A method for measuring water level in hydraulic engineering

Abstract

The invention relates to the technical field of water level measurement of hydraulic engineering, and discloses a method for measuring water level of hydraulic engineering, comprising: establishing a three-dimensional coordinate system with any point of a dam as a coordinate origin; establishing a water surface fluctuation height model, executing a regional measurement strategy, dividing the dam water surface into a plurality of measurement areas, and calculating the fluctuation amplitude of each measurement area; if the fluctuation amplitude is greater than a fluctuation threshold, using an unmanned aerial vehicle to measure the water level; otherwise, using a sensor to measure the water level; obtaining a historical data set of water levels measured by the sensor, executing a spatiotemporal regression modeling strategy, establishing a water level prediction model of the sensor, and obtaining a first water level; executing an unmanned aerial vehicle attitude adjustment strategy, adjusting the attitude of the unmanned aerial vehicle so that the sensor on the unmanned aerial vehicle is perpendicular to the side of a water surface wave crest; executing an unmanned aerial vehicle measurement strategy, and obtaining a second water level; executing a measurement fusion strategy, calculating the final water level, and improving the accuracy of measuring the water level based on the characteristics of water surface fluctuations.

Description

Technical Field

[0001] The invention relates to the technical field of water level measurement of hydraulic engineering, and in particular to a method for measuring water level of hydraulic engineering. Background Art

[0002] In water conservancy projects, accurate water level measurement is the key to ensuring the normal operation of various water conservancy activities such as water resources management, flood prevention and control, irrigation, and shipping. Water level measurement is one of the basic data for assessing water conditions, monitoring reservoir / dam safety, and conducting water resources scheduling. Traditional water level measurement methods usually use technologies such as buoy sensors, lidar, and radar sensors, which are widely used in dams, lakes, rivers and other waters. However, with the increasing complexity of water surface fluctuations, especially in environments where water flows change rapidly and fluctuations frequently, a single sensor or technology often cannot effectively respond to the dynamic changes in water levels, resulting in reduced measurement accuracy or data distortion. Therefore, the use of flexible and diverse measurement methods, combined with technologies such as sensors and drones, has become a necessary way to solve the problem of water level measurement.

[0003] The water level measurement technology commonly used in the prior art has certain limitations. Sensors are usually suitable for small-scale measurements, but their accuracy and reliability will be greatly reduced in areas with large water surface fluctuations or under extreme weather conditions. In addition, the effective measurement range of the sensor is limited, and it cannot effectively cover a large area of ​​water, and it will be affected by changes in fluctuation amplitude and environmental factors (such as wind speed, temperature, etc.), resulting in inaccurate data. On the other hand, although drone measurements can cover a larger range, due to the constraints of measurement accuracy, errors are prone to occur during long-term operation, especially in the case of high wind speeds. Existing drones often measure the water surface perpendicular to the horizontal plane, resulting in the inability to accurately capture fluctuations, unable to adapt to the water surface slope and wave crests, affecting the accuracy of the overall water surface height calculation. Most of the existing technologies rely on a single measurement method and fail to effectively combine the respective advantages of sensors and drones, resulting in the inability to maximize the accuracy and efficiency of water level measurement results.

[0004] This scheme proposes a water level measurement method for water conservancy projects. It performs intelligent regional division based on the water surface fluctuation characteristics and combines the regional measurement strategy of sensors and drones to achieve more accurate and stable water level measurement, solving the problems of inconsistent data and large measurement errors in the existing technology. Summary of the invention

[0005] The present invention provides a water level measurement method for a hydraulic engineering project, which is used to promote solving the problems mentioned in the above background technology.

[0006] The present invention provides the following technical solution: a method for measuring water level in a hydraulic project, comprising:

[0007] Establish a three-dimensional coordinate system with any point of the dam as the coordinate origin;

[0008] Modeling water surface fluctuation height Among them, z(x,y,t) is the height of the water surface at the position of the horizontal coordinate x and the vertical coordinate y at time t, N represents the number of components that affect the wave frequency, and A i is the amplitude of the ith water surface fluctuation component, is the spatial frequency of the ith water surface wave component in the horizontal and vertical directions, ω i is the angular frequency of the ith water surface wave component, Φ i is the phase of the i-th water surface wave component;

[0009] Implement a regional measurement strategy, divide the dam water surface into multiple measurement areas, and calculate the fluctuation amplitude of each measurement area;

[0010] Setting volatility thresholds;

[0011] If the fluctuation amplitude is greater than the fluctuation threshold, the water level is measured using a drone;

[0012] If the fluctuation amplitude is less than or equal to the fluctuation threshold, the sensor is used to measure the water level;

[0013] When using a sensor to measure water level:

[0014] Obtain the historical data set of water level measured by the sensor, execute the spatiotemporal regression modeling strategy, establish the water level prediction model of the sensor, and correct the water level measured by the sensor to obtain the first water level;

[0015] When using drones to measure water levels:

[0016] Set multiple drone measurement points in the measurement area, for any drone measurement point;

[0017] According to the water surface wave height model, the UAV attitude adjustment strategy is executed to adjust the attitude of the UAV so that the sensor on the UAV is perpendicular to the side of the water surface wave crest;

[0018] Execute the drone measurement strategy to obtain the water level measured by the drone, which is recorded as the second water level;

[0019] According to the first water level and the second water level, a measurement fusion strategy is executed to calculate the final water level.

[0020] Optionally, the executing of the regional measurement strategy, dividing the dam water surface into different measurement areas, and calculating the fluctuation amplitude of each measurement area, includes:

[0021] Execute the experimental measurement strategy to obtain the effective measurement radius R of the sensor 0 , specifically:

[0022] Select any point on the water surface as the experimental measurement point, measure the water level at the experimental measurement point, and record it as the standard height;

[0023] Set the experimental interval distance;

[0024] Starting from the experimental measurement point, use the sensor to measure the water level at every experimental interval, and calculate the difference between the water level measured each time and the standard height, which is recorded as the measurement error;

[0025] Set the difference threshold;

[0026] The measurement error is compared with the difference threshold. If the measurement error is greater than or equal to the difference threshold, the measurement point corresponding to the measurement error is considered to be an invalid measurement point.

[0027] Obtain the first invalid measurement point in the order from near to far from the experimental measurement point, obtain the distance between the invalid measurement point and the experimental measurement point, and obtain the effective measurement radius R 0 .

[0028] Optionally, the executing of the regional measurement strategy, dividing the dam water surface into different measurement areas, and calculating the fluctuation amplitude of each measurement area, further includes:

[0029] Get the effective measurement radius R 0 ;

[0030] Calculate the diagonal length equal to 2R 0 The side length of the square is R 1 ;

[0031] The area where the dam water surface is located is divided into grids with side lengths R 1 A number of squares, each square is recorded as a measurement area;

[0032] Calculate the fluctuation amplitude for each measurement area:

[0033] Arbitrarily set e measuring points in the measuring area and measure the water level at each measuring point;

[0034] Get the maximum water level and the minimum water level among e measuring points;

[0035] Calculate the difference between the maximum water level and the minimum water level, and record the result as the fluctuation amplitude of the measurement area.

[0036] Optionally, the acquiring of a historical data set of water levels measured by sensors, executing a spatiotemporal regression modeling strategy, establishing a water level prediction model of the sensors, and correcting the water levels measured by the sensors includes:

[0037] One sensor measures the water level in one measurement area;

[0038] Sort all sensors;

[0039] Use the historical data set to build a water level prediction model for the fth sensor at the kth time:

[0040]

[0041] Among them, v k，f represents the predicted sensor-measured water level, represents the impact of p environmental factors on a single sensor, μ k,m represents the influence of the mth environmental factor on the water level measured by the fth sensor at the kth moment, β m,f represents the influence coefficient of the mth environmental factor on the fth sensor;

[0042] represents the correlation between water levels measured by multiple sensors, γ l,f represents the influence coefficient of the lth sensor on the water level measured by the fth sensor, v k,l represents the water level value measured by the lth sensor at the kth time, and q is the total number of sensors;

[0043] represents the time dependence of the water level value, δ t,f is the hysteresis coefficient, which indicates the influence of the sensor f measurement value on the time compensation t, v k-t,f represents the water level measured by the fth sensor at the ktth time;

[0044] ε k,f represents the error term, β 0,f is the bias term of the f-th sensor.

[0045] Optionally, the step of acquiring a historical data set of water levels measured by sensors, executing a spatiotemporal regression modeling strategy, establishing a water level prediction model of the sensors, and correcting the water levels measured by the sensors further includes:

[0046] Calculate the regression coefficient γ using the least squares method l,f , β m,f , δ t,f and β 0,f :

[0047] Among them, s is the number of water level moments recorded in historical data;

[0048] Get the optimal regression coefficient γ' l,f , β' m,f , δ' t,f and β' 0,f ;

[0049] Substitute the optimal regression coefficient into the water level prediction value model to obtain the latest water level prediction model;

[0050] Get the water level value measured by each sensor this time, execute the water level prediction model for each sensor measurement, and get the water level prediction value of each sensor represents the predicted water level value of the fth sensor at the kth moment;

[0051] Use the water level prediction value to correct the water level of the fth sensor in, is the proportionality coefficient, v′ k，f The water level measured by the corrected sensor.

[0052] Optionally, executing a drone attitude adjustment strategy according to the water surface fluctuation height model to adjust the attitude of the drone so that a sensor on the drone is perpendicular to the side of the water surface wave crest includes:

[0053] Calculate the gradient of the water surface crest at the drone measurement point:

[0054] in, represents the slope on the horizontal axis, Represents the slope on the vertical axis, (x 1 ,y 1 ,z 1 ) is the coordinate of the UAV measurement point in the three-dimensional coordinate system, is the gradient of the water surface crest, is the gradient symbol;

[0055] Get the normal vector of the gradient direction As the normal vector of the wave crest on the water surface;

[0056] Get the direction of the drone's flight Among them, g x ,g y ,g z The unit vector representing the flight direction is decomposed into the directions of the coordinate axes parallel to the three-dimensional coordinates;

[0057] Adjust the drone's flight attitude so that the sensor on the drone is perpendicular to the side of the wave crest on the water surface:

[0058]

[0059] Optionally, executing the drone measurement strategy to obtain the water level measured by the drone, recorded as the second water level, includes:

[0060] When the drone measures the water level at any drone measurement point:

[0061] Get the current position coordinates of the drone (x0 ,y 0 ,z 0 );

[0062] Calculate the vertical distance from the drone to the drone measurement point:

[0063] Among them, (n x ,n y ,n z ) is the value of the normal vector in the three-dimensional coordinate system of the gradient direction of the water surface crest at the measurement point of the UAV;

[0064] Calculate the measured angle θ of the drone;

[0065] The measurement angle θ is in accordance with

[0066] When the UAV measures the water level at the UAV measurement point by measuring the distance and the angle, the water level output by the UAV sensor is obtained as the water level measured at the UAV measurement point;

[0067] For any measurement area:

[0068] Obtain the water levels of all drone measurement points in the measurement area, calculate the mean of all water levels, and record the mean as the second water level.

[0069] Optionally, executing a measurement fusion strategy according to the first water level and the second water level to calculate a final water level includes:

[0070] The water level in the measurement area is denoted as v 1 / 2 , where v is measured when the measurement area is measured using a sensor. 1 / 2 is the first water level;

[0071] When the survey area is measured using a drone, v 1 / 2 is the second water level;

[0072] Execute measurement fusion strategy to calculate final water level at dam surface Wherein, o is the number of measurement areas and o is a positive integer.

[0073] The present invention has the following beneficial effects:

[0074] 1. This water level measurement method for water conservancy projects provides a stable basic framework for subsequent water surface fluctuation modeling by establishing a three-dimensional coordinate system at any point on the dam as the coordinate origin. The water surface fluctuation height model is established, and the mathematical expression of the water surface fluctuation is clarified, making the model more mathematically standardized and scientific. By introducing the water surface fluctuation component N that affects the fluctuation frequency, as well as the parameters such as the amplitude, spatial frequency, angular frequency and phase of each component, the changes in water surface fluctuations in time and space can be accurately described. This process provides a clear reference framework for subsequent water surface measurements and helps to better understand the dynamic characteristics of water surface fluctuations.

[0075] 2. The water level measurement method of this water conservancy project, the design of the regional measurement strategy divides the dam water surface into multiple measurement areas, making the measurement of each area more specific, reducing the possibility of error propagation and data inconsistency. By calculating the fluctuation amplitude of each area and setting the fluctuation threshold, it can be intelligently decided to use sensors for measurement in areas with smaller fluctuation amplitudes, and use drones for measurement in areas with larger fluctuations. This regional strategy can effectively control the measurement cost, improve the measurement accuracy and flexibility, and avoid the situation where blindly using drones for extensive scanning wastes energy or reduces data quality. By dynamically selecting measurement methods and areas, more reliable input data is provided for subsequent fusion algorithms and water level predictions.

[0076] 3. This water level measurement method for water conservancy projects can determine the effective measurement range of the sensor at different distances by selecting experimental measurement points on the water surface and calculating the measurement error, which helps to ensure that the data acquisition range of the sensor is within its effective range, reduce errors and improve the accuracy of the measurement. The comparison between the set difference threshold and the measurement error can effectively screen out invalid measurement points, thereby excluding data with large errors in practical applications. It is convenient to exclude measurement data that does not meet the accuracy requirements and ensure the stability of measurement accuracy by dynamically adjusting the measurement radius. Through this effective calculation of the sensor measurement radius, the data quality in the measurement process is ensured, and accurate basic data is provided for subsequent regional division and measurement fusion.

[0077] 4. The water level measurement method of this water conservancy project calculates the inscribed square of the circle and divides the water surface of the dam into several square measurement areas. This division method can ensure that the size and shape of each measurement area can adapt to the changing characteristics of water surface fluctuations. The division of the measurement area not only simplifies the scope of water surface monitoring, but also effectively improves the representativeness and accuracy of the measurement data. Calculating the fluctuation amplitude of each measurement area By selecting multiple measurement points in the area, the real dynamic situation of water surface fluctuations can be effectively captured. The amplitude of water surface fluctuations is quantified by calculating the difference between the maximum and minimum water levels in each area. It provides a basic basis for the subsequent selection of appropriate measurement methods (sensors or drones), and can use sensors for efficient and accurate measurements in areas with smaller fluctuations, and use drones for flexible and comprehensive measurements in areas with larger fluctuations, thereby balancing the efficiency and accuracy of the measurement.

[0078] 5. The water level measurement method of this water conservancy project provides a theoretical basis for subsequent water level correction by using historical data sets to establish a water level prediction model for sensors. When establishing the prediction model, the influence of environmental factors, the correlation between sensors, and the temporal dependence of water level values ​​are considered, making the prediction model more comprehensive and accurate. Through the spatiotemporal regression modeling strategy, the measurement data of each sensor under different environmental conditions can be accurately predicted, and the predicted water level can be corrected according to the actual measurement data. This correction method makes the water level measured by each sensor closer to the actual water level and reduces the influence of environmental factors. By fusing and correcting multiple sensor data, the accuracy of the overall measurement method can be improved, making the measurement results of the dam surface water level more stable and reliable.

[0079] 6. The water level measurement method of the water conservancy project optimizes the regression coefficient through the least square method to obtain the optimal water level prediction model. By using the correction of the regression coefficient, environmental factors and the correlation between sensors, the accuracy of the sensor water level prediction value is further improved. After the water level prediction value of each sensor is corrected, the influence of environmental factors can be effectively eliminated and the accuracy of the measurement data can be improved. Finally, these corrected water level prediction models can provide a more reliable basis for the comprehensive measurement of the dam surface water level.

[0080] 7. The water level measurement method of the water conservancy project can effectively improve the measurement accuracy by calculating the gradient of the crest of the water surface where the drone measurement point is located and adjusting the flight attitude of the drone so that the sensor on the drone is perpendicular to the side of the crest of the water surface. By combining the normal vector in the gradient direction and the unit vector in the flight direction, it is ensured that the drone can always accurately align with the side of the crest for measurement. This attitude adjustment method can avoid water level data errors caused by inappropriate measurement angles, thereby improving the accuracy of water level measurement. By precisely controlling the flight attitude, the measurement error is minimized and more reliable measurement data is provided for subsequent data fusion and water level estimation. Instead of calculating the difference by measuring the crest and trough of the water surface to obtain the water level of the water surface, the measurement distance and angle of the drone are customized based on the characteristics of the water surface fluctuations, thereby improving the measurement accuracy.

[0081] 8. This water level measurement method for water conservancy projects makes the measurement more accurate by accurately calculating the vertical distance and measurement angle of the drone measurement point and adjusting the drone's flight and measurement methods according to these parameters. By obtaining the current position coordinates of the drone and comparing them with the water level height of the measurement point, the water level measured by the drone sensor can be accurately obtained. This method not only takes into account the gradient of the water surface crest, but also dynamically adjusts the flight trajectory according to the change of the measurement angle, effectively reducing the measurement error and ensuring the accuracy and consistency of the measurement. By dynamically adjusting the drone's flight attitude, the influence of external factors (such as wind speed, flight altitude, etc.) on the measurement results can be effectively reduced, and the accuracy of water level measurement can be optimized.

[0082] 9. This water level measurement method for water conservancy projects can calculate the final water level of the dam surface by fusing the water level data from sensors and drones. Whether using sensors or drones for measurement, the fused water level data is more accurate and can eliminate the limitations of a single measurement method to the greatest extent. By combining the advantages of sensors and drones, not only can sensors be used for efficient measurement in areas with small water surface fluctuations, but drones can also be used for flexible measurement in areas with large water surface fluctuations. This fusion method effectively improves the reliability and measurement accuracy of the entire method, ensuring that the final water level estimate is as close to the true value as possible. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0084] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0085] Embodiment 1, refer to Figure 1 , a water level measurement method for a hydraulic project, comprising:

[0086] Establish a three-dimensional coordinate system with any point of the dam as the coordinate origin;

[0087] Modeling water surface fluctuation height Among them, z(x,y,t) is the height of the water surface at the position of the horizontal coordinate x and the vertical coordinate y at time t, N represents the number of components that affect the wave frequency, and A i is the amplitude of the ith water surface fluctuation component, is the spatial frequency of the ith water surface wave component in the horizontal and vertical directions, ω i is the angular frequency of the ith water surface wave component, Φ i is the phase of the i-th water surface wave component;

[0088] In this embodiment, N=2, indicating that there are two water surface fluctuation components that affect the height of the water surface fluctuation, wherein the first component represents a stronger water surface fluctuation, such as waves caused by a higher wind speed. Specifically, A 1 =5, ω 1 =6π,Φ 1 = 0; the second component represents weaker water surface fluctuations, such as waves caused by tides. Specifically, A 2 =2, ω 2 =6π,Φ 2 =π / 2;

[0089] By establishing a three-dimensional coordinate system as the origin of coordinates at any point on the dam, a stable basic framework is provided for the subsequent modeling of water surface fluctuations. The water surface fluctuation height model is established, and the mathematical expression of water surface fluctuations is clarified, making the model more mathematically standardized and scientific. By introducing the water surface fluctuation component N that affects the fluctuation frequency, as well as the parameters such as the amplitude, spatial frequency, angular frequency and phase of each component, the changes in water surface fluctuations in time and space can be accurately described. This process provides a clear reference framework for subsequent water surface measurements and helps to better understand the dynamic characteristics of water surface fluctuations.

[0090] Implement a regional measurement strategy, divide the dam water surface into multiple measurement areas, and calculate the fluctuation amplitude of each measurement area;

[0091] Setting volatility thresholds;

[0092] If the fluctuation amplitude is greater than the fluctuation threshold, the water level is measured using a drone;

[0093] If the fluctuation amplitude is less than or equal to the fluctuation threshold, the sensor is used to measure the water level;

[0094] The design of the regional measurement strategy divides the dam surface into multiple measurement areas, making the measurement of each area more specific and reducing the possibility of error propagation and data inconsistency. By calculating the fluctuation amplitude of each area and setting the fluctuation threshold, it is possible to intelligently decide to use sensors for measurement in areas with smaller fluctuation amplitudes and use drones for measurement in areas with larger fluctuations. This regional strategy can effectively control the measurement cost, improve the measurement accuracy and flexibility, and avoid the situation where blindly using drones for extensive scanning wastes energy or reduces data quality. By dynamically selecting measurement methods and areas, more reliable input data is provided for subsequent fusion algorithms and water level predictions.

[0095] When using a sensor to measure water level:

[0096] Obtain the historical data set of water level measured by the sensor, execute the spatiotemporal regression modeling strategy, establish the water level prediction model of the sensor, and correct the water level measured by the sensor to obtain the first water level;

[0097] When using drones to measure water levels:

[0098] Set multiple drone measurement points in the measurement area, for any drone measurement point;

[0099] According to the water surface wave height model, the UAV attitude adjustment strategy is executed to adjust the attitude of the UAV so that the sensor on the UAV is perpendicular to the side of the water surface wave crest;

[0100] Execute the drone measurement strategy to obtain the water level measured by the drone, which is recorded as the second water level;

[0101] According to the first water level and the second water level, a measurement fusion strategy is executed to calculate the final water level.

[0102] The implementation of the regional measurement strategy divides the dam water surface into different measurement areas and calculates the fluctuation amplitude of each measurement area, including:

[0103] Execute the experimental measurement strategy to obtain the effective measurement radius R of the sensor 0 , specifically:

[0104] Select any point on the water surface as the experimental measurement point, measure the water level at the experimental measurement point, and record it as the standard height;

[0105] Set the experimental interval distance;

[0106] Starting from the experimental measurement point, use the sensor to measure the water level at every experimental interval, and calculate the difference between the water level measured each time and the standard height, which is recorded as the measurement error;

[0107] Set the difference threshold;

[0108] The measurement error is compared with the difference threshold. If the measurement error is greater than or equal to the difference threshold, the measurement point corresponding to the measurement error is considered to be an invalid measurement point.

[0109] Obtain the first invalid measurement point in the order from near to far from the experimental measurement point, obtain the distance between the invalid measurement point and the experimental measurement point, and obtain the effective measurement radius R 0 .

[0110] By selecting experimental measurement points on the water surface and calculating the measurement error, the effective measurement range of the sensor at different distances can be determined, which helps to ensure that the data acquisition range of the sensor is within its effective range, reduce errors and improve the accuracy of the measurement. The comparison between the set difference threshold and the measurement error can effectively screen out invalid measurement points, thereby excluding data with large errors in practical applications. It is convenient to exclude measurement data that does not meet the accuracy requirements and ensure the stability of measurement accuracy by dynamically adjusting the measurement radius. Through this effective calculation of the sensor measurement radius, the data quality during the measurement process is ensured, and accurate basic data is provided for subsequent regional division and measurement fusion.

[0111] The method of executing the regional measurement strategy, dividing the dam water surface into different measurement areas, and calculating the fluctuation amplitude of each measurement area, also includes:

[0112] Get the effective measurement radius R 0 ;

[0113] Calculate the diagonal length equal to 2R 0 The side length of the square is R 1 ;

[0114] The area where the dam water surface is located is divided into grids with side lengths R 1 A number of squares, each square is recorded as a measurement area;

[0115] Calculate the fluctuation amplitude for each measurement area:

[0116] Arbitrarily set e measuring points in the measuring area and measure the water level at each measuring point;

[0117] Get the maximum water level and the minimum water level among e measuring points;

[0118] Calculate the difference between the maximum water level and the minimum water level, and record the result as the fluctuation amplitude of the measurement area.

[0119] By calculating the inscribed square of the circle and dividing the dam water surface into several square measurement areas, this division method can ensure that the size and shape of each measurement area can adapt to the changing characteristics of water surface fluctuations. The division of the measurement area not only simplifies the scope of water surface monitoring, but also effectively improves the representativeness and accuracy of the measurement data. Calculating the fluctuation amplitude of each measurement area By selecting multiple measurement points in the area, the real dynamic situation of water surface fluctuations can be effectively captured. By calculating the difference between the maximum and minimum water levels in each area, the amplitude of water surface fluctuations is quantified. It provides a basic basis for the subsequent selection of appropriate measurement methods (sensors or drones), and can use sensors for efficient and accurate measurements in areas with smaller fluctuations, and use drones for flexible and comprehensive measurements in areas with larger fluctuations, thereby balancing the efficiency and accuracy of the measurement.

[0120] The method of obtaining a historical data set of water levels measured by sensors, executing a spatiotemporal regression modeling strategy, establishing a water level prediction model of the sensors, and correcting the water levels measured by the sensors includes:

[0121] One sensor measures the water level in one measurement area;

[0122] Sort all sensors;

[0123] Use the historical data set to build a water level prediction model for the fth sensor at the kth time:

[0124]

[0125] Among them, v k，f represents the predicted sensor-measured water level, represents the impact of p environmental factors on a single sensor, μ k,m represents the influence of the mth environmental factor on the water level measured by the fth sensor at the kth moment, β m,f represents the influence coefficient of the mth environmental factor on the fth sensor;

[0126] represents the correlation between water levels measured by multiple sensors, γ l,f represents the influence coefficient of the lth sensor on the water level measured by the fth sensor, v k,l represents the water level value measured by the lth sensor at the kth time, and q is the total number of sensors;

[0127] represents the time dependence of the water level value, δ t,f is the hysteresis coefficient, which indicates the influence of the sensor f measurement value on the time compensation t, v k-t,f represents the water level measured by the fth sensor at the ktth time;

[0128] ε k,f represents the error term, β 0,f is the bias term of the f-th sensor.

[0129] By using historical data sets to establish a water level prediction model for sensors, a theoretical basis is provided for subsequent water level corrections. When establishing the prediction model, the influence of environmental factors, the correlation between sensors, and the temporal dependence of water level values ​​are taken into account, making the prediction model more comprehensive and accurate. Through the spatiotemporal regression modeling strategy, accurate predictions can be made for the measurement data of each sensor under different environmental conditions, and the predicted water level can be corrected based on the actual measurement data. This correction method makes the water level measured by each sensor closer to the actual water level and reduces the influence of environmental factors. By fusing and correcting multiple sensor data, the accuracy of the overall measurement method can be improved, making the measurement results of the dam surface water level more stable and reliable.

[0130] The method of obtaining a historical data set of water levels measured by sensors, executing a spatiotemporal regression modeling strategy, establishing a water level prediction model of the sensors, and correcting the water levels measured by the sensors also includes:

[0131] Calculate the regression coefficient γ using the least squares method l,f , β m,f , δ t,f and β 0,f :

[0132] Among them, s is the number of water level moments recorded in historical data;

[0133] Get the optimal regression coefficient γ' l,f , β' m,f , δ' t,f and β' 0,f ;

[0134] Substitute the optimal regression coefficient into the water level prediction value model to obtain the latest water level prediction model;

[0135] Get the water level value measured by each sensor this time, execute the water level prediction model for each sensor measurement, and get the water level prediction value of each sensor represents the predicted water level value of the fth sensor at the kth moment;

[0136] Use the water level prediction value to correct the water level of the fth sensor in, is the proportionality coefficient, v′ k，f The water level measured by the corrected sensor.

[0137] The regression coefficient is optimized by the least square method to obtain the optimal water level prediction model. The accuracy of the sensor water level prediction value is further improved by correcting the correlation between the regression coefficient, environmental factors and sensors. After the water level prediction value of each sensor is corrected, the influence of environmental factors can be effectively eliminated and the accuracy of the measurement data can be improved. Finally, these corrected water level prediction models can provide a more reliable basis for the comprehensive measurement of the dam surface water level.

[0138] The method of executing the UAV attitude adjustment strategy according to the water surface fluctuation height model to adjust the attitude of the UAV so that the sensor on the UAV is perpendicular to the side of the water surface wave crest includes:

[0139] Calculate the gradient of the water surface crest at the drone measurement point:

[0140] in, represents the slope on the horizontal axis, Represents the slope on the vertical axis, (x 1 ,y 1 ,z 1 ) is the coordinate of the UAV measurement point in the three-dimensional coordinate system, is the gradient of the water surface crest, is the gradient symbol;

[0141] Get the normal vector of the gradient direction As the normal vector of the wave crest on the water surface;

[0142] Get the direction of the drone's flight Among them, g x ,g y ,g z The unit vector representing the flight direction is decomposed into the directions of the coordinate axes parallel to the three-dimensional coordinates;

[0143] Adjust the drone's flight attitude so that the sensor on the drone is perpendicular to the side of the wave crest on the water surface:

[0144]

[0145] By calculating the gradient of the crest of the water surface where the drone is measuring and adjusting the drone's flight attitude so that the sensor on the drone is perpendicular to the side of the crest of the water surface, the accuracy of the measurement can be effectively improved. By combining the normal vector in the gradient direction and the unit vector in the flight direction, it is ensured that the drone can always accurately align with the side of the crest for measurement. This attitude adjustment method can avoid water level data errors caused by inappropriate measurement angles, thereby improving the accuracy of water level measurement. By precisely controlling the flight attitude, measurement errors are minimized and more reliable measurement data is provided for subsequent data fusion and water level estimation.

[0146] The method of executing the drone measurement strategy to obtain the water level measured by the drone, recorded as the second water level, includes:

[0147] When the drone measures the water level at any drone measurement point:

[0148] Get the current position coordinates of the drone (x 0 ,y 0 ,z 0 );

[0149] Calculate the vertical distance from the drone to the drone measurement point:

[0150] Among them, (n x ,n y ,n z ) is the value of the normal vector in the three-dimensional coordinate system of the gradient direction of the water surface crest at the measurement point of the UAV;

[0151] Calculate the measured angle θ of the drone;

[0152] The measurement angle θ is in accordance with

[0153] When the UAV measures the water level at the UAV measurement point by measuring the distance and the angle, the water level output by the UAV sensor is obtained as the water level measured at the UAV measurement point;

[0154] For any measurement area:

[0155] Obtain the water levels of all drone measurement points in the measurement area, calculate the mean of all water levels, and record the mean as the second water level.

[0156] By accurately calculating the vertical distance and measurement angle of the drone measurement point, and adjusting the drone's flight and measurement methods according to these parameters, the measurement is made more accurate. By obtaining the current position coordinates of the drone and comparing them with the water level height of the measurement point, the water level measured by the drone sensor can be accurately obtained. This method not only takes into account the gradient of the water surface crest, but also dynamically adjusts the flight trajectory according to the change of the measurement angle, effectively reducing the measurement error and ensuring the accuracy and consistency of the measurement. By dynamically adjusting the drone's flight attitude, the impact of external factors (such as wind speed, flight altitude, etc.) on the measurement results can be effectively reduced, and the accuracy of water level measurement can be optimized.

[0157] The method of executing the measurement fusion strategy according to the first water level and the second water level to calculate the final water level includes:

[0158] The water level in the measurement area is denoted as v 1 / 2 , where v is measured when the measurement area is measured using a sensor. 1 / 2 is the first water level;

[0159] When the survey area is measured using a drone, v 1 / 2 is the second water level;

[0160] Execute measurement fusion strategy to calculate final water level at dam surface Wherein, o is the number of measurement areas and o is a positive integer.

[0161] By fusing the water level data from sensors and drones, the final water level of the dam surface can be calculated. Whether using sensors or drones for measurement, the fused water level data is more accurate and can eliminate the limitations of a single measurement method to the greatest extent.

[0162] By combining the advantages of sensors and drones, not only can sensors be used for efficient measurement in areas with small water surface fluctuations, but drones can also be used for flexible measurement in areas with large water surface fluctuations. This fusion method effectively improves the reliability and measurement accuracy of the entire method, ensuring that the final water level estimate is as close to the true value as possible.

[0163] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0164] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for measuring water level in a hydraulic project, characterized in that: include: Establish a three-dimensional coordinate system with any point of the dam as the coordinate origin; Modeling water surface fluctuation height Among them, z(x,y,t) is the height of the water surface at the position of the horizontal coordinate x and the vertical coordinate y at time t, N represents the number of components that affect the frequency of fluctuations, and A i is the amplitude of the ith water surface fluctuation component, is the spatial frequency of the ith water surface wave component in the horizontal and vertical directions, ω i is the angular frequency of the ith water surface wave component, Φ i is the phase of the i-th water surface wave component; Implement a regional measurement strategy, divide the dam water surface into multiple measurement areas, and calculate the fluctuation amplitude of each measurement area; Setting volatility thresholds; If the fluctuation amplitude is greater than the fluctuation threshold, the water level is measured using a drone; If the fluctuation amplitude is less than or equal to the fluctuation threshold, the sensor is used to measure the water level; When using a sensor to measure water level: Obtain the historical data set of water level measured by the sensor, execute the spatiotemporal regression modeling strategy, establish the water level prediction model of the sensor, and correct the water level measured by the sensor to obtain the first water level; When using drones to measure water levels: Set multiple drone measurement points in the measurement area. For any drone measurement point: According to the water surface wave height model, the UAV attitude adjustment strategy is executed to adjust the attitude of the UAV so that the sensor on the UAV is perpendicular to the side of the water surface wave crest; Execute the drone measurement strategy to obtain the water level measured by the drone, which is recorded as the second water level; According to the first water level and the second water level, a measurement fusion strategy is executed to calculate the final water level.

2. The water level measurement method for hydraulic engineering according to claim 1, characterized in that: The implementation of the regional measurement strategy divides the dam water surface into different measurement areas and calculates the fluctuation amplitude of each measurement area, including: Execute the experimental measurement strategy to obtain the effective measurement radius R0 of the sensor, specifically: Select any point on the water surface as the experimental measurement point, measure the water level at the experimental measurement point, and record it as the standard height; Set the experimental interval distance; Starting from the experimental measurement point, use the sensor to measure the water level at every experimental interval, and calculate the difference between the water level measured each time and the standard height, which is recorded as the measurement error; Set the difference threshold; The measurement error is compared with the difference threshold. If the measurement error is greater than or equal to the difference threshold, the measurement point corresponding to the measurement error is considered to be an invalid measurement point. The first invalid measurement point is obtained in order from near to far from the experimental measurement point, and the distance between the invalid measurement point and the experimental measurement point is obtained to obtain the effective measurement radius R0.

3. The water level measurement method for hydraulic engineering according to claim 2, characterized in that: The method of executing the regional measurement strategy, dividing the dam water surface into different measurement areas, and calculating the fluctuation amplitude of each measurement area, also includes: Get the effective measurement radius R0; Calculate the side length of the square whose diagonal length is equal to 2R0, and get the side length R1; The area where the dam water surface is located is divided into a number of squares with a side length of R1, and each square is recorded as a measurement area; Calculate the fluctuation amplitude for each measurement area: Arbitrarily set e measuring points in the measuring area and measure the water level at each measuring point; Get the maximum water level and the minimum water level among e measuring points; Calculate the difference between the maximum water level and the minimum water level, and record the result as the fluctuation amplitude of the measurement area.

4. The water level measurement method for hydraulic engineering according to claim 1, characterized in that: The method of obtaining a historical data set of water levels measured by sensors, executing a spatiotemporal regression modeling strategy, establishing a water level prediction model of the sensors, and correcting the water levels measured by the sensors includes: One sensor measures the water level in one measurement area; Sort all sensors; Use the historical data set to build a water level prediction model for the fth sensor at the kth time: Among them, v k,f represents the predicted sensor-measured water level, represents the impact of p environmental factors on a single sensor, μ k,m represents the influence of the mth environmental factor on the water level measured by the fth sensor at the kth moment, β m,f represents the influence coefficient of the mth environmental factor on the fth sensor; represents the correlation between water levels measured by multiple sensors, γ l,f represents the influence coefficient of the lth sensor on the water level measured by the fth sensor, v k,l represents the water level value measured by the lth sensor at the kth time, and q is the total number of sensors; represents the time dependence of the water level value, δ t,f is the hysteresis coefficient, which indicates the influence of the sensor f measurement value on the time compensation t, v k-t,f represents the water level measured by the fth sensor at the ktth time; ε k,f represents the error term, β 0,f is the bias term of the f-th sensor.

5. The water level measurement method for hydraulic engineering according to claim 4, characterized in that: The method of obtaining a historical data set of water levels measured by sensors, executing a spatiotemporal regression modeling strategy, establishing a water level prediction model of the sensors, and correcting the water levels measured by the sensors also includes: Calculate the regression coefficient γ using the least squares method l,f , β m,f , δ t,f and β 0,f : Among them, s is the number of water level moments recorded in historical data; Get the optimal regression coefficient γ l ' ,f , β' m,f , δ t ' ,f and β0' ,f ; Substitute the optimal regression coefficient into the water level prediction value model to obtain the latest water level prediction model; Get the water level value measured by each sensor this time, execute the water level prediction model for each sensor measurement, and get the water level prediction value of each sensor represents the predicted water level value of the fth sensor at the kth moment; Use the water level prediction value to correct the water level of the fth sensor in, is the proportionality coefficient, v' k,f The water level measured by the corrected sensor.

6. The water level measurement method for a hydraulic project according to claim 1, characterized in that: The method of executing the UAV attitude adjustment strategy according to the water surface fluctuation height model to adjust the attitude of the UAV so that the sensor on the UAV is perpendicular to the side of the water surface wave crest includes: Calculate the gradient of the water surface crest at the drone measurement point: in, represents the slope on the horizontal axis, represents the slope on the vertical axis, (x1, y1, z1) is the coordinate of the drone measurement point in the three-dimensional coordinate system, is the gradient of the water surface crest, is the gradient symbol; Get the normal vector of the gradient direction As the normal vector of the wave crest on the water surface; Get the direction of the drone's flight Among them, g x ,g y ,g z The unit vector representing the flight direction is decomposed into directions parallel to the three-dimensional coordinate axes; Adjust the drone's flight attitude so that the sensor on the drone is perpendicular to the side of the wave crest on the water surface:

7. The water level measurement method for a hydraulic project according to claim 6, characterized in that: The method of executing the drone measurement strategy to obtain the water level measured by the drone, recorded as the second water level, includes: When the drone measures the water level at any drone measurement point: Get the current position coordinates of the drone (x0, y0, z0); Calculate the vertical distance from the drone to the drone measurement point: Among them, (n x ,n y ,n z ) is the value of the normal vector in the three-dimensional coordinate system of the gradient direction of the water surface crest at the measurement point of the UAV; Calculate the measured angle θ of the drone; The measurement angle θ is in accordance with When the UAV measures the water level at the UAV measurement point by measuring the distance and the angle, the water level output by the UAV sensor is obtained as the water level measured at the UAV measurement point; For any measurement area: Obtain the water levels of all drone measurement points in the measurement area, calculate the mean of all water levels, and record the mean as the second water level.

8. The water level measurement method for a hydraulic project according to claim 3, characterized in that: The method of executing the measurement fusion strategy according to the first water level and the second water level to calculate the final water level includes: The water level in the measurement area is denoted as v 1 / 2 , where v is measured when the measurement area is measured using a sensor. 1 / 2 is the first water level; When the survey area is surveyed using a drone, v 1 / 2 is the second water level; Execute measurement fusion strategy to calculate final water level at dam surface Wherein, o is the number of measurement areas and o is a positive integer.

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