Measuring method of moving dune flow measuring device

Abstract

The invention provides a measuring method of a moving dune flow measuring device. The measuring method of the flowing dune flow measuring device comprises the steps that a, dynamic changes of the surface of a dune are monitored through the sensors, b, collected data of the sensors are transmitted to the data collecting system, the data collecting system integrates real-time data of the multiple sensors and carries out primary processing, and a dynamic characteristic sequence and an airflow data sequence of the surface of the dune are obtained; wherein the variable is a time variable. According to the measuring method of the flowing dune flow measuring device, surface dynamic changes and airflow characteristics of a dune are monitored in real time through high-precision equipment such as a laser scanner, a displacement sensor and a wind speed sensor, key parameters such as the height, the flowing speed, the gradient and the wind speed of the dune are obtained, and comprehensiveness and accuracy of data are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of dune flow measurement, and specifically to a method for measuring the flow rate of a mobile dune using a flow rate measuring device for mobile dunes. Background Art

[0002] The basic structure of a flow rate measuring device for mobile dunes mainly consists of the following parts: Dune measurement unit: This part installs a series of sensors, such as laser scanners or displacement sensors, to monitor the height changes and morphological flow of the dune in real time. The sensors can accurately record the change process of the dune and provide accurate data support. Airflow sensor: Located near the dune, it is used to measure important parameters such as the speed and direction of the airflow. Through these airflow data, the relationship between the wind force and the dune flow can be further analyzed to calculate the flow rate of the dune flow. Data acquisition system and control module: The data acquisition system is responsible for collecting real-time data from each sensor and analyzing and processing it through the control module. The control module can adjust the measurement parameters in real time to ensure the accuracy and consistency of data acquisition. In principle, the measuring device calculates the flow rate of the dune flow through a physical model of the interaction of factors such as wind speed, dune morphology, and slope. The changes in airflow and dune morphology directly affect the evaluation of the flow rate. The flow rate measurement depends on the accurate modeling of the dune dynamics and airflow changes and the dynamic adjustment in combination with the sensor data.

[0003] Although the flow rate measuring device for mobile dunes can effectively measure the flow rate under certain conditions, the system still has several defects: Due to the highly non-linear characteristics of the dune flow, the existing models may not be able to fully capture the complexity of the dune flow. Sensor errors and environmental changes may also lead to deviations in the flow rate measurement. Summary of the Invention

[0004] In view of the deficiencies of the prior art, the present invention provides a method for measuring the flow rate of a mobile dune using a flow rate measuring device, which solves the problems that the dune flow has highly non-linear characteristics, the existing models cannot fully capture the complexity of the dune flow, and the deviations in the flow rate measurement caused by sensor errors and environmental changes.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: A method for measuring the flow rate of a mobile dune using a flow rate measuring device, including:

[0006] a. Monitoring the dynamic changes on the dune surface through sensors;

[0007] b. Transmitting the acquisition data of the sensors to the data acquisition system, and the data acquisition system integrates and preliminarily processes the real-time data of multiple sensors to obtain the dynamic feature sequence S(t) = {h(t), v(t), θ(t)} of the dune surface and the airflow data sequence G(t) = {U(t), α(t)}, where t is the time variable;

[0008] c. Based on the nonlinear characteristics of dune flow, a nonlinear dynamics model is used to model the complexity of dune flow. By introducing the coupling relationship between the changes on the dune surface and the airflow action, the flow rate is measured. The nonlinear model is as follows:

[0009] Q(t) = f(S(t), G(t), γ)

[0010] Where Q(t) is the flow rate, f(S(t), G(t), γ) is the coupling function of the dune and the airflow, and γ is the model adjustment coefficient, which controls the intensity of the interaction between the dune and the wind-blown sand;

[0011] d. Combining the principles of fluid dynamics, using the wind speed, dune flow velocity, and slope parameters, the sand grain flow intensity in the dune area is deduced. The flow rate calculation method is as follows:

[0012] Q(t) = ∫ A ρ·U(t)·v(t)dA

[0013] Where ρ is the sand grain density, A is the cross-sectional area of the dune in the measurement area, U(t) is the wind speed, and v(t) is the sand grain flow velocity;

[0014] e. Based on the complexity of dune flow, a segmented flow rate calculation method is adopted;

[0015] f. The sensor data is periodically calibrated, and based on the calibration results, the dune flow rate measurement process is corrected. The correction process is carried out through the following formula:

[0016] Q calib (t) = Q(t) × (1 + ∈(t))

[0017] Where ∈(t) is the error correction coefficient, which is used to correct the sensor error in real time;

[0018] g. A regional flow rate assessment model is adopted to calculate the local flow rate according to the flow characteristics and airflow conditions in different regions of the dune;

[0019] h. Through machine learning algorithms, the historical data is learned and optimized, and the prediction model is used to predict the future dune flow rate;

[0020] i. Through multi-sensor data fusion technology, the errors generated by different sensors are eliminated, and the stability of the flow rate measurement is improved;

[0021] j. A real-time feedback mechanism is adopted to continuously correct the measured data. According to the current measurement error, the model parameters are dynamically adjusted to adapt to the interference of environmental factors such as wind speed changes and dune shape changes, ensuring the long-term stable operation of the system;

[0022] k. Automatically generate time - series data of dune flow based on the corrected flow calculation results for subsequent analysis and decision - making support.

[0023] Preferably, the sensors include a laser scanner, a displacement sensor, and a wind speed sensor. The sensors collect the height, flow velocity, slope of the dune, and airflow parameters such as wind speed and wind direction in real - time.

[0024] Preferably, the segmented flow calculation method divides the dune into multiple regions, independently measures each region, and obtains the overall flow by weighted summation. This method can effectively reduce model errors and improve the accuracy of flow measurement.

[0025] Preferably, the local flow calculation combines the flow contributions of each region through weighted merging to obtain the overall flow. This method can improve the adaptability to the complex structure of the dune and the measurement accuracy.

[0026] Preferably, the machine - learning algorithm forms a machine - learning model. The machine - learning model is trained based on historical data and can adjust the measurement strategy in real - time to improve the prediction accuracy of the system.

[0027] Preferably, the data fusion method uses the Kalman filter algorithm to correct according to the difference between the sensor measurement value and the predicted value, optimizing the system performance.

[0028] Preferably, the non - linear dynamics model includes a dune flow model and an airflow dynamics model. By coupling these models, the accuracy of flow prediction is improved. The multi - sensor data fusion technology uses the Kalman filter algorithm to optimize the data fusion accuracy and reduce the error propagation between different sensors.

[0029] Preferably, the flow meter further includes a dune morphology prediction module for predicting the future flow trend based on the current dune morphology, thereby adjusting the measurement strategy in advance.

[0030] The present invention provides a method for measuring the flow rate of a mobile dune. It has the following beneficial effects:

[0031] This method for measuring the flow rate of a mobile dune, through high - precision devices such as a laser scanner, a displacement sensor, and a wind speed sensor, monitors the surface dynamic changes of the dune and the airflow characteristics in real - time, obtaining key parameters such as the dune height, flow velocity, slope, and wind speed, ensuring the comprehensiveness and accuracy of the data. On this basis, a non - linear dynamics model is used to describe the complex interaction between the dune and the airflow, and the sand flow intensity in the dune area is effectively calculated by combining the principles of fluid dynamics. Through the segmented flow calculation method, the local flow is evaluated by combining the characteristics of different regions of the dune, and the flow contributions of each region are weighted and merged, making the overall measurement more accurate and significantly improving the adaptability and accuracy of the system.

[0032] In response to the deviation in flow measurement caused by sensor errors and environmental changes, the present invention introduces a periodic data calibration, error correction, and real-time feedback mechanism. By optimizing the prediction model through machine learning algorithms and adopting data fusion technologies such as Kalman filtering, the system can adjust the measurement strategy in real time, dynamically correct the errors in the data, thereby improving the stability and reliability of the system. In particular, combined with the dune morphology prediction module, predicting the future flow trend based on the real-time changes in dune morphology can adjust the measurement strategy in advance to cope with environmental changes, further ensuring the accuracy and stability of the system during long-term operation. Brief Description of the Drawings

[0033] Figure 1 It is a schematic diagram of the internal structure of the present invention. Detailed Embodiment

[0034] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0035] As Figure 1 shown, the embodiment of the present invention provides a method for measuring the flow rate of a mobile dune flow rate measuring device, including: a. Monitoring the dynamic changes on the dune surface through sensors. The sensors include a laser scanner, a displacement sensor, and a wind speed sensor. The sensors collect the height h(t), flow velocity v(t), slope θ(t) of the dune, and the airflow parameters wind speed U(t) and wind direction α(t) in real time.

[0036] b. Transmitting the collected data of the sensors to the data acquisition system. The data acquisition system integrates and preliminarily processes the real-time data of multiple sensors to obtain the dynamic feature sequence S(t) = {h(t), v(t), θ(t)} of the dune surface and the airflow data sequence G(t) = {U(t), α(t)}, where t is the time variable.

[0037] c. Based on the non-linear characteristics of dune flow, using a non-linear dynamics model to model the complexity of dune flow. By introducing the coupling relationship between the dune surface change and the airflow action, the flow rate is measured. The non-linear model is:

[0038] Q(t) = f(S(t), G(t), γ)

[0039] where Q(t) is the flow rate, f(S(t), G(t), γ) is the coupling function between the dune and the airflow, and γ is the model adjustment coefficient, which controls the intensity of the interaction between the dune and the wind sand.

[0040] d. Combining with the principles of fluid dynamics, using the wind speed, dune flow velocity, and slope parameters, the sand grain flow intensity in the dune area is deduced. The flow rate calculation method is as follows:

[0041] Q(t) = ∫ A ρ·U(t)·v(t)dA

[0042] where ρ is the sand grain density, A is the cross-sectional area of the dune in the measurement area, U(t) is the wind speed, and v(t) is the sand grain flow velocity.

[0043] e. Based on the complexity of dune flow, a segmented flow rate calculation method is adopted. The segmented flow rate calculation method divides the dune into multiple regions, independently measures each region, and sums them up with weights to obtain the overall flow rate. This method can effectively reduce the model error and improve the accuracy of flow rate measurement.

[0044] f. Periodically calibrate the sensor data, and correct the dune flow rate measurement process based on the calibration results. The correction process is carried out through the following formula:

[0045] Q calib (t) = Q(t) × (1 + ∈(t))

[0046] where ∈(t) is the error correction coefficient, which is used to correct the sensor error in real time;

[0047] g. Adopt a regional flow rate assessment model, calculate the local flow rate according to the flow characteristics and airflow conditions in different regions of the dune, and combine and weight the local flow rates in each region to obtain the overall flow rate. This method can improve the adaptability to the complex structure of the dune and the measurement accuracy.

[0048] h. Through machine learning algorithms, learn and optimize the historical data, and use the prediction model to predict the future dune flow rate. The machine learning algorithms form a machine learning model, which is trained based on historical data and can adjust the measurement strategy in real time to improve the prediction accuracy of the system.

[0049] i. Through multi-sensor data fusion technology, eliminate the errors generated by different sensors, improve the stability of flow rate measurement. The data fusion method uses the Kalman filter algorithm to correct according to the difference between the sensor measurement value and the predicted value, and optimize the system performance.

[0050] j. Adopt a real-time feedback mechanism to continuously correct the measurement data. Dynamically adjust the model parameters according to the current measurement error to adapt to environmental factor interferences such as wind speed changes and dune morphology variations, ensuring the long-term stable operation of the system. The nonlinear dynamics model includes a dune flow model and an airflow dynamics model. By coupling these models, the accuracy of flow prediction is improved. The multi-sensor data fusion technology uses the Kalman filtering algorithm to optimize the data fusion accuracy and reduce the error propagation between different sensors.

[0051] k. Based on the corrected flow calculation results, automatically generate time series data of dune flow for subsequent analysis and decision support. The flow detector also includes a dune morphology prediction module for predicting future flow trends according to the current dune morphology, thereby adjusting the measurement strategy in advance.

[0052] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for measuring the flow rate of a mobile sand dune, characterized in that, Including: a. Monitoring the dynamic changes of the dune surface through sensors; b. Transmitting the collected data of the sensors to a data acquisition system, which integrates and preliminarily processes the real-time data of multiple sensors to obtain the dynamic feature sequence S(t) = {h(t), v(t), θ(t)} of the dune surface and the airflow data sequence G(t) = {U(t), α(t)}, where t is the time variable; c. Based on the non-linear characteristics of dune flow, using a non-linear dynamics model to model the complexity of dune flow, and measuring the flow rate by introducing the coupling relationship between the dune surface change and the airflow action. The non-linear model is: Q(t) = f(S(t), G(t), γ) where Q(t) is the flow rate, f(S(t), G(t), γ) is the coupling function between the dune and the airflow, and γ is the model adjustment coefficient, which controls the intensity of the interaction between the dune and the wind sand; d. Combining the principles of fluid dynamics, using the wind speed, dune flow velocity, and slope parameters to calculate the sand grain flow intensity in the dune area. The flow rate calculation method is: Q(t) = ∫ A ρ·U(t)·v(t) dA where ρ is the sand grain density, A is the cross-sectional area of the dune in the measurement area, U(t) is the wind speed, and v(t) is the sand grain flow velocity; e. Based on the complexity of dune flow, adopting a segmented flow rate calculation method; f. Periodically calibrating the sensor data, and correcting the dune flow rate measurement process based on the calibration results. The correction process is carried out through the following formula: Q calib (t) = Q(t) × (1 + ∈(t)) where ∈(t) is the error correction coefficient, which is used to correct the sensor error in real time; g. Adopting a regional flow rate assessment model to calculate the local flow rate according to the flow characteristics and airflow conditions in different regions of the dune; h. Through machine learning algorithms, learning and optimizing historical data, and using a prediction model to predict the future dune flow rate; i. Through multi-sensor data fusion technology, eliminating the errors generated by different sensors and improving the stability of flow rate measurement; j. Adopting a real-time feedback mechanism to continuously correct the measured data, dynamically adjusting the model parameters according to the current measurement error to adapt to the interference of environmental factors such as wind speed changes and dune morphology changes, and ensuring the long-term stable operation of the system; k. Based on the corrected flow rate calculation results, automatically generating time series data of the dune flow rate for subsequent analysis and decision support.

2. The measuring method of a mobile dune flow rate measuring device according to claim 1, characterized in that: The sensors include a laser scanner, a displacement sensor, and a wind speed sensor. The sensors real-time collect the height h(t), flow velocity v(t), slope θ(t) of the dune, and the airflow parameters wind speed U(t) and wind direction α(t).

3. The measuring method of a mobile dune flow rate measuring device according to claim 1, characterized in that: The segmented flow rate calculation method divides the dune into multiple regions, independently measures each region, and obtains the overall flow rate by weighted summation. This method can effectively reduce the model error and improve the accuracy of flow rate measurement.

4. The measurement method of a mobile dune flow rate measuring device according to claim 1, characterized in that: The local flow rate calculation combines the flow rate contributions of each region through weighted merging to obtain the overall flow rate. This method can improve the adaptability to the complex structure of the dune and the measurement accuracy.

5. A method for measuring the flow rate of a mobile dune using the flow rate measuring device according to claim 1, characterized in that: The machine learning algorithm forms a machine learning model. The machine learning model is trained based on historical data and can adjust the measurement strategy in real time to improve the prediction accuracy of the system.

6. The measurement method of a mobile dune flow rate measuring device according to claim 1, characterized in that: The data fusion method adopts the Kalman filtering algorithm, which is corrected according to the difference between the sensor measurement value and the predicted value to optimize the system performance.

7. The method for measuring the flow rate of a mobile sand dune using the flow rate measuring device according to claim 1, characterized in that: The non-linear dynamics model includes a dune flow model and an air flow dynamics model. By coupling these models, the accuracy of flow prediction is improved. The multi-sensor data fusion technology uses the Kalman filtering algorithm to optimize the data fusion accuracy and reduce the error propagation between different sensors.

8. The measurement method of a mobile dune flow rate measuring device according to claim 1, characterized in that: The flow meter further includes a dune morphology prediction module for predicting the future flow trend according to the current dune morphology, so as to adjust the measurement strategy in advance.

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