Water conservancy unmanned aerial vehicle inspection process control system and method based on big data analysis

Abstract

The present application relates to the technical field of path planning, in particular to a water conservancy unmanned aerial vehicle inspection process control system and method based on big data analysis, the present application carries out water surface anomaly prediction based on the water surface conditions of each region and the weather conditions of each region, carries out path risk assessment based on the water surface anomaly prediction result and the weather anomaly analysis result, carries out unmanned aerial vehicle flight anomaly analysis based on unmanned aerial vehicle operation condition data and historical path risk analysis result, carries out path planning selection, the present application quantitatively evaluates the path risk based on the evolution of weather and wave conditions on the path in the process of unmanned aerial vehicle flight, improves the safety of path selection, in the process of evaluating wave conditions, the influence of future wind conditions on wave risk is analyzed, and then the future wave risk is quantitatively predicted and evaluated, which improves the evaluation accuracy of path risk.

Description

Technical Field

[0001] This invention relates to the field of path planning technology, and in particular to a process control system and method for water conservancy unmanned aerial vehicle (UAV) inspection based on big data analysis. Background Technology

[0002] The stable operation of water conservancy facilities is crucial for ensuring the rational use of water resources, flood control and disaster reduction, and sustainable socio-economic development. Traditional water conservancy inspections mainly rely on manual methods, requiring inspectors to personally inspect water areas and related facilities. However, this method has many limitations. On the one hand, water conservancy facilities are widely distributed, and some areas have complex environments, such as reservoirs in remote mountainous areas and rivers with steep terrain, making manual inspections difficult, inefficient, and posing certain safety risks. On the other hand, manual inspections cannot achieve real-time, dynamic monitoring of large areas of water, and cannot promptly detect potential safety hazards. With the development of technology, drone technology is gradually being applied to the field of water conservancy inspections. Drones have advantages such as high flexibility and the ability to quickly reach designated areas, which can, to some extent, compensate for the shortcomings of manual inspections. By carrying various sensors, drones can collect and monitor data on water surface conditions and the operational status of related facilities.

[0003] Currently, most drone inspection path planning is based on static information, such as preset fixed routes or approximate ranges determined by historical data. This planning method does not fully consider the dynamic changes in the water environment. In terms of water surface conditions, wave conditions are dynamic and affected by various factors, such as wind and water flow. Different wave sizes and shapes have a significant impact on drone flight safety. For example, larger waves may generate strong airflow disturbances, affecting the drone's flight stability. However, existing path planning systems often ignore the dynamic changes in wave conditions and cannot accurately assess their potential hazards to drone flight. In addition, existing systems rarely consider the drone's own operating conditions when planning paths. Factors such as the drone's battery level, flight attitude, and equipment status all affect its flight capability and safety. If these factors are not fully considered during path planning, the drone may malfunction during flight or fail to complete the inspection task. Therefore, there is an urgent need for a water conservancy drone inspection process control system and method based on big data analysis. Summary of the Invention

[0004] In order to overcome the defects and shortcomings of existing technologies, this invention provides a process control system and method for water conservancy unmanned aerial vehicle (UAV) inspection based on big data analysis.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] In a first aspect, the present invention provides a method for controlling the inspection process of water conservancy drones based on big data analysis, comprising the following steps:

[0007] Step S1: Obtain the water surface conditions and weather conditions of each area to be inspected, and at the same time obtain the drone operation status;

[0008] Step S2: Based on the water surface conditions and weather conditions in each region, predict water surface anomalies;

[0009] Step S3: Conduct a path hazard assessment based on the water surface anomaly prediction results and the weather anomaly analysis results;

[0010] Step S4: Based on the UAV operation data and historical path hazard analysis results, conduct UAV flight anomaly analysis and plan and select a path.

[0011] In one implementation of the present invention, step S1 includes the following specific contents:

[0012] S11. Obtain the height, shape, trough depth, and area of ​​the waves along the corresponding path using wave sensors.

[0013] S12. Obtain the wind conditions of the corresponding area for the drone's flight time through the weather forecast component, whereby the wind conditions include wind speed and wind direction.

[0014] S13. Acquire flight data of the UAV during its historical operation, including the swaying of the UAV during flight and the deviation of its flight trajectory. The swaying of the UAV during flight and the deviation of its flight trajectory are acquired through flight sensors.

[0015] In one implementation of the present invention, the water surface anomaly prediction in step S2 includes the following specific steps:

[0016] S21. Based on the obtained information regarding the height, shape, trough depth, and area of ​​the waves along the corresponding path, a surge hazard assessment is performed. The formula for assessing the surge hazard in the corresponding area along the path is as follows: Where n is the number of wave parameters, including wave height, trough depth, and area; Ti is the value of the i-th wave parameter; Ts is the standard value of the i-th wave parameter; and ci is the influence weight of the i-th wave parameter, obtained experimentally based on historical data. The set standard angle value, X represents the wave angle, and X is the surge hazard assessment value. The larger the value, the higher the surge hazard. The sharpness of the wave crest is a key indicator of the surge's intensity. A sharp crest usually indicates a more violent surge, while a smooth crest may mean a relatively weak surge. The actual measured wave height is an important and intuitive indicator of the surge's intensity. The higher the wave, the greater the energy it contains. The area of ​​the wave trough reflects the range of the wave's horizontal expansion.

[0017] S22. Obtain the wind force and direction conditions for the corresponding area, as well as the angle between the wave transmission and the wind direction transmission in the corresponding area. Based on the wind force conditions and the angle between the wave transmission and the wind direction transmission in the corresponding area, perform a wind force impact analysis. The formula for the wind force impact analysis of the corresponding area is: Where Fs represents the wind conditions in the corresponding area. Fm is the cosine of the angle between the wind force and the wave propagation direction relative to the wind direction in the corresponding area, reflecting the relative relationship between the wind direction and the wave propagation direction. Fm is the standard wind force value. This is the wind influence coefficient, used to represent the weight of wind's influence on wave transmission;

[0018] S23. Obtain the wind force and direction conditions at the time the drone passes through the corresponding area, and substitute them into the wind force impact analysis formula for the corresponding area to calculate the wind force impact analysis result fc at the time the drone passes through the corresponding area. Use the obtained wind force impact analysis results at the time the drone passes through the corresponding area, the real-time wind force impact analysis results for the corresponding area, and the surge hazard assessment results for the corresponding area to conduct a surge hazard assessment of the corresponding area during its passage. The surge hazard assessment formula for the corresponding area during its passage is: By quantifying the impact of wind on surges, the surge hazard in the corresponding area when a drone passes by can be predicted.

[0019] In one implementation of the present invention, the path hazard assessment in step S3 includes the following specific steps:

[0020] S31. Obtain the wind conditions of the corresponding area during the UAV's flight time, and perform wind impact analysis based on the wind conditions of the corresponding area during the corresponding flight time. The formula for the wind impact analysis of the k-th area is: Fk represents the wind force in the k-th region. Let Fc be the cosine of the drone's flight direction and the wind direction, and Fc be the wind safety value. The coefficient representing the influence of longitudinal wind on the drone. The coefficient representing the influence of lateral wind on the drone;

[0021] S32. Obtain the surge hazard assessment results and wind impact analysis results in the corresponding area, and then perform a weighted summation to obtain the hazard assessment results for the corresponding area.

[0022] S33. Obtain the hazard assessment results of all areas along the path and overlay them to obtain the path hazard assessment results for the corresponding path.

[0023] In one implementation of the present invention, the UAV flight anomaly analysis in step S4 includes the following specific contents:

[0024] S41. Obtain the swaying and trajectory deviation of the UAV during flight. Based on the swaying and trajectory deviation, assess the UAV flight anomaly. The formula for assessing the UAV flight anomaly is: Where c is the weight of trajectory deviation, Xr is the distance of UAV trajectory deviation, Xm is the trajectory deviation safety value, M is the number of swings, Lj is the swing amplitude of the j-th swing, and Lm is the swing amplitude safety value. By combining trajectory deviation and flight swing, the degree of abnormality of flight status is quantified.

[0025] S42. Obtain the flight anomaly assessment results, path hazard assessment results, and future path hazard assessment results of the corresponding UAV during flight. Substitute these results into the future path flight anomaly calculation formula to calculate the future path flight anomaly. The future path flight anomaly calculation formula is as follows: In this formula, Prc represents the flight anomaly assessment result during flight, Lwz represents the path hazard assessment result for the future path, and Lwc represents the path hazard assessment result during flight. This formula analyzes the flight anomalies of the future path by examining the impact of path hazard on the UAV's flight state. The formula quantifies the coupling effect between current anomalies and changes in path hazard. If the future path is more dangerous, the future anomaly value is amplified; conversely, it is attenuated.

[0026] S43. Obtain the corresponding future path flight anomaly analysis results, and compare the future path flight anomaly analysis results with the set future path flight anomaly analysis threshold. If the future path flight anomaly analysis results are greater than or equal to the set future path flight anomaly analysis threshold, it indicates that the path is dangerous and the path needs to be replanned. If the future path flight anomaly analysis results are less than the set future path flight anomaly analysis threshold, it indicates that the path is safe.

[0027] Secondly, the present invention also provides a water conservancy unmanned aerial vehicle (UAV) inspection process control system based on big data analysis, including:

[0028] The acquisition module is used to acquire the water surface conditions and weather conditions of each area that needs to be inspected, as well as the drone's operational status.

[0029] The water surface anomaly prediction module predicts water surface anomalies based on the water surface conditions and weather conditions in each region.

[0030] The path hazard assessment module assesses path hazard based on water surface anomaly prediction results and weather anomaly analysis results.

[0031] The planning and selection module analyzes drone flight anomalies based on drone operation data and historical path hazard analysis results, and then plans and selects the appropriate path.

[0032] Thirdly, the present invention provides an electronic device comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a water conservancy unmanned aerial vehicle (UAV) inspection process control method based on big data analysis by calling the computer program stored in the memory.

[0033] Fourthly, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a process control method for water conservancy unmanned aerial vehicle (UAV) inspection based on big data analysis.

[0034] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0035] This invention acquires the water surface conditions and weather conditions of each area to be inspected, and simultaneously acquires the drone operation status. Based on the water surface conditions and weather conditions of each area, it performs water surface anomaly prediction, and based on the water surface anomaly prediction results and weather anomaly analysis results, it performs drone flight anomaly analysis, and performs path planning and selection based on drone operation data and historical path hazard analysis results. This invention quantifies the path hazard assessment based on the evolution of weather and wave conditions along the path during drone flight, thereby improving the safety of path selection.

[0036] In the process of assessing wave conditions, this invention analyzes the impact of future wind conditions on wave hazard, and then quantitatively predicts and assesses future wave hazard, thereby improving the accuracy of path hazard assessment. Attached Figure Description

[0037] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0038] Figure 1 This is a schematic diagram of the overall process of an embodiment of the method of the present invention;

[0039] Figure 2 This is a schematic diagram of step S2 in an embodiment of the method of the present invention;

[0040] Figure 3This is a schematic diagram of step S3 in an embodiment of the method of the present invention;

[0041] Figure 4 This is a schematic diagram of the structure in a system embodiment of the present invention. Detailed Implementation

[0042] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0043] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0044] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0045] Example 1

[0046] like Figures 1 to 3 As shown, this embodiment provides a method for controlling the inspection process of water conservancy drones based on big data analysis, specifically including the following steps:

[0047] Step S1: Obtain the water surface conditions and weather conditions of each area to be inspected, and at the same time obtain the drone operation status;

[0048] In this embodiment, step S1 includes the following specific contents:

[0049] S11. Obtain the wave height, wave shape, and trough depth and area along the corresponding path using wave sensors. It should be noted that, for example, the wave sensors here can be: pressure sensor buoys (where pressure sensors are installed inside the buoy to calculate wave height, period, and other parameters by measuring changes in underwater pressure); optical buoys (which use optical principles to measure the surface shape of waves); or shore-based radar (where radar installed on the coastline can emit electromagnetic waves towards the sea surface and determine wave parameters by analyzing the signals reflected back from the waves).

[0050] S12. Obtain the wind conditions of the corresponding area for the drone's flight time through the weather forecast component, whereby the wind conditions include wind speed and wind direction.

[0051] S13. Acquire flight data of the UAV during its historical operation, including the swaying of the UAV during flight and the deviation of the flight trajectory. The swaying of the UAV during flight and the deviation of the flight trajectory are acquired through flight sensors; the acquired data is stored in the corresponding storage components.

[0052] Step S2: Based on the water surface conditions and weather conditions in each region, predict water surface anomalies;

[0053] In this embodiment, the water surface anomaly prediction in step S2 includes the following specific steps:

[0054] S21. Based on the obtained information regarding the height, shape, trough depth, and area of ​​the waves along the corresponding path, a surge hazard assessment is performed. The formula for assessing the surge hazard in the corresponding area along the path is as follows: Where n is the number of wave parameters, including wave height, trough depth, and area; Ti is the value of the i-th wave parameter; Ts is the standard value of the i-th wave parameter; and ci is the influence weight of the i-th wave parameter, obtained experimentally based on historical data. The set standard angle value, X represents the wave angle, and X is the surge hazard assessment value. The larger the value, the higher the surge hazard. The sharpness of wave crests indicates the severity of the surge, while smooth crests may indicate a relatively weak surge. The actual measured wave height is an important and intuitive indicator of surge intensity. The higher the wave, the greater the energy it contains. The trough area reflects the horizontal extent of the wave. The standard value of the i-th parameter of the wave is determined based on the drone's flight conditions. It is obtained by setting the standard value of the i-th parameter of the wave to be greater than the specific parameters of the drone's flight conditions, and the trough area of ​​the wave to be greater than the approximate area of ​​the drone's rotor plane. For example, if the drone's flight altitude is 5m, then the maximum standard value of the wave height is set to 5m. Setting parameters based on the drone's flight conditions is beneficial for analyzing the impact of drones on different missions.

[0055] S22. Obtain the wind force and direction conditions for the corresponding area, as well as the angle between the wave transmission and the wind direction transmission in the corresponding area. Based on the wind force conditions and the angle between the wave transmission and the wind direction transmission in the corresponding area, perform a wind force impact analysis. The formula for the wind force impact analysis of the corresponding area is: Where Fs represents the wind conditions in the corresponding area. Fm is the cosine of the angle between the wind force and the wave propagation direction relative to the wind direction in the corresponding area, reflecting the relative relationship between the wind direction and the wave propagation direction. Fm is the standard wind force value. This is the wind influence coefficient, used to represent the weight of wind's influence on wave transmission;

[0056] S23. Obtain the wind force and direction conditions at the time the drone passes through the corresponding area, and substitute them into the wind force impact analysis formula for the corresponding area to calculate the wind force impact analysis result fc at the time the drone passes through the corresponding area. Use the obtained wind force impact analysis results at the time the drone passes through the corresponding area, the real-time wind force impact analysis results for the corresponding area, and the surge hazard assessment results for the corresponding area to conduct a surge hazard assessment of the corresponding area during its passage. The surge hazard assessment formula for the corresponding area during its passage is: By quantifying the impact of wind on surges, the surge hazard in the corresponding area when a drone passes by can be predicted.

[0057] Step S3: Conduct a path hazard assessment based on the water surface anomaly prediction results and the weather anomaly analysis results;

[0058] In this embodiment, the path hazard assessment in step S3 includes the following specific steps:

[0059] S31. Obtain the wind conditions of the corresponding area during the UAV's flight time, and perform wind impact analysis based on the wind conditions of the corresponding area during the corresponding flight time. The formula for the wind impact analysis of the k-th area is: Fk represents the wind force in the k-th region. Let Fc be the cosine of the drone's flight direction and the wind direction, and Fc be the wind safety value. The coefficient representing the influence of longitudinal wind on the drone. The influence coefficient of lateral wind force on the drone is obtained from drone experiments. Since different types of drones have different effects on resisting longitudinal and lateral wind forces, the drone is fixed in the wind tunnel, the wind speed and wind direction are adjusted, and a six-axis force sensor is used to measure the change of longitudinal thrust and lateral offset force of the drone. Data fitting is performed to measure the thrust change under different combinations, and the influence coefficient is obtained by fitting.

[0060] S32. Obtain the surge hazard assessment results and wind impact analysis results for the corresponding area, and then perform a weighted summation to obtain the hazard assessment result for the corresponding area. This is because relying solely on wind or surge data may lead to misjudgment (e.g., a sea area with low wind but high surge is still dangerous). Weighted summation can integrate two key environmental factors and more comprehensively reflect the true risk. Regional characteristics are adapted: For example, open sea areas: surge weight is high; nearshore / port areas: wind weight is high (because surge is weakened by terrain). Task type is adapted: logistics drones: focus on wind (high crosswind resistance is required); marine monitoring drones: focus on surge.

[0061] S33. Obtain the hazard assessment results of all areas on the path and overlay them to obtain the path hazard assessment results for the corresponding path;

[0062] Step S4: Based on the UAV operation data and historical path hazard analysis results, conduct UAV flight anomaly analysis and plan and select the path.

[0063] In this embodiment, the UAV flight anomaly analysis in step S4 includes the following specific contents:

[0064] S41. Obtain the swaying and trajectory deviation of the UAV during flight. Based on the swaying and trajectory deviation, assess the UAV flight anomaly. The formula for assessing the UAV flight anomaly is: Where c is the weight of trajectory deviation, Xr is the distance of UAV trajectory deviation, Xm is the trajectory deviation safety value, M is the number of swings, Lj is the swing amplitude of the j-th swing, and Lm is the swing amplitude safety value. By combining trajectory deviation and flight swing, the degree of abnormality of flight status is quantified.

[0065] S42. Obtain the flight anomaly assessment results, path hazard assessment results, and future path hazard assessment results of the corresponding UAV during flight. Substitute these results into the future path flight anomaly calculation formula to calculate the future path flight anomaly. The future path flight anomaly calculation formula is as follows: In this formula, Prc represents the flight anomaly assessment result during flight, Lwz represents the path hazard assessment result for the future path, and Lwc represents the path hazard assessment result during flight. This formula analyzes the flight anomalies of the future path by examining the impact of path hazard on the UAV's flight state. The formula quantifies the coupling effect between current anomalies and changes in path hazard. If the future path is more dangerous, the future anomaly value is amplified; conversely, it is attenuated.

[0066] S43. Obtain the corresponding future path flight anomaly analysis results, compare the future path flight anomaly analysis results with the set future path flight anomaly analysis threshold. If the future path flight anomaly analysis results are greater than or equal to the set future path flight anomaly analysis threshold, it indicates that the path is dangerous and the path needs to be replanned. If the future path flight anomaly analysis results are less than the set future path flight anomaly analysis threshold, it indicates that the path is safe.

[0067] In this embodiment, the values ​​of the set parameters can all be obtained through experiments. The preferred experimental method is to obtain the water surface conditions and weather conditions of each area to be inspected for 500 sets, and at the same time obtain the operation status of the UAV. Substitute these into the various steps of this embodiment for analysis, calculation and comparison to obtain the calculation results of whether the path is dangerous. At the same time, obtain the factual results of whether a dangerous accident occurred when the UAV actually flew the corresponding path. Import the obtained calculation results and factual results into the MATLAB fitting software, use the polynomial fitting function or the nonlinear fitting function to fit the results, and output the values ​​of the set parameters that meet the maximum accuracy of the factual results.

[0068] In this embodiment, it should be noted that the following advantages are available: It acquires the water surface conditions and weather conditions of each area to be inspected, and simultaneously acquires the drone's operational status. Based on the water surface conditions and weather conditions of each area, it performs water surface anomaly prediction; based on the water surface anomaly prediction results and weather anomaly analysis results, it performs path hazard assessment; based on the drone's operational data and historical path hazard analysis results, it performs drone flight anomaly analysis and performs path planning and selection. This invention quantifies the path hazard assessment based on the evolution of weather and wave conditions along the path during drone flight, improving the safety of path selection. In the wave condition assessment process, it analyzes the impact of future wind conditions on wave hazard, and then quantifies and predicts future wave hazard, improving the accuracy of path hazard assessment.

[0069] Example 2

[0070] like Figure 4As shown, this embodiment provides a water conservancy drone inspection process control system based on big data analysis, implemented based on the water conservancy drone inspection process control method based on big data analysis in Embodiment 1. It includes: an acquisition module for acquiring the water surface conditions and weather conditions of each area to be inspected, and simultaneously acquiring the drone's operational status; a water surface anomaly prediction module for predicting water surface anomalies based on the water surface conditions and weather conditions of each area; a path hazard assessment module for assessing path hazard based on the water surface anomaly prediction results and weather anomaly analysis results; and a planning and selection module for analyzing drone flight anomalies based on drone operational data and historical path hazard analysis results, and planning and selecting a path. The specific steps of each module in this embodiment are the same as those in the method embodiment of Embodiment 1, and will not be repeated here.

[0071] Example 3

[0072] An electronic device according to an embodiment of the present invention includes a processor and a memory, wherein the memory stores a computer program that can be called by the processor. The processor executes a water conservancy drone inspection process control method based on big data analysis by calling the computer program stored in the memory. It should be noted that all computer programs for the water conservancy drone inspection process control method based on big data analysis are implemented using the C language.

[0073] Example 4

[0074] This embodiment proposes a computer-readable storage medium on which an erasable and rewritable computer program is stored.

[0075] When the computer program runs on the computer device, it causes the computer device to execute the above-mentioned water conservancy drone inspection process control method based on big data analysis.

[0076] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network and / or wireless network. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives (SSDs).

[0077] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0078] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0079] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only one method, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0080] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0081] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0082] In the description of this specification, references to terms such as "an embodiment," "example," and "specific example" indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0083] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for controlling the inspection process of water conservancy drones based on big data analysis, characterized in that, Includes the following steps: Step S1: Obtain the water surface conditions and weather conditions of each area to be inspected, and at the same time obtain the drone operation status; Step S2: Based on the water surface conditions and weather conditions in each region, predict water surface anomalies; The water surface anomaly prediction includes the following specific steps: The wave height, wave shape, and wave trough depth and area along the corresponding path are obtained, and a surge hazard assessment is performed based on the obtained wave height, wave shape, and wave trough depth and area along the corresponding path. Obtain the wind force and wind direction conditions in the corresponding area, as well as the angle between the wave transmission and the wind direction transmission in the corresponding area. Based on the wind force conditions and the angle between the wave transmission and the wind direction transmission in the corresponding area, conduct a wind force impact analysis. The wind force and direction of the drone at the time it passes through the corresponding area are obtained and substituted into the wind force impact analysis formula of the corresponding area to calculate the wind force impact analysis result of the drone at the time it passes through the corresponding area. The obtained wind force impact analysis result of the drone at the time it passes through the corresponding area, the real-time wind force impact analysis result of the corresponding area, and the surge hazard assessment result of the corresponding area are used to conduct a surge hazard assessment of the corresponding area when the drone passes through. Step S3: Conduct a path hazard assessment based on the water surface anomaly prediction results and the weather anomaly analysis results; The route hazard assessment includes the following specific steps: Obtain the wind conditions in the corresponding area during the drone's flight time, and conduct a wind impact analysis based on the wind conditions in the corresponding area during the corresponding flight time. The surge hazard assessment results and wind impact analysis results for the corresponding area are obtained by weighted summation to obtain the hazard assessment results for the corresponding area; The hazard assessment results of all areas along the route are obtained by overlaying them to obtain the route hazard assessment result for the corresponding route; Step S4: Based on the UAV operation data and historical path hazard analysis results, conduct UAV flight anomaly analysis and plan and select the path. The analysis of drone flight anomalies includes the following specific contents: Acquire the swaying and trajectory deviation of the drone during flight, and conduct anomaly assessment of the drone flight based on the swaying and trajectory deviation during flight. Obtain the flight anomaly assessment results, path hazard assessment results, and future path hazard assessment results of the corresponding UAV during flight, and substitute them into the future path flight anomaly calculation formula to calculate the future path flight anomaly. Obtain the corresponding future path flight anomaly analysis results, and compare the results with the set future path flight anomaly analysis threshold. If the results are greater than or equal to the set threshold, the path is considered dangerous and needs to be replanned. If the results are less than the set threshold, the path is considered safe.

2. The method for controlling the inspection process of water conservancy drones based on big data analysis according to claim 1, characterized in that, The formula for assessing the surge hazard in the corresponding area along the path is as follows: Where n is the number of wave parameters, including wave height, trough depth, and area; Ti is the value of the i-th wave parameter; Ts is the standard value of the i-th wave parameter; and ci is the influence weight of the i-th wave parameter, obtained experimentally based on historical data. The set standard angle value, X represents the wave angle, and X represents the surge hazard assessment value.

3. The method for controlling the inspection process of water conservancy drones based on big data analysis according to claim 1, characterized in that, The formula for analyzing the wind impact in the k-th region is: Fk represents the wind force in the k-th region. Let Fc be the cosine of the drone's flight direction and the wind direction, and Fc be the wind safety value. The coefficient representing the influence of longitudinal wind on the drone. The coefficient representing the influence of lateral wind on the drone.

4. The method for controlling the inspection process of water conservancy drones based on big data analysis according to claim 1, characterized in that, Step S1 includes the following specific contents: Wave sensors are used to obtain information on the height, shape, trough depth, and area of ​​waves along the corresponding path. The wind conditions in the corresponding area for the drone's flight time are obtained through the weather forecast component, including wind speed and wind direction. Data on the drone's flight history is acquired, including the drone's swaying and trajectory deviation during flight. The swaying and trajectory deviation are acquired through flight sensors.

5. A process control system for water conservancy unmanned aerial vehicle (UAV) inspection based on big data analysis, implemented based on the process control method for water conservancy UAV inspection based on big data analysis as described in any one of claims 1-4, characterized in that... The system includes: The acquisition module is used to acquire the water surface conditions and weather conditions of each area that needs to be inspected, as well as the drone's operational status. The water surface anomaly prediction module predicts water surface anomalies based on the water surface conditions and weather conditions in each region. The path hazard assessment module assesses path hazard based on water surface anomaly prediction results and weather anomaly analysis results. The planning and selection module analyzes drone flight anomalies based on drone operation data and historical path hazard analysis results, and then plans and selects the appropriate path.

6. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the water conservancy unmanned aerial vehicle inspection process control method based on big data analysis as described in any one of claims 1-4 by calling the computer program stored in the memory.

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