River flood testing system based on unmanned aerial vehicle carrying

Abstract

The invention belongs to the technical field of unmanned aerial vehicle testing, and provides a river flood testing system based on unmanned aerial vehicle carrying, which comprises a sensor integration module used for integrating a Beidou positioning module, a radar wave depth and velocity measurement sensor, a high-resolution camera, an infrared sensor and a multispectral sensor; the data fusion module is used for fusing data from the sensor integration module; the automatic flight control module is used for planning a flight path of the unmanned aerial vehicle; the intelligent data analysis module is used for analyzing the image captured by the high-resolution camera; the environment adaptability module is used for realizing real-time obstacle avoidance and environment perception; the user interface and data visualization module is used for monitoring the state of the unmanned aerial vehicle and checking data in real time; by integrating various sensors and an advanced data processing algorithm, the real-time performance and accuracy of river flood monitoring are remarkably improved, the monitoring range is not limited by a fixed monitoring station due to the flexibility of the unmanned aerial vehicle, and a wide area can be rapidly covered.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicle (UAV) flood measurement, and particularly relates to a river flood measurement system based on a UAV. Background Art

[0002] For considerations such as water resource management, flood control and disaster reduction, and ecological environment protection, it is necessary to measure hydrological elements such as water level, flow velocity, and flow rate during floods at specific cross-sections of rivers. River flood measurement is an important task in hydrology and hydraulic engineering.

[0003] Traditional river flood monitoring relies on manual measurement or fixed hydrological monitoring stations. These methods have problems such as limited monitoring range, untimely data update, and high cost. Especially in emergency situations such as floods, quickly and accurately obtaining flood flow data is crucial for flood control and disaster reduction.

[0004] Therefore, those skilled in the art have proposed a river flood measurement system based on a UAV to solve the problems raised in the background art. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides a river flood measurement system based on a UAV to solve the problems in the prior art that traditional river flood monitoring relies on manual measurement or fixed hydrological monitoring stations, and these methods have problems such as limited monitoring range, untimely data update, and high cost.

[0006] A river flood measurement system based on a UAV includes

[0007] An automatic flight control module for planning and executing the flight path of the UAV;

[0008] A Beidou satellite positioning module for controlling the position of specific measurement vertical lines;

[0009] A sensor integration module for integrating radar wave depth sounding and velocity sensors for measuring water depth and flow velocity;

[0010] A data fusion module for fusing the data collected by the sensor integration module, automatically eliminating invalid data, and calculating and generating flow data according to set conditions, empirical parameters, and formulas;

[0011] An intelligent data analysis module for analyzing flow data;

[0012] An environmental adaptability module for realizing real-time obstacle avoidance and environmental perception;

[0013] A user interface and data visualization module for real-time monitoring of the UAV status and viewing data.

[0014] Preferably, the data fusion module uses the Kalman filter algorithm to fuse data, and the Kalman filter algorithm is as follows; Wherein, is the state estimate at time k, is the state prediction at time K based on previous information, K k is the Kalman gain, z k is the measurement value at time k, and H is the observation matrix.

[0015] Preferably, the automatic flight control module plans the flight path based on the genetic algorithm, and the genetic algorithm is as follows: Wherein, f(x) is the fitness function, N is the number of points in the path, x i is the i-th point in the path, x * is the target point, and β is the parameter that controls the shape of the function.

[0016] Preferably, the intelligent data analysis module constructs a model for flood prediction by combining PCA dimensionality reduction and the XGBoost algorithm.

[0017] Preferably, the environmental adaptability module integrates a lidar system for real-time obstacle avoidance and environmental perception. The lidar system measures distances by emitting and receiving laser pulses and provides high-precision spatial information.

[0018] Preferably, the user interface and data visualization module are developed based on the Web for real-time monitoring of the UAV status and viewing data.

[0019] Preferably, it further includes an energy management unit for optimizing the energy consumption of the UAV and ensuring the energy supply during the execution of the flood measurement task.

[0020] Preferably, it further includes a communication encryption module for providing security protection when transmitting data between the UAV and the ground control station.

[0021] Preferably, it further includes an intelligent fault diagnosis unit for real-time monitoring of the system status, predicting potential faults, and providing maintenance suggestions.

[0022] Preferably, it further includes an environmental impact assessment tool for evaluating the impact of UAV operations on the surrounding environment and adjusting operations to minimize adverse effects.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] 1. The present invention significantly improves the real-time performance and accuracy of river flood monitoring by integrating multiple sensors and advanced data processing algorithms. The flexibility of the unmanned aerial vehicle (UAV) enables the monitoring range to be no longer limited to fixed monitoring stations, and it can quickly cover a wide area. It can also operate even under adverse weather conditions, not only improving the efficiency and accuracy of flood monitoring, but also reducing the operation cost, providing strong technical support for flood control and disaster reduction.

[0025] 2. In the present invention, the data fusion module uses the Kalman filtering algorithm to improve the accuracy and robustness of sensor data. The automatic flight control module plans the flight path based on the genetic algorithm, optimizing the data acquisition efficiency and coverage. The intelligent data analysis module analyzes images using a convolutional neural network to automatically identify flood features, reducing the need for manual analysis.

[0026] 3. In the present invention, the environmental adaptability module and the energy management unit ensure the safe flight and energy efficiency of the UAV in complex environments, while the communication encryption module and the intelligent fault diagnosis unit improve the security and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a schematic diagram of a river flood measurement system based on a UAV. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] The following further describes in detail the embodiments of the present invention in conjunction with the drawings and examples. The following examples are used to illustrate the present invention, but cannot be used to limit the scope of the present invention.

[0029] Embodiment 1

[0030] As shown in the attached Figure 1 figure, the present invention provides a river flood measurement system based on a UAV. By integrating multiple sensors and advanced data processing technologies on the UAV, the flexibility and mobility of the UAV enable monitoring to no longer be limited to fixed monitoring stations, and it can cover a wider area. Especially in river areas with complex terrain or difficult access, it can quickly respond during floods and provide real-time and accurate flood flow data, thus providing a scientific basis for flood control and disaster reduction. It can overcome the limitations of traditional hydrological monitoring methods, such as limited monitoring range, untimely data update, high cost, etc., including,

[0031] An automatic flight control module for planning and executing the flight path of the UAV to ensure coverage of all key monitoring areas. The automatic flight control module automatically plans the optimal flight path according to monitoring requirements to improve data acquisition efficiency;

[0032] A Beidou satellite positioning module for position control of specific measurement vertical lines. The Beidou positioning module can provide accurate geographical location information;

[0033] A sensor integration module is used to integrate radar wave depth sounding and velocity measuring sensors for measuring water depth and flow velocity. The radar wave depth sounding and velocity measuring sensors are used to measure the flow velocity and depth of water bodies. The combination of these sensors can provide more abundant data;

[0034] A data fusion module is used to fuse the data collected from the sensor integration module, automatically eliminate invalid data, and calculate and generate flow data according to set conditions, empirical parameters and formulas. By integrating multi-source information through algorithms, it provides more comprehensive river flood characteristics. By integrating data from different sensors through the data fusion module, the accuracy and integrity of the data are improved;

[0035] An intelligent data analysis module is used to analyze the flow data, identify flood areas and evaluate flood impacts. The intelligent data analysis module automatically identifies flood characteristics through deep learning technology, reducing the workload and time of manual analysis;

[0036] An environmental adaptability module is used to achieve real-time obstacle avoidance and environmental perception, ensuring the safe flight of the UAV in complex environments, enabling operators to easily monitor the UAV status and view data;

[0037] A user interface and data visualization module is used to monitor the UAV status and view data in real time, providing an intuitive operation interface and data display, enabling operators to easily monitor the UAV status and view data.

[0038] As can be seen from the above, the flexibility and mobility of the UAV in the present invention enable the monitoring to no longer be limited to fixed monitoring sites and can cover a wider area. Especially in river areas with complex terrain or difficult access, the system integrates data from different sensors through the data fusion module, improving the accuracy and integrity of the data. The automatic flight control module can automatically plan the optimal flight path according to monitoring requirements, improving the data collection efficiency. The intelligent data analysis module automatically identifies flood characteristics through deep learning technology, reducing the workload and time of manual analysis. The environmental adaptability module ensures the safe flight of the UAV in complex environments. The user interface and data visualization module provides an intuitive operation interface and data display, enabling operators to easily monitor the UAV status and view data.

[0039] Embodiment 2

[0040] This embodiment is basically the same as the previous embodiment, except that the data fusion module uses the Kalman filter algorithm to fuse data. The Kalman filter algorithm is as follows; Wherein, is the state estimate at time k, is the state prediction at time K based on previous information, K kis the Kalman gain, z k is the measurement at time k, H is the observation matrix. By dynamically adjusting the Kalman gain to adapt to different measurement conditions and system dynamics, more accurate system state estimation is provided. The data fusion module is a core component of this system. It fuses data from multiple sensors through the Kalman filtering algorithm and can estimate the state of a dynamic system from a series of noisy measurements.

[0041] Specifically, the automatic flight control module plans the flight path based on the genetic algorithm. The genetic algorithm is as follows: where f(x) is the fitness function, N is the number of points in the path, x i is the i-th point in the path, x * is the target point, and β is the parameter that controls the shape of the function. By simulating the natural selection process, the flight path is optimized to improve the data acquisition efficiency and coverage.

[0042] Furthermore, the intelligent data analysis module combines PCA dimensionality reduction and the XGBoost algorithm to build a model for flood prediction. In flood prediction, PCA can effectively reduce the mutual dependence between input variables, reduce the data dimension, and at the same time remove noise. The XGBoost algorithm performs well in dealing with nonlinear problems and large datasets and can reduce the risk of overfitting.

[0043] Furthermore, the environmental adaptability module integrates a lidar system for real-time obstacle avoidance and environmental perception. The lidar system measures distances by emitting and receiving laser pulses and provides high-precision spatial information, which enables the UAV to achieve real-time obstacle avoidance and environmental perception in complex environments and ensures the safe flight of the UAV in complex environments.

[0044] Furthermore, the user interface and data visualization module are developed based on the Web for real-time monitoring of the UAV state and viewing data. The user interface and data visualization module provide a graphical display of the real-time data stream. Operators can use it to monitor the state of the UAV in real time, such as flight altitude, speed, battery power, etc., and view sensor data and image analysis results.

[0045] As can be seen from the above, the rapid deployment and mobility of the UAV greatly improve the monitoring efficiency. Especially in emergency situations, key data can be quickly obtained. Through multi-sensor data fusion and advanced data processing algorithms, this system can provide more accurate and complete flood monitoring data. The automated and intelligent monitoring process reduces the need for manual operations, thereby reducing the monitoring cost. The environmental adaptability module and the energy management unit ensure the safe flight of the UAV in complex environments and reduce the operation risk.

[0046] Embodiment III

[0047] This embodiment is basically the same as the previous one, except that it further includes an energy management unit, which is used to optimize the energy consumption of the drone and ensure the energy supply during the flood measurement task. By monitoring the battery status and adjusting the task load, this unit can extend the flight time of the drone, ensure the continuity of critical tasks, and complete more monitoring tasks with a limited battery capacity, improving the working efficiency and practicality of the system.

[0048] Specifically, it also includes a communication encryption module, which is used to provide security protection when transmitting data between the drone and the ground control station. By adopting advanced encryption algorithms to protect data transmission, such as AES or RSA, the communication encryption module can protect the security of the data transmission process, prevent unauthorized access and data leakage.

[0049] Furthermore, it also includes an intelligent fault diagnosis unit, which is used to monitor the system status in real time, predict potential faults, and provide maintenance suggestions. By analyzing system logs and various sensor data to identify abnormal patterns, once an anomaly is detected, the system will automatically prompt the maintenance personnel to conduct an inspection, greatly improving the reliability and stability of the system and reducing monitoring interruptions caused by faults.

[0050] Furthermore, it also includes an environmental impact assessment tool, which is used to evaluate the impact of drone operations on the surrounding environment and adjust operations to minimize adverse effects. This tool will evaluate possible environmental impacts based on the flight altitude, speed of the drone, and ecological environment information of the operation area, and put forward corresponding adjustment suggestions. For example, during the bird breeding season, the system will suggest adjusting the flight path to avoid bird habitats; or in noise-sensitive areas, the system will suggest reducing the flight speed to reduce noise.

[0051] As can be seen from the above, the intelligent energy management strategy of the energy management unit enables the drone to complete more monitoring tasks with a limited battery capacity, improving the working efficiency of the system. The addition of the communication encryption module ensures the security of data transmission, prevents unauthorized access and data leakage, and protects sensitive hydrological data. The real-time monitoring and prediction functions of the intelligent fault diagnosis unit improve the reliability and stability of the system and reduce monitoring interruptions caused by faults. The introduction of the environmental impact assessment tool enables the system to reduce adverse effects on the environment.

[0052] All the standard parts used in the present invention can be purchased from the market. The special-shaped parts can be customized according to the descriptions in the specification and the attached drawings. The specific connection methods of each part all adopt conventional means such as bolts, rivets, and welding that are mature in the prior art. The machines, parts, and equipment all adopt conventional models in the prior art. In addition, the circuit connection adopts the conventional connection method in the prior art, which will not be elaborated here. The content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0053] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The meaning of "a plurality" is two or more, unless otherwise specifically defined.

[0054] In the present invention, unless otherwise clearly defined and limited, the terms such as "mounted", "connected", "coupled", "fixed", etc. shall be construed in a broad sense. For example, it may be a fixed connection, a detachable connection, or integrated; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the internal communication of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0055] In the present invention, unless otherwise clearly defined and limited, the first feature being "on" or "under" the second feature may be that the first and second features are in direct contact, or the first and second features are indirectly in contact through an intermediate medium. Moreover, the first feature being "above", "over" and "on" the second feature may be that the first feature is directly above or obliquely above the second feature, or merely indicates that the first feature is at a higher level than the second feature in terms of horizontal height. The first feature being "under", "beneath" and "under" the second feature may be that the first feature is directly below or obliquely below the second feature, or merely indicates that the first feature is at a lower level than the second feature in terms of horizontal height.

[0056] In the description of this specification, the description referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0057] In the accompanying drawings of the disclosed embodiments of the present invention, only the structures related to the disclosed embodiments are involved, and other structures can refer to the general design. Without conflict, the same embodiment and different embodiments of the present invention can be combined with each other.

[0058] Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A river flood testing system based on drones, characterized by: include, An automated flight control module for planning and executing the drone’s flight path; Beidou satellite positioning module, used for position control of specific measurement vertical lines; Sensor integration module, used to integrate radar wave depth and velocity sensors to measure water depth and flow velocity; The data fusion module is used to fuse the data collected from the sensor integration module, automatically eliminate invalid data and generate flow data according to the set conditions, empirical parameters and formula calculations; Intelligent data analysis module, which conducts in-depth analysis of flow data to predict flood peaks, flow change trends, etc. Environmental adaptability module, used to achieve real-time obstacle avoidance and environmental perception; User interface and data visualization module for real-time monitoring of drone status and viewing of data.

2. A river flood testing system based on an unmanned aerial vehicle as claimed in claim 1, characterized in that: The data fusion module uses a Kalman filter algorithm to fuse data, and the Kalman filter algorithm is as follows; in, is the state estimate at time k, is the state prediction at time K based on previous information, K k is the Kalman gain, z k is the measurement at time k, and H is the observation matrix.

3. A river flood testing system based on an unmanned aerial vehicle as claimed in claim 2, characterized in that: The automated flight control module plans the flight path based on a genetic algorithm, which is as follows: Where f(x) is the fitness function, N is the number of points in the path, and x i is the i-th point in the path, x * is the target point and β is the parameter that controls the shape of the function.

4. A river flood testing system based on an unmanned aerial vehicle as claimed in claim 3, characterized in that: The intelligent data analysis module combines PCA dimension reduction and XGBoost algorithm to build a model for flood prediction.

5. A river flood testing system based on an unmanned aerial vehicle as claimed in claim 4, characterized in that: The environmental adaptability module integrates a laser radar system to achieve real-time obstacle avoidance and environmental perception. The laser radar system measures distance by transmitting and receiving laser pulses to provide high-precision spatial information.

6. A river flood testing system based on an unmanned aerial vehicle as claimed in claim 5, characterized in that: The user interface and data visualization module are developed based on the Web and are used to monitor the status of drones and view data in real time.

7. A river flood testing system based on an unmanned aerial vehicle as claimed in claim 6, characterized in that: It also includes an energy management unit to optimize the drone's energy consumption and ensure energy supply during flood testing missions.

8. A river flood testing system based on an unmanned aerial vehicle as claimed in claim 7, characterized in that: It also includes a communication encryption module to provide security when transmitting data between the drone and the ground control station.

9. A river flood testing system based on an unmanned aerial vehicle as claimed in claim 8, characterized in that: It also includes an intelligent fault diagnosis unit to monitor system status in real time, predict potential failures, and provide maintenance recommendations.

10. A river flood testing system based on an unmanned aerial vehicle as claimed in claim 9, characterized in that: Also included are environmental impact assessment tools to evaluate the impact of drone operations on the surrounding environment and adjust operations to minimize adverse effects.

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