roughness, etc. The values of a and b are determined by conducting a large number of actual flow velocity measurements on different channels and using methods such as regression analysis to determine the coefficient values suitable for a specific channel. The value ranges of both a and b are 1.0 - 1.5. In this embodiment, after fitting, the value of a is determined to be 1.23 and the value of b is determined to be 1.01.

[0045] In other embodiments, the hydrodynamic model can also adopt the following calculation model: ; ; ; Among them, is the flow velocity at a distance from the riverbed of the i-th flow measurement sub-region , K is the von Kármán constant, with a value of 0.41, is the riverbed roughness height, is the river water height of the i-th flow measurement sub-region, and its value is equal to the first river water level data or the second river water level data; is the river flow velocity data of the i-th flow measurement sub-region.

[0046] In other embodiments, if a low precision of the calculation result is required, the river flow velocity data of each flow measurement sub-region can be used to replace the average flow velocity data of each flow measurement sub-region for the calculation of subsequent steps.

[0047] S13 Flow calculation and alarm. According to the first river water level data measured in a certain flow measurement sub-region, combined with the previously obtained riverbed elevation distribution data, calculate the second river water level data of the remaining flow measurement sub-regions. Since there is a hydraulic connection between the flow measurement sub-regions during the flow of the river channel water, the water level will change with the changes in the riverbed topography and flow conditions. Given the known first water level data and the riverbed elevation distribution data, the riverbed elevation distribution data includes the riverbed elevation data of each flow measurement sub-region. Therefore, the water level data of other flow measurement sub-regions can be deduced from the above content.

[0048] ; Among them, is the river water level data of the j-th flow measurement sub-region, that is, the j-th second river water level data; is the riverbed elevation data of the j-th flow measurement sub-region; is the riverbed elevation data of the i-th flow measurement sub-region; is the river water level data of the i-th flow measurement sub-region, that is, the first river water level data.

[0049] Calculate the instantaneous flow rate based on the cross-section parameters (such as cross-sectional area) of each flow measurement sub-region, the first river water level data, the calculated second river water level data, and the average flow velocity data obtained from the hydrodynamic model.

[0050] The calculation formula for the instantaneous flow rate is that the flow rate is equal to the cross-sectional area multiplied by the average flow velocity data. In actual calculation, it is necessary to calculate for each flow measurement sub-region separately, and then add up the instantaneous flow rates of each sub-region to obtain the total instantaneous flow rate of the entire target monitoring cross-section area. The above calculation process is already mature and will not be elaborated here. The calculation of the instantaneous flow rate in this embodiment fully considers the differences in water flow characteristics in different regions of the river channel, and can more accurately reflect the actual flow rate situation of the river channel at a certain moment.

[0051] Calculate the cumulative flow rate by methods such as integration based on the calculated instantaneous flow rate data. The cumulative flow rate reflects the total amount of water passing through the target monitoring cross-section area within a certain period of time.

[0052] During the real-time monitoring process, when the cumulative flow rate exceeds the preset flow rate threshold, an alarm signal is automatically sent. The alarm signal can be conveyed to relevant personnel through various means, such as text messages, emails, sound and light alarms, etc., so as to take corresponding measures in a timely manner, such as strengthening flood control inspections, adjusting the water resource allocation plan, etc., thereby ensuring the safety and normal water use requirements of the river channel and the surrounding areas.

[0053] Refer to Figure 2 , in other embodiments, the method further includes: S14 Data update. In actual water flow monitoring, due to factors such as equipment installation location and cost limitations, it may not be possible to directly measure the river water flow velocity data at every location in the target monitoring cross-section area. The interpolation method is a method of estimating data at unknown locations through known data points. Its purpose is to use limited measured flow velocity data to accurately estimate the surface flow velocity of the entire target monitoring cross-section area, so as to obtain a more comprehensive surface flow velocity distribution in this area.

[0054] This embodiment adopts the inverse distance weighted interpolation method. The principle of this method is to assume that the flow velocity value at the unknown location is inversely proportional to the flow velocity values of the known data points, and the data points closer have a greater influence on the unknown location. During specific calculation, different weights are assigned according to the distances between the known data points and the unknown location, and then the flow velocity value at the unknown location is estimated through weighted average.

[0055] Suppose there are 5 measured flow velocity data points in the target monitoring section area, located at different positions, and the coordinates and corresponding flow velocity values of these data points are known. Taking the inverse distance weighted interpolation method as an example, for any unmeasured point in the area, calculate its distances from the 5 measured points, assign weights according to the reciprocals of the distances, and then perform weighted averaging to obtain the estimated surface flow velocity value of the unmeasured point. Repeat the above process to perform interpolation calculations on the entire target monitoring section area, and finally obtain the surface flow velocity data of this area.

[0056] In other embodiments, the water level data of the remaining flow measurement sub-areas can also be calculated by the linear interpolation method.

[0057] The distribution of flow velocity in the vertical direction is usually affected by various factors, such as the dynamic characteristics of the water flow, the riverbed topography, the boundary conditions, etc. Common flow velocity distribution calculation models include the logarithmic flow velocity distribution model, the exponential flow velocity distribution model, etc.

[0058] Logarithmic flow velocity distribution model: This model is based on fluid mechanics theory, assuming that the water flow is under the combined action of viscous force and gravity in the vertical direction. The flow velocity is relatively small near the riverbed due to the influence of viscous force, and as the height increases, the flow velocity gradually increases and conforms to a logarithmic relationship.

[0059] The calculation model of the logarithmic flow velocity distribution model is as follows: ; ; Among them, is the flow velocity at the position from the riverbed in the i-th flow measurement sub-area, K is the von Karman constant, with a value of 0.41, is the riverbed roughness height, is the river water flow velocity data of the i-th flow measurement sub-area.

[0060] Exponential flow velocity distribution model: This model assumes that the vertical flow velocity distribution conforms to an exponential law.

[0061] The calculation model of the exponential flow velocity distribution model is as follows: ; Among them, is the flow velocity at the position from the riverbed in the i-th flow measurement sub-area, is the river water flow velocity data of the i-th flow measurement sub-area, and a is an empirical coefficient, the same as in S12 modeling, with a value of 1.23.

[0062] Taking the shortest distance from each position in the target monitoring section area to any river bank as the abscissa and the corresponding vertical flow velocity data as the ordinate, a scatter plot is drawn in the coordinate system. According to the distribution trend of the above scatter points, a suitable curve fitting method (such as polynomial fitting, exponential fitting, etc.) is selected to draw the curve of the change of vertical flow velocity data.

[0063] Subsequently, calculate the first derivative of the curve of the change of vertical flow velocity data. The first derivative of the curve of the change of vertical flow velocity data represents the change rate of the vertical flow velocity with respect to the distance. By calculating the first derivative, it is possible to understand how fast the vertical flow velocity changes in different flow measurement sub-areas. In this embodiment, the numerical differentiation method is used to calculate the first derivative of the curve of the change of vertical flow velocity data.

[0064] Since the first derivative is continuous data obtained through numerical calculation, discretization processing can convert these first derivatives into a discrete form that is more convenient to process and analyze. At the same time, discretization processing can also reduce the amount of data and improve the calculation efficiency.

[0065] In this embodiment, according to the division method of each flow measurement sub-area, the range of the first derivative values is divided into several intervals, and then each first derivative value is mapped to the corresponding interval, and the interval number or representative value is used to represent the first derivative value.

[0066] For example, there are 10 flow measurement sub-areas (the length of the target monitoring section area is 50 m, and each 5 m is divided into a flow measurement sub-area, so there are 10 flow measurement sub-areas). Similarly, the range of the first derivative values is divided into 10 number segments (0 - 5 m corresponds to one number segment, and so on, 10 number segments are obtained), and the first derivative values of each number segment are assigned to the corresponding flow measurement sub-areas to obtain the result of discretization processing. Determine whether there is a first derivative value of 0 in each flow measurement sub-area. If so, divide the flow measurement sub-area again to obtain multiple new flow measurement sub-areas, and re-execute S11 data acquisition; if not, no processing is performed.

[0067] For example, if there are c first derivative values of 0 in the i-th flow measurement sub-area, then the i-th flow measurement sub-area needs to be divided into c + 1 new flow measurement sub-areas.

[0068] Normalization processing can map the data range of the discretization processing result to [0, 1], and record the discretization processing result after normalization as the first data.

[0069] Multiply the first data as the weight by the corresponding vertical flow velocity data, then sum all the products, and then divide by the sum of the first data to obtain the vertical average value.

[0070] Update the vertical average value to the river flow velocity data. The vertical average value can reflect the overall characteristics of the river flow velocity in the entire target monitoring section area.

[0071] Example 2: Refer to Figure 3 , the difference between this embodiment and Embodiment 1 is that the method further includes: S21 Determine the original coordinates, and set at least one reference point, multiple working reference points, and multiple observation points on the riverbanks on both sides of the target monitoring section area.

[0072] Reference point: Select at least one reference point on the riverbanks on both sides of the target monitoring section area. The reference point is the basic reference point of the entire coordinate system, and it is required to have high stability and not be easily affected by external factors (such as water flow scouring, riverbed deformation, etc.). For example, a reference point can be selected in an area where the riverbed rock is relatively hard and the geological structure is stable. Generally, a deeply buried concrete pile is used, and a metal marker is embedded in the pile to ensure that its position remains fixed for a long time as much as possible.

[0073] Working reference point: Set multiple working reference points near the reference point and within the monitoring section area. The role of the working reference point is to serve as a transition point between the reference point and the observation point for transmitting coordinate information, and it can also be an important reference point for monitoring riverbed deformation. The setting of the working reference point needs to consider the uniformity and rationality of its distribution to comprehensively cover the monitoring section area. For example, set a working reference point at a certain distance upstream, downstream, and on both sides of the monitoring section area, using a shallowly buried concrete pile or a metal marker.

[0074] Observation point: Set multiple observation points at key positions in the target monitoring section area. The observation points are used to directly monitor the deformation of the riverbed, and their positions should be selected according to the monitoring purpose and key points. For example, encrypt the observation points in areas where deformations such as landslides and settlements may occur, and surface markers or embedded sensors can be used.

[0075] Take the position of the reference point as the coordinate origin, determine the directions of the X, Y, and Z axes according to the geographical directions, and construct a three-dimensional rectangular coordinate system. In this embodiment, the X-axis is set as the flowing direction of the river, the Y-axis is perpendicular to the flowing direction of the river, and the Z-axis is perpendicular to the riverbed surface and upward. In other embodiments, the directions of the coordinate axes can also be set according to requirements.

[0076] Use high-precision measuring instruments (such as total stations, GPS, etc.) to measure each working reference point and observation point to determine their original three-dimensional coordinates in the constructed three-dimensional coordinate system. During the measurement process, multiple observations and data processing are required to improve the accuracy of the coordinates. For example, when using a total station for measurement, multiple measurements need to be carried out at different time periods and under different meteorological conditions, and then the average value is taken as the final original coordinate.

[0077] S22 Determine the change amount, and construct a monitoring reference network with the reference point and at least one working reference point. The role of the monitoring reference network is to provide a stable reference framework for monitoring the displacement changes of the working reference points.

[0078] After a preset time period (such as half a year), retest the three-dimensional coordinates of each working reference point using the same measuring instrument and method to obtain the retested three-dimensional coordinates of each working reference point.

[0079] Based on the original three-dimensional coordinates (X0, Y0, Z0) and the retested three-dimensional coordinates (X1, Y1, Z1) of each working reference point, calculate the displacement changes ΔX = X1 - X0 and ΔY = Y1 - Y0 of each working reference point in the horizontal direction (X-axis and Y-axis directions), and the displacement change ΔZ = Z1 - Z0 in the vertical direction (Z-axis direction).

[0080] S23 Analyze the three-dimensional displacement amount, calculate the three-dimensional displacement amount of each working reference point according to the displacement changes of each working reference point in the horizontal direction and the vertical direction, and sequentially determine whether the three-dimensional displacement amount of each working reference point is greater than the preset three-dimensional displacement amount threshold. If so, send a warning signal for the working reference point; if not, execute S24 to retest the coordinates of the observation points.

[0081] According to the displacement changes ΔX and ΔY of each working reference point in the horizontal direction and the displacement change ΔZ in the vertical direction, calculate the three-dimensional displacement amount D of each working reference point. The calculation model of the three-dimensional displacement amount D is as follows: 。

[0082] Sequentially determine whether the three-dimensional displacement amount D of each working reference point is greater than the preset three-dimensional displacement amount threshold. The preset three-dimensional displacement amount threshold is comprehensively determined according to factors such as the geological conditions of the riverbed, monitoring purposes, and safety requirements. If the three-dimensional displacement amount of a certain working reference point is greater than the threshold, send a warning signal for the working reference point to prompt relevant personnel to check and analyze the working reference point and its surrounding area; if the three-dimensional displacement amounts of all working reference points are not greater than the threshold, execute S24 to retest the coordinates of the observation points.

[0083] S24 Retest the coordinates of the observation points to obtain the retested three-dimensional coordinates (X1’, Y1’, Z1’) of the observation points. The retested three-dimensional coordinates of the observation points have the same timestamp as the retested three-dimensional coordinates of the working reference points.

[0084] Based on the displacement changes of each working reference point in the horizontal direction and the vertical direction, update the retested three-dimensional coordinates of the observation points to obtain the absolute three-dimensional coordinates (X2, Y2, Z2) of the observation points.

[0085] ; According to the original three-dimensional coordinates (X3, Y3, Z3) and absolute three-dimensional coordinates (X2, Y2, Z2) of the observation point, the three-dimensional displacement D' corresponding to each observation point is calculated. The calculation model of the three-dimensional displacement D' is as follows: ; It is determined in turn whether the three-dimensional displacement D' of each observation point is greater than the preset three-dimensional displacement threshold. If the three-dimensional displacement of a certain observation point is greater than the preset three-dimensional displacement threshold, an observation point alarm signal is issued to prompt relevant personnel to focus on monitoring and processing the observation point and its surrounding areas; if the three-dimensional displacement of all observation points is not greater than the threshold, the prediction in S25 is executed.

[0086] S25 prediction, building a statistical analysis model. The statistical analysis model can adopt a time series analysis model (such as ARIMA model, BI-LSTM model), a regression analysis model, etc. The above models can predict the changing trend at the next moment based on historical data and current data.

[0087] The re-measured 3D coordinates of each work base point and the 3D displacement of each work base point are input into the constructed statistical analysis model in sequence. The re-measured 3D coordinates can provide the spatial position information of the work base point, while the 3D displacement reflects the position change of the work base point at different times. The model will train these input data, adjust the model parameters by learning the patterns and rules in the data, so that it can better fit the historical data. After the training is completed, the model will calculate the predicted value of the 3D displacement of each work base point at the next moment based on the current data and the learned rules.

[0088] Similarly, the absolute three-dimensional coordinates of each observation point and the three-dimensional displacement of each observation point are sequentially input into the statistical analysis model to obtain the predicted value of the three-dimensional displacement of each observation point at the next moment.

[0089] It is determined whether the predicted value of the three-dimensional displacement of each working base point at the next moment and the predicted value of the three-dimensional displacement of each observation point at the next moment meet expectations. If the predicted value does not meet expectations, an early warning signal is output to prompt relevant personnel to take corresponding measures; if the predicted value meets expectations, S26 is executed to calculate the deviation.

[0090] The predicted value of the three-dimensional displacement of each working base point at the next moment and the predicted value of the three-dimensional displacement of each observation point at the next moment are in line with expectations, which means that the predicted value of the three-dimensional displacement of each working base point at the next moment and the predicted value of the three-dimensional displacement of each observation point at the next moment are not greater than the preset three-dimensional displacement threshold.

[0091] S26 Calculate the deviation, and obtain the historical three-dimensional displacement of the observation point and the real-time three-dimensional displacement at the current moment. Arrange the historical three-dimensional displacements in the order of acquisition time to obtain a sequence of historical three-dimensional displacements, and draw a curve of historical three-dimensional displacements based on the sequence of historical three-dimensional displacements.

[0092] Establish a safety monitoring model for fitting the curve of historical three-dimensional displacements, and obtain the theoretical three-dimensional displacement at the current moment based on the safety monitoring model. The theoretical three-dimensional displacement is the expected displacement value at the current moment calculated according to the safety monitoring model.

[0093] For example, the sequence of historical three-dimensional displacements is: 0.8, 0.85, 0.89, 0.9, 0.95, 0.97. In this example, a quadratic polynomial is used for fitting, so the safety monitoring model is: ; After recursive fitting, it is obtained that A = -0.0029, B = 0.0386, and C = 0.7714.

[0094] That is, the calculation model of the safety monitoring model in this example is: .

[0095] In other embodiments, a linear polynomial or a high-degree polynomial can also be used for recursive fitting.

[0096] Judge whether the difference between the real-time three-dimensional displacement and the theoretical three-dimensional displacement is greater than a preset difference threshold. If the difference is greater than the preset difference threshold, output a deviation warning signal to prompt relevant personnel to conduct further inspections and treatments on the observation point and its surrounding areas; if the difference is not greater than the preset difference threshold, no treatment is required.

[0097] In this embodiment, at least one reference point, multiple working reference points and observation points are first set on the river banks on both sides of the target monitoring section area, a three-dimensional coordinate system is constructed with the reference point as the origin, and the original three-dimensional coordinates of each working reference point and the observation point are determined; then a monitoring reference network is constructed, and the coordinates of the working reference point are re-measured after a preset time, and the displacement changes in the horizontal and vertical directions are calculated based on this, and then the three-dimensional displacement is analyzed. If it is greater than a preset threshold, an alarm signal of the working reference point is issued, otherwise the coordinates of the observation point are re-measured; after obtaining the re-measured coordinates of the observation point, its coordinates are updated in combination with the displacement change of the working reference point to obtain the absolute three-dimensional coordinates, and the three-dimensional displacement of the observation point is calculated. If it exceeds the threshold, an alarm is issued, otherwise a prediction is performed; a statistical analysis model is constructed, and the coordinates and displacement data of the working reference point and the observation point are input to obtain a predicted value of the displacement change. If it does not meet expectations, an early warning is issued, and if it meets expectations, a deviation is calculated; the historical and real-time three-dimensional displacements of the observation point are obtained, a historical curve is drawn and a safety monitoring model is established, and the theoretical three-dimensional displacement at the current moment is obtained based on this, and if the difference between the real-time value and the theoretical value exceeds the preset difference threshold, a deviation alarm signal is output, otherwise no processing is performed.

[0098] Embodiment 3: This embodiment is different from Embodiment 1 in that the method further comprises: The laser radar system equipped with a drone is used to collect river data. The laser radar emits a laser beam to the surrounding environment of the river. The laser beam is reflected back after encountering an object. The sensor receives the reflected signal and records the flight time of the laser. According to the speed of light and the flight time, the distance between the object and the drone can be accurately calculated. Combined with the positioning information (such as GPS coordinates) and attitude data (such as pitch angle, roll angle, yaw angle) of the drone, the precise position of each measurement point in three-dimensional space can be determined, thereby obtaining three-dimensional point cloud data of the river.

[0099] Use professional 3D modeling software (such as CloudCompare, MeshLab) or algorithms (such as Poisson reconstruction algorithm) to convert 3D point cloud data into a 3D point cloud model of the river. These software and algorithms can automatically generate a triangular mesh model based on the distribution and characteristics of the point cloud data to restore the surface morphology and terrain characteristics of the river.

[0100] Clearly define inspection objectives, such as monitoring changes in river water quality, checking the condition of buildings around the river, and observing the river ecology. Different inspection objectives may require focusing on different areas and parameters. Based on the 3D point cloud model and inspection objectives, a path planning algorithm (such as the A algorithm and the Dijkstra algorithm) is used to develop the initial inspection route of the drone. The algorithm takes into account factors such as the river's topography and obstacle distribution to ensure that the drone can cover all inspection areas safely and efficiently. For example, the A algorithm estimates the cost function from the start point to the end point to find the optimal flight path while avoiding obstacles.

[0101] Control the drone to inspect the river channel according to the initial inspection route. During the inspection, sensors carried by the drone (such as meteorological sensors and water quality sensors) collect meteorological data (such as temperature, humidity, wind speed, wind direction) and water quality data (such as pH value, dissolved oxygen, turbidity) in real time.

[0102] The collected meteorological data and water quality data are uploaded to the cloud server through the communication module of the drone (such as 4G / 5G transmission mode). The cloud server can store, analyze and process the data to provide data support for subsequent decision-making. For example, by long-term monitoring of the changing trend of water quality data, water pollution problems can be detected in time and corresponding treatment measures can be taken.

[0103] In other embodiments, the method further includes: An unmanned boat is equipped with a variety of sensors, such as lidar, cameras, sonar, etc., to perceive the water surface and boundary environment of the water area. The lidar is used to obtain three-dimensional information above the water surface and on the shore, the camera is used to capture visual images of the water surface, and the sonar is used to detect underwater terrain and obstacles. The data collected by the sensors are fused and processed to build a river channel model. The river channel model is updated in real time through algorithms (such as Simultaneous Localization and Mapping algorithm, SLAM).

[0104] Mark the water area contour and the positions of obstacles in the river channel model. Marking the water area contour helps to determine the scope of cleaning operations, while marking the obstacles can prevent the unmanned boat from colliding during the cleaning process. For example, mark the positions and sizes of obstacles such as bridge piers, reefs, and abandoned ships. This embodiment adopts a method combining manual marking and automatic marking. Manual marking directly marks important feature points by operators on the software interface; automatic marking uses algorithms such as image recognition and object detection to automatically identify and mark obstacles and water area contours.

[0105] Based on the marked river channel model, the river channel is divided into multiple sub-regions. The division can be carried out according to the geographical features of the river channel (such as the length and width of the river section), the distribution of obstacles, or the priority of the cleaning task. In each sub-region respectively, the unmanned boat conducts water area cleaning according to the preset cleaning strategy. The cleaning strategy can be formulated according to the characteristics of the sub-region. For example, increase the cleaning frequency and intensity for areas with severe pollution. The unmanned boat can improve the water area environment by salvaging floating objects, adsorbing oil pollution, etc.

[0106] Through the collaborative operation of the drone and the unmanned boat in this embodiment, comprehensive monitoring and efficient cleaning of the river channel can be achieved, improving the intelligent level of river channel management and the water area environment quality.

[0107] Embodiment 4: This embodiment discloses a river channel safety monitoring system, and the system includes: a processor and a memory, Program code is stored in the memory; When the processor calls the program code in the memory, it executes the steps of the method.

[0108] The above are all preferred embodiments of this application. It does not limit the protection scope of this application accordingly. Therefore, all equivalent changes made according to the structure, shape, and principle of this application shall be covered within the protection scope of this application.

Claims

1. A river channel safety monitoring method, characterized in that, Including: Obtain the riverbed elevation distribution data of the target monitoring section area, divide the target monitoring section area into multiple flow measurement sub-areas, obtain the section parameters of each flow measurement sub-area, a radar velocity sensor for collecting river water velocity data is arranged in each flow measurement sub-area, and a radar water level sensor for measuring the first river water level data is arranged in any one of the flow measurement sub-areas; Construct a hydrodynamic model, input the river water velocity data of each flow measurement sub-area into the hydrodynamic model, and obtain the average velocity data of each flow measurement sub-area; Calculate the second river water level data of the remaining flow measurement sub-areas according to the first river water level data and the riverbed elevation distribution data; calculate the instantaneous flow according to the section parameters, the first river water level data, the second river water level data and the average velocity data, calculate the cumulative flow according to the instantaneous flow, and send an alarm signal when the cumulative flow exceeds the preset flow threshold.

2. The river channel safety monitoring method according to claim 1, characterized in that, The method further includes: Based on all the river water velocity data, use the interpolation method to calculate the surface velocity data of the target monitoring section area, calculate the vertical velocity data of each position in the target monitoring section area according to the preset velocity distribution calculation model, and draw a vertical velocity data change curve according to the vertical velocity data. The horizontal axis of the vertical velocity data change curve is the shortest distance to any river bank, and the vertical axis is the vertical velocity data; Calculate the first derivative of the vertical velocity data change curve with respect to the distance, perform discretization processing on the first derivative to obtain the discretization processing result, perform normalization processing on the discretization processing result to obtain the first data, calculate the vertical average value based on the first data and the vertical velocity data, and update the vertical average value to the river water velocity data.

3. The river channel safety monitoring method according to claim 1 or 2, characterized in that, The method further includes: Set at least one reference point, multiple working base points and multiple observation points on the river banks on both sides of the target monitoring section area, construct a three-dimensional coordinate system with the position of the reference point as the coordinate origin, and determine the original three-dimensional coordinates of each working base point and the original three-dimensional coordinates of each observation point; Select at least one reference point and at least one working base point to construct a monitoring reference network, re-measure the three-dimensional coordinates of each working base point after a preset time period to obtain the re-measured three-dimensional coordinates of each working base point, and obtain the displacement change amount in the horizontal direction and the displacement change amount in the vertical direction of each working base point based on the original three-dimensional coordinates and the re-measured three-dimensional coordinates of each working base point; Calculate the three-dimensional displacement amount of each working base point according to the displacement change amount in the horizontal direction and the displacement change amount in the vertical direction of each working base point, and sequentially determine whether the three-dimensional displacement amount of each working base point is greater than the preset three-dimensional displacement threshold. If so, send a working base point alarm signal; if not, do not process.

4. The river channel safety monitoring method according to claim 3, wherein, When the three-dimensional displacement amount of the working base point is not greater than the preset three-dimensional displacement threshold, the method further includes: Obtain the re-measured three-dimensional coordinates of the observation point, and the re-measured three-dimensional coordinates of the observation point have the same timestamp as the re-measured three-dimensional coordinates of the working base point; The remeasured three-dimensional coordinates of the observation point are updated based on the displacement changes in the horizontal direction and the vertical direction of each working base point to obtain the absolute three-dimensional coordinates of the observation point. The three-dimensional displacement of each observation point is calculated according to the original three-dimensional coordinates and the absolute three-dimensional coordinates of the observation point, and it is determined in turn whether the three-dimensional displacement of each observation point is greater than the preset three-dimensional displacement threshold. If so, an observation point alarm signal is issued; if not, no processing is performed.

5. The river channel safety monitoring method according to claim 4, characterized in that, When the three-dimensional displacement of the observation point is not greater than a preset three-dimensional displacement threshold, the method further includes: Construct a statistical analysis model, input the re-measured three-dimensional coordinates of each working base point and the three-dimensional displacement of each working base point into the statistical analysis model in sequence, and obtain the predicted value of the three-dimensional displacement of each working base point at the next moment; input the absolute three-dimensional coordinates of each observation point and the three-dimensional displacement of each observation point into the statistical analysis model in sequence, and obtain the predicted value of the three-dimensional displacement of each observation point at the next moment; Determine whether the predicted value of the three-dimensional displacement of each working base point at the next moment and the predicted value of the three-dimensional displacement of each observation point at the next moment are consistent with expectations. If so, no processing is performed; if not, an early warning signal is output.

6. The river channel safety monitoring method according to claim 5, wherein, The method further comprises: Obtain the historical three-dimensional displacement of the observation point and the real-time three-dimensional displacement at the current moment, arrange the historical three-dimensional displacement in the order of acquisition time, obtain the historical three-dimensional displacement sequence, and draw the historical three-dimensional displacement curve based on the historical three-dimensional displacement sequence; Establish a safety monitoring model for fitting the historical three-dimensional displacement curve, and obtain the theoretical three-dimensional displacement at the current moment based on the safety monitoring model; It is determined whether the difference between the real-time three-dimensional displacement and the theoretical three-dimensional displacement is greater than the preset difference threshold. If so, a deviation alarm signal is output; if not, no processing is performed.

7. The river channel safety monitoring method according to claim 1 or 2, characterized in that, The method also includes: using a laser radar carried by a drone to obtain three-dimensional point cloud data of the river channel, constructing a three-dimensional point cloud model of the river channel based on the three-dimensional point cloud data, formulating an initial inspection route of the drone according to the three-dimensional point cloud model and inspection targets and using a path planning algorithm, controlling the drone to inspect the river channel according to the initial inspection route, and collecting meteorological data and water quality data during the inspection process, and uploading the meteorological data and water quality data to the cloud.

8. The river channel safety monitoring method according to claim 7, wherein, The method further comprises: The sensors carried by the unmanned boat are used to perceive and model the water surface and boundary environment of the water area to obtain a river model. The water contour and the location of obstacles are marked in the river model to obtain a marked river model. The river is divided into multiple sub-areas based on the marked river model, and the water area is cleaned in each sub-area.

9. A river channel safety monitoring system, characterized in that, include: processor and memory, The memory stores program code; When the processor calls the program code in the memory, the steps of the method according to any one of claims 1 to 8 are executed.

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