Multi-mode unmanned aerial vehicle for breeding and control method thereof

Abstract

The invention provides a multi-mode unmanned aerial vehicle for breeding and a control method thereof, and relates to the field of automatic control of unmanned aerial vehicles in an aquaculture environment. The unmanned aerial vehicle comprises a vehicle body; the sensor assembly comprises a visual sensor, a temperature sensor, a wind speed sensor and an air pressure sensor, and the sensor assembly is arranged on the machine body; the control module is used for collecting environment information and fishpond image information, controlling the flight height of the machine body according to the environment information, and controlling the oxygenation equipment to carry out oxygenation according to the environment information and the fishpond image information; wherein the control module comprises an acquisition unit, a flight control unit and a demand processing unit. Multi-modal data is utilized, the intelligent level of breeding management is improved, monitoring is comprehensive, manual intervention is reduced, the oxygenation amount can be accurately determined, and a technical innovation example is provided for intelligent breeding.

Description

Technical Field

[0001] The present invention relates to the field of automatic control of unmanned aerial vehicles in an aquaculture environment, and in particular to a multi-modal aquaculture unmanned aerial vehicle and a control method thereof. Background Art

[0002] With the development of modern agricultural technology, aquaculture is moving towards intensification and intelligence. However, traditional aquaculture management methods usually rely on manual observation and manual operation, which is not only labor-intensive, but also prone to inadequate monitoring or untimely response when environmental conditions are complex or the scale of aquaculture is large, resulting in increased risks of aquaculture such as fish hypoxia and death, thereby reducing economic benefits.

[0003] Fish floating is a common sign of hypoxia in the aquaculture process. The traditional judgment method relies on manual inspection, but due to environmental influences and the limitations of observation experience, there may be errors in judgment. In addition, the oxygen supply of the aeration equipment is generally adjusted manually or set to a fixed value, which is difficult to dynamically adjust according to actual needs, which may lead to insufficient or excessive oxygen supply, waste energy and affect the balance of the water environment.

[0004] Therefore, it is necessary to provide a multimodal aquaculture drone and a control method to solve the problems of incomplete manual monitoring and inaccurate oxygenation adjustment in aquaculture in the prior art. Summary of the invention

[0005] Purpose of the invention: To propose a multimodal farming drone, and further propose a control method for the drone, so as to solve the above-mentioned problems existing in the prior art.

[0006] The first aspect of the present invention provides a multi-modal farming drone, the drone comprising:

[0007] Body;

[0008] A sensor assembly, including a visual sensor, a temperature sensor, a wind speed sensor and an air pressure sensor, wherein the sensor assembly is arranged on the machine body;

[0009] A control module is used to collect environmental information and fish pond image information, control the flight altitude of the aircraft according to the environmental information, and control the oxygenation equipment to increase oxygen according to the environmental information and the fish pond image information; wherein the control module includes a collection unit, a flight control unit, and a demand processing unit;

[0010] The acquisition unit is used to collect environmental information and fish pond image information, wherein the environmental information includes environmental temperature, wind speed and air pressure;

[0011] The flight control unit is used to determine the flight altitude of the drone based on the weight information of the drone and the wind speed, and to determine whether to adjust the flight altitude based on the air pressure. If it is determined to be adjusted, the flight altitude is adjusted based on the air pressure to obtain the final value of the flight altitude.

[0012] The demand processing unit is used to determine whether oxygenation is needed based on the fish pond image information. If it is determined that oxygenation is needed, the amount of oxygenation is determined based on the fish pond image information and the ambient temperature. The air pressure is used to determine whether the amount of oxygenation is adjusted. If it is determined that adjustment is needed, the amount of oxygenation is adjusted based on the air pressure to obtain a final value of the amount of oxygenation. The final value of the amount of oxygenation is transmitted to the oxygenation equipment, so that the oxygenation equipment oxygenates the fish pond according to the final value of the amount of oxygenation.

[0013] Furthermore, the flight control unit is used to determine the flight altitude of the UAV according to the weight information of the UAV and the wind speed, including:

[0014] The standard flight altitude of the drone is pre-set when it is unloaded and windless, and the flight altitude of the drone is calculated according to the following formula:

[0015] H=H0*kz+H0*kv;

[0016] In the above formula, H represents the flight altitude, H0 represents half of the standard flight altitude of the UAV, kz represents the influence coefficient of the body weight, and kv represents the influence coefficient of the wind speed. The value range of kz and kv is (0,1];

[0017] Set the minimum altitude. If the flight altitude is lower than the minimum altitude, it is considered that the drone flight conditions are not met.

[0018] Furthermore, the flight control unit is used to determine whether to adjust the flight altitude according to the air pressure, including:

[0019] Set the minimum air pressure value. If the air pressure is greater than or equal to the minimum air pressure value, the flight altitude will not be adjusted.

[0020] If the air pressure is lower than the minimum value, it is determined that the flight altitude needs to be adjusted.

[0021] Furthermore, if the flight control unit is used to determine that the adjustment is required, the flight altitude is adjusted according to the air pressure to obtain a final value of the flight altitude, including:

[0022] Setting a first air pressure and a second air pressure, wherein the first air pressure is higher than the second air pressure;

[0023] If the air pressure is greater than the first air pressure, the flight altitude is adjusted by the first adjustment coefficient;

[0024] If the air pressure is less than or equal to the first air pressure and greater than or equal to the second air pressure, the flight altitude is adjusted by the second adjustment coefficient;

[0025] If the air pressure is less than the second air pressure, the flight altitude is adjusted by the third adjustment coefficient;

[0026] Among them, 1>first adjustment coefficient>second adjustment coefficient>third adjustment coefficient>0.

[0027] Furthermore, the demand processing unit is used to determine whether oxygenation is needed according to the fish pond image information, including:

[0028] Splicing several fish pond image information captured by the visual sensor to obtain a complete fish pond image;

[0029] Remove noise from the complete fish pond image;

[0030] Mark the floating head area and determine whether the fish pond needs to be oxygenated based on the area of ​​the floating head area.

[0031] Furthermore, judging whether the fish pond needs to be oxygenated based on the area of ​​the floating head includes:

[0032] Set the minimum floating head area. If the floating head area is smaller than the minimum floating head area, it is determined that the fish pond will not be oxygenated.

[0033] If the floating head area is greater than or equal to the minimum floating head area, it is judged that the fish pond needs to be oxygenated.

[0034] Furthermore, when marking the floating head area, it includes:

[0035] The fish pond image is converted from RGB color space to HSV color space, and the extremely dark shadow area in the fish pond image is marked according to the following formula:

[0036] v(i,j)≤0.4;

[0037] 0.5≤h(i,j)≤0.9;

[0038] Based on the marked extremely dark shadow area, each pixel is assigned the following value:

[0039]

[0040] In the above formula, s(i,j) represents the saturation value at the coordinate (i,j) of the fish pond image; v(i,j) represents the brightness value at the coordinate (i,j) of the pixel point in the fish pond image; h(i,j) represents the hue value at the coordinate (i,j) of the pixel point in the fish pond image;

[0041] Calculate the number of pixels r in the fish pond image that meet the above assignment requirements P , the number of pixels r P Compared with the number of pixels in the complete fish pond image, the total area of ​​the floating head is obtained through pixel mapping:

[0042]

[0043] In the above formula, A R A is the total area of ​​the floating head; P is the total area of ​​the pond in the photo; R P is the total number of pixels of the complete fish pond image.

[0044] Furthermore, if it is determined that oxygenation is required, the amount of oxygenation is determined according to the fish pond image information and the ambient temperature, including:

[0045] Set the maximum temperature and calculate the oxygen increase amount based on the floating head area and ambient temperature:

[0046]

[0047] In the above formula, Z represents the amount of oxygen increase, Z0 represents the oxygen supply per unit time in the current fish pond, mf represents the floating head area, m represents the complete fish pond area, t represents the current ambient temperature, t0 represents the maximum temperature, and t>t0.

[0048] Furthermore, judging whether to adjust the oxygen increase amount according to the air pressure includes:

[0049] Set the minimum air pressure value. If the air pressure is greater than the minimum air pressure value, it is determined that there is no need to adjust the oxygen increase amount.

[0050] If the air pressure is less than or equal to the minimum air pressure value, it is determined that the oxygen addition amount needs to be adjusted.

[0051] Furthermore, if it is determined that adjustment is required, the oxygen increase amount is adjusted according to the air pressure, and when the final value of the oxygen increase amount is obtained, it includes:

[0052] Setting a third air pressure and a fourth air pressure, wherein the third air pressure is greater than the fourth air pressure;

[0053] If the air pressure is greater than the third air pressure, it is determined that the oxygen increase amount is adjusted by the first adjustment coefficient;

[0054] If the air pressure is less than or equal to the third air pressure and greater than or equal to the fourth air pressure, it is determined that the oxygen increase amount is adjusted by the second adjustment coefficient;

[0055] If the air pressure is less than the fourth air pressure, it is determined that the oxygen increase amount is adjusted by the third adjustment coefficient;

[0056] Among them, 1>first adjustment coefficient>second adjustment coefficient>third adjustment coefficient>2.

[0057] As a second aspect of the present invention, a multi-modal farming drone control method is also proposed, comprising:

[0058] A visual sensor, a temperature sensor, a wind speed sensor and an air pressure sensor are arranged on the machine body;

[0059] Collecting the environmental information and fish pond image information, wherein the environmental information includes environmental temperature, wind speed and air pressure;

[0060] Determine the flight altitude of the drone according to the weight information of the drone body and the wind speed, determine whether to adjust the flight altitude according to the air pressure, and if it is determined to be adjusted, adjust the flight altitude according to the air pressure to obtain a final value of the flight altitude;

[0061] Determine whether oxygenation is needed according to the fish pond image information; if oxygenation is needed, determine the amount of oxygenation according to the fish pond image information and the ambient temperature; determine whether to adjust the amount of oxygenation according to the air pressure; if adjustment is needed, adjust the amount of oxygenation according to the air pressure to obtain a final value of the amount of oxygenation; transmit the final value of the amount of oxygenation to the oxygenation equipment, so that the oxygenation equipment oxygenates the fish pond according to the final value of the amount of oxygenation.

[0062] Compared with the prior art, the beneficial effect of the present invention is that the present invention realizes comprehensive perception and precise control of the aquaculture environment by integrating data of multiple modes (such as image information, ambient temperature, wind speed and air pressure). First, the combination of different modal data enables the drone to fully understand the fish pond environment from multiple dimensions. For example, the image of the floating head area of ​​the fish pond is analyzed by a visual sensor, and the temperature and air pressure data collected by the environmental sensor are combined to provide accurate judgment for the fish hypoxia problem. Secondly, through multimodal information fusion, the flight altitude and oxygenation strategy can be dynamically adjusted in a complex environment. For example, the flight altitude is estimated by wind speed and body weight, and then the air pressure is corrected to ensure the stability and safety of the drone operation; at the same time, the oxygenation amount is comprehensively calculated according to the multimodal data of the floating head area, ambient temperature and air pressure, and the oxygen supply scheme is optimized. The advantage of multimodal fusion is that it can effectively reduce the uncertainty brought by single modal data and improve the reliability and accuracy of decision-making. Overall, the drone uses multimodal data to not only improve the intelligent level of aquaculture management, but also significantly optimize resource allocation, reduce manual intervention, and provide an example of technological innovation for smart aquaculture. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a functional block diagram of a multimodal farming drone provided in an embodiment of the present invention.

[0064] Figure 2 The present invention provides a flowchart of a multi-modal farming drone control method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0065] In the following description, a large number of specific details are provided to provide a more thorough understanding of the present invention. However, it is apparent to those skilled in the art that the present invention can be implemented without one or more of these details. In other examples, in order to avoid confusion with the present invention, some technical features known in the art are not described.

[0066] In some embodiments of the present application, see Figure 1 As shown, this embodiment provides a multi-modal farming drone, which consists of three parts: a body, a sensor component, and a control module.

[0067] The sensor assembly comprises a visual sensor, a temperature sensor, a wind speed sensor and an air pressure sensor, and the sensor assembly is arranged on the machine body.

[0068] The control module is used to collect environmental information and fish pond image information, control the flight altitude of the aircraft according to the environmental information, and control the oxygenation equipment to increase oxygen according to the environmental information and the fish pond image information; wherein the control module includes an acquisition unit, a flight control unit and a demand processing unit.

[0069] The acquisition unit is used to acquire environmental information and fish pond image information, wherein the environmental information includes ambient temperature, wind speed and air pressure.

[0070] The flight control unit is used to determine the flight altitude of the drone based on the weight information of the drone itself and the wind speed, and to determine whether to adjust the flight altitude based on the air pressure. If it is determined to be adjusted, the flight altitude is adjusted based on the air pressure to obtain the final value of the flight altitude.

[0071] The demand processing unit is used to determine whether oxygenation is needed based on the fish pond image information. If it is determined that oxygenation is needed, the amount of oxygenation is determined based on the fish pond image information and the ambient temperature. The air pressure is used to determine whether the amount of oxygenation is adjusted. If it is determined that adjustment is needed, the amount of oxygenation is adjusted based on the air pressure to obtain a final value of the amount of oxygenation. The final value of the amount of oxygenation is transmitted to the oxygenation equipment, so that the oxygenation equipment oxygenates the fish pond according to the final value of the amount of oxygenation.

[0072] It is understandable that the present invention realizes comprehensive perception and precise control of the breeding environment by fusing data of multiple modes (such as image information, ambient temperature, wind speed and air pressure). First, the combination of different modal data enables the drone to fully understand the fish pond environment from multiple dimensions, such as analyzing the image of the floating head area of ​​the fish pond through visual sensors, and combining the temperature and air pressure data collected by environmental sensors to provide accurate judgment for the fish hypoxia problem. Secondly, through multimodal information fusion, the flight altitude and oxygenation strategy can be dynamically adjusted in a complex environment. For example, the flight altitude is estimated by wind speed and body weight, and then the air pressure is corrected to ensure the stability and safety of the drone operation; at the same time, the oxygenation amount is comprehensively calculated according to the multimodal data of floating head area, ambient temperature and air pressure, and the oxygen supply scheme is optimized. The advantage of multimodal fusion is that it can effectively reduce the uncertainty brought by single modal data and improve the reliability and accuracy of decision-making. Overall, the drone uses multimodal data to not only improve the intelligent level of breeding management, but also significantly optimize resource allocation, reduce manual intervention, and provide an example of technological innovation for smart breeding.

[0073] In some embodiments of the present application, when the flight control unit is used to determine the flight altitude of the drone according to the weight information of the drone itself and the wind speed, it includes:

[0074] The standard flight altitude of the drone under the condition of no load and no wind is preset, and the flight altitude of the drone is calculated according to formula (1):

[0075] H=H0*kz+H0*kv (1)

[0076] H represents the flight altitude, H0 represents half of the standard flight altitude of the drone, kz represents the influence coefficient of the body weight, and kv represents the influence coefficient of the wind speed. The value range of kz and kv is (0,1];

[0077] Set the minimum altitude. If the flight altitude is lower than the minimum altitude, it is considered that the drone flight conditions are not met.

[0078] It is understandable that by presetting the standard flight altitude of the drone in the absence of load and wind, and combining the influence coefficient of the body weight and the influence coefficient of the wind speed, the flight altitude is dynamically adjusted to achieve an accurate response to the actual flight conditions. The value range of kz and kv is limited to (0, 1], which ensures the rationality and safety of the calculation results. At the same time, by setting the minimum flight altitude, the dangerous situation of flying too low due to excessive load or high wind speed can be effectively avoided, which improves the safety and reliability of the drone in complex environments and takes into account both flight efficiency and stability.

[0079] In some embodiments of the present application, when the flight control unit is used to determine whether to adjust the flight altitude according to the air pressure, it includes:

[0080] Set the minimum air pressure value. If the air pressure is greater than or equal to the minimum air pressure value, the flight altitude will not be adjusted.

[0081] If the air pressure is lower than the minimum value, it is determined that the flight altitude needs to be adjusted.

[0082] In some embodiments of the present application, the flight control unit is used to adjust the flight altitude according to the air pressure if it is determined to be an adjustment, and obtain a final value of the flight altitude, including:

[0083] Setting a first air pressure and a second air pressure, wherein the first air pressure is higher than the second air pressure;

[0084] If the air pressure is greater than the first air pressure, the flight altitude is adjusted by the first adjustment coefficient;

[0085] If the air pressure is less than or equal to the first air pressure and greater than or equal to the second air pressure, the flight altitude is adjusted by the second adjustment coefficient;

[0086] If the air pressure is less than the second air pressure, the flight altitude is adjusted by the third adjustment coefficient;

[0087] Among them, 1>first adjustment coefficient>second adjustment coefficient>third adjustment coefficient>0.

[0088] It is understandable that the present invention ensures accurate adjustment of the flight altitude of the UAV under different air pressure conditions by setting the minimum air pressure value and the first and second air pressure graded adjustment mechanisms. Through graded adjustment, the flight altitude is optimized using different adjustment coefficients to ensure the operational safety and flight efficiency of the UAV.

[0089] In some embodiments of the present application, when the demand processing unit is used to determine whether oxygenation is needed according to the fish pond image information, it includes:

[0090] The image information of several fish ponds taken by the visual sensor is stitched together to obtain a complete fish pond image; the noise of the complete fish pond image is removed; the floating head area is marked, and it is determined whether the fish pond needs to be oxygenated according to the area of ​​the floating head area.

[0091] The marking floating head area can be calculated as follows:

[0092] The fish pond image is converted from RGB color space to HSV color space, and the extremely dark shadow area in the fish pond image is marked according to formula (2):

[0093]

[0094] Based on the marked extremely dark shadow area, each pixel point is assigned a value as follows (3):

[0095]

[0096] s(i,j) represents the saturation value at the coordinate (i,j) of the fish pond image; v(i,j) represents the brightness value at the coordinate (i,j) of the pixel point in the fish pond image; h(i,j) represents the hue value at the coordinate (i,j) of the pixel point in the fish pond image;

[0097] Calculate the number of pixels r in the fish pond image that meet the requirements of the above formula (3) P , the number of pixels r P Compared with the number of pixels in the complete fish pond image, the total area of ​​the floating head is obtained through pixel mapping (Equation 4):

[0098]

[0099] A R A is the total area of ​​the floating head; P is the total area of ​​the pond in the photo; R P is the total number of pixels of the complete fish pond image.

[0100] It is understandable that the present invention effectively covers the entire area of ​​the fish pond by splicing multiple images into a complete fish pond image, avoiding local monitoring blind spots and ensuring a more comprehensive situation analysis. De-noising and grayscale processing can eliminate external interference, improve image quality, and improve the accuracy of subsequent analysis. By using an edge detection algorithm to mark the floating head area, the floating head phenomenon on the surface of the fish pond can be accurately identified, and whether oxygenation is needed can be judged according to the floating head area, thereby realizing the accurate judgment of the oxygenation demand. This process not only improves the efficiency of the oxygenation operation, but also avoids the situation of excessive or insufficient oxygenation, and optimizes the breeding environment and resource use. Specifically, after edge detection, the floating head area is usually manifested as an obvious edge contour. According to the detected edge, a connected region analysis can be performed: the contour extraction function (such as findContours) of a library such as OpenCV is used to detect the edge of the floating head area. By analyzing the extracted contour, the floating head area is selected. The floating head area generally has a large area and the edge is relatively continuous.

[0101] In some embodiments of the present application, judging whether the fish pond needs to be oxygenated according to the area of ​​the floating head region includes:

[0102] Set the minimum floating head area. If the floating head area is smaller than the minimum floating head area, it is determined that the fish pond will not be oxygenated.

[0103] If the floating head area is greater than or equal to the minimum floating head area, it is judged that the fish pond needs to be oxygenated.

[0104] In some embodiments of the present application, if it is determined that oxygenation is required, the amount of oxygenation is determined according to the fish pond image information and the ambient temperature, including:

[0105] Set the maximum temperature and calculate the oxygen increase amount based on the floating head area and ambient temperature:

[0106] Z = Z0*[mf / m+(t-t0) / t];

[0107] In the above formula, Z represents the amount of oxygen increase, Z0 represents the oxygen supply per unit time in the current fish pond, mf represents the floating head area, m represents the complete fish pond area, t represents the current ambient temperature, t0 represents the maximum temperature, and t>t0.

[0108] It is understandable that the present invention, by setting the minimum value of the floating head area, ensures that the oxygenation operation is triggered only when the floating head area reaches a certain standard, thereby avoiding unnecessary oxygenation and improving resource utilization efficiency. At the same time, the method of calculating the amount of oxygenation based on the floating head area and the ambient temperature takes into account the actual conditions of the fish pond and the environmental impact, so that the amount of oxygenation can be accurately adjusted according to actual needs. In particular, after setting the maximum temperature, the amount of oxygenation is increased only when the temperature exceeds the threshold, which ensures the rationality and scientificity of the amount of oxygenation, further optimizes the breeding environment, and helps to improve the growth quality and breeding efficiency of fish.

[0109] In some embodiments of the present application, when determining whether to adjust the oxygen addition amount according to the air pressure, it includes:

[0110] Set the minimum air pressure value. If the air pressure is greater than the minimum air pressure value, it is determined that there is no need to adjust the oxygen increase amount.

[0111] If the air pressure is less than or equal to the minimum air pressure value, it is determined that the oxygen addition amount needs to be adjusted.

[0112] In some embodiments of the present application, if it is determined that adjustment is required, the oxygen increase amount is adjusted according to the air pressure, and when the final value of the oxygen increase amount is obtained, it includes:

[0113] Setting a third air pressure and a fourth air pressure, wherein the third air pressure is greater than the fourth air pressure;

[0114] If the air pressure is greater than the third air pressure, it is determined that the oxygen increase amount is adjusted by the first adjustment coefficient;

[0115] If the air pressure is less than or equal to the third air pressure and greater than or equal to the fourth air pressure, it is determined that the oxygen increase amount is adjusted by the second adjustment coefficient;

[0116] If the air pressure is less than the fourth air pressure, it is determined that the oxygen increase amount is adjusted by the third adjustment coefficient;

[0117] Among them, 1>first adjustment coefficient>second adjustment coefficient>third adjustment coefficient>2.

[0118] It is understandable that by setting the minimum air pressure value and multiple air pressure intervals, the amount of oxygen added is dynamically adjusted to ensure the efficiency and rationality of oxygenation under different air pressure conditions. For situations where the air pressure is higher than the minimum value, there is no need to adjust the amount of oxygen added; different adjustment coefficients are used according to the air pressure interval to accurately control the amount of oxygen added, so that it is more in line with actual needs. The graded design of multiple adjustment coefficients can effectively cope with changes in air pressure and avoid insufficient oxygenation or waste of resources due to low air pressure. At the same time, it improves the flexibility and adaptability of oxygenation equipment, provides a more stable dissolved oxygen level for fish ponds, ensures that aquatic organisms can still grow healthily under extreme climatic conditions, and further improves the reliability and economic benefits of aquaculture.

[0119] On the other hand, see Figure 2 As shown, the present application also provides a multi-modal farming drone control method, which is applied to the above-mentioned multi-modal farming drone, and includes the following steps:

[0120] S100, setting a visual sensor, a temperature sensor, a wind speed sensor and an air pressure sensor on the machine body;

[0121] S200, collecting environmental information and fish pond image information, wherein the environmental information includes ambient temperature, wind speed and air pressure;

[0122] S300, determining the flight altitude of the UAV according to the weight information of the UAV and the wind speed, judging whether to adjust the flight altitude according to the air pressure, and if it is judged to be adjusted, adjusting the flight altitude according to the air pressure to obtain a final value of the flight altitude;

[0123] S400, judging whether oxygenation is needed according to the image information of the fish pond; if it is judged that oxygenation is needed, determining the amount of oxygenation according to the image information of the fish pond and the ambient temperature; judging whether to adjust the amount of oxygenation according to the air pressure; if it is judged that adjustment is needed, adjusting the amount of oxygenation according to the air pressure to obtain a final value of the amount of oxygenation; transmitting the final value of the amount of oxygenation to the oxygenation equipment, so that the oxygenation equipment oxygenates the fish pond according to the final value of the amount of oxygenation.

[0124] It is understandable that the present invention combines multiple sensor data to provide an accurate and efficient drone control method for fish pond farming management. By collecting environmental information such as ambient temperature, wind speed, air pressure, and fish pond image information, the drone can evaluate the flight altitude and oxygen demand in real time and optimize its operating efficiency. The flight altitude is dynamically determined based on the body weight and wind speed, and the flight stability is guaranteed by adjusting the air pressure, so that tasks can be accurately performed under various environmental conditions. At the same time, the fish pond image information is used to detect the floating head phenomenon, the oxygen increase amount is calculated in combination with the ambient temperature, and then the oxygen increase amount is adjusted according to the air pressure to ensure that the oxygen increase measures are more scientific and reasonable. The overall process not only improves the autonomy and intelligence level of the drone, but also significantly improves the dissolved oxygen management efficiency of fish pond farming, reduces manpower dependence and resource waste, and provides technical support for modern and intelligent aquaculture.

[0125] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0126] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0127] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0128] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0129] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A multi-modal farming drone, characterized in that: include: Body; A sensor assembly, including a visual sensor, a temperature sensor, a wind speed sensor and an air pressure sensor, wherein the sensor assembly is arranged on the machine body; A control module, used to collect environmental information and fish pond image information, control the flight altitude of the aircraft body according to the environmental information, and control the oxygenation equipment to increase oxygen according to the environmental information and the fish pond image information; wherein the control module includes a collection unit, a flight control unit and a demand processing unit; The acquisition unit is used to acquire the environmental information and fish pond image information, wherein the environmental information includes environmental temperature, wind speed and air pressure; The flight control unit determines the flight altitude of the UAV according to the weight information of the aircraft body and the wind speed, and determines whether to adjust the flight altitude according to the air pressure. If it is determined to be adjusted, the flight altitude is adjusted according to the air pressure to obtain a final value of the flight altitude; The demand processing unit determines whether oxygenation is needed based on the fish pond image information. If oxygenation is needed, the oxygenation amount is determined based on the fish pond image information and the ambient temperature. The demand processing unit determines whether to adjust the oxygenation amount based on the air pressure. If adjustment is needed, the oxygenation amount is adjusted based on the air pressure to obtain a final value of the oxygenation amount. The final value of the oxygenation amount is transmitted to the oxygenation equipment, so that the oxygenation equipment oxygenates the fish pond according to the final value of the oxygenation amount.

2. The multi-modal farming drone according to claim 1, characterized in that: When the flight control unit determines the flight altitude of the UAV according to the weight information of the UAV and the wind speed, it includes: The standard flight altitude of the drone without load and in a windless environment is preset, and the flight altitude H of the drone is calculated according to the following formula: H=H0*kz+H0*kv In the formula, H0 represents half of the standard flight altitude of the UAV, kz represents the influence coefficient of the fuselage weight, and kv represents the influence coefficient of the wind speed. The value range of kz and kv is (0,1]; Set a minimum altitude. If the flight altitude is lower than the minimum altitude, it is determined that the drone flight conditions are not met.

3. The multi-modal farming drone according to claim 1, characterized in that: When the flight control unit determines whether to adjust the flight altitude according to the air pressure, it includes: Setting a minimum air pressure value, and if the air pressure is greater than or equal to the minimum air pressure value, determining not to adjust the flight altitude; If the air pressure is less than the minimum air pressure value, it is determined that the flight altitude needs to be adjusted.

4. The multi-modal farming drone according to claim 3, characterized in that: The flight altitude is adjusted according to the air pressure to obtain a final value of the flight altitude, specifically including: Setting a first air pressure and a second air pressure, wherein the first air pressure is higher than the second air pressure; If the air pressure is greater than the first air pressure, adjusting the flight altitude by a first adjustment coefficient; If the air pressure is less than or equal to the first air pressure and greater than or equal to the second air pressure, the flight altitude is adjusted by a second adjustment coefficient; If the air pressure is less than the second air pressure, adjusting the flight altitude by a third adjustment coefficient; Among them, 1>first adjustment coefficient>second adjustment coefficient>third adjustment coefficient>0.

5. The multi-modal farming drone according to claim 1, characterized in that: The demand processing unit determines whether oxygenation is needed according to the fish pond image information, specifically including: Several fish pond images taken by the visual sensor are stitched together to obtain a complete fish pond image; Remove the noise of the complete fish pond image, mark the floating head area, and determine whether the fish pond needs to be oxygenated according to the area of ​​the floating head area: Setting a minimum floating head area value, if the floating head area is less than the minimum floating head area value, it is determined that the fish pond is not oxygenated; If the floating head area is greater than or equal to the minimum floating head area, it is determined that the fish pond needs to be oxygenated.

6. The multi-modal farming drone according to claim 5, characterized in that: The marking floating head area specifically includes: The fish pond image is converted from RGB color space to HSV color space, and the extremely dark shadow area in the fish pond image is marked according to the following formula: v(i,j)≤0.4 0.5≤h(i,j)≤0.9 Where v(i,j) represents the brightness value of the pixel point coordinate (i,j) of the fish pond image; h(i,j) represents the hue value of the pixel point coordinate (i,j) of the fish pond image; Based on the marked extremely dark shadow area, each pixel is assigned the following value: Where s(i,j) represents the saturation value at the coordinate (i,j) of the fish pond image; Calculate the number of pixels r in the fish pond image that meet the above assignment requirements P , the number of pixels r P Compared with the number of pixels in the complete fish pond image, the total area of ​​the floating head is obtained through pixel mapping: In the formula, A R A is the total area of ​​the floating head; P is the total area of ​​the pond in the photo; R P is the total number of pixels of the complete fish pond image.

7. The multi-modal farming drone according to claim 5, characterized in that: If it is determined that the fish pond needs to be oxygenated, the amount of oxygenation is determined according to the fish pond image information and the ambient temperature, specifically including: Set the maximum temperature and calculate the oxygen increase amount Z according to the floating head area and the ambient temperature: Where Z0 represents the oxygen supply per unit time in the current fish pond, mf represents the floating head area, m represents the complete fish pond area, t represents the current ambient temperature, t0 represents the maximum temperature, and t>t0.

8. The multi-modal farming drone according to claim 1, characterized in that: The step of determining whether to adjust the oxygen increase amount according to the air pressure specifically includes: Setting a minimum air pressure value, if the air pressure is greater than the minimum air pressure value, it is determined that the oxygen increase amount does not need to be adjusted; If the air pressure is less than or equal to the minimum air pressure value, it is determined that the oxygen increase amount needs to be adjusted.

9. The multi-modal farming drone according to claim 8, characterized in that: If it is determined that the oxygen increase amount needs to be adjusted, the oxygen increase amount is adjusted according to the air pressure to obtain a final value of the oxygen increase amount, which specifically includes: Setting a third air pressure and a fourth air pressure, wherein the third air pressure is greater than the fourth air pressure; If the air pressure is greater than the third air pressure, it is determined that the oxygen increase amount is adjusted by the first adjustment coefficient; If the air pressure is less than or equal to the third air pressure and greater than or equal to the fourth air pressure, it is determined that the oxygen increase amount is adjusted by a second adjustment coefficient; If the air pressure is less than the fourth air pressure, it is determined that the oxygen increase amount is adjusted by a third adjustment coefficient; Among them, 1>first adjustment coefficient>second adjustment coefficient>third adjustment coefficient>2.

10. A multi-modal farming drone control method, applied to the multi-modal farming drone as claimed in any one of claims 1 to 9, characterized in that: include: A visual sensor, a temperature sensor, a wind speed sensor and an air pressure sensor are arranged on the machine body; Collecting environmental information and fish pond image information, wherein the environmental information includes ambient temperature, wind speed and air pressure; Determine the flight altitude of the drone according to the weight information of the drone and the wind speed, and determine whether to adjust the flight altitude according to the air pressure. If it is determined to be adjusted, adjust the flight altitude according to the air pressure to obtain a final value of the flight altitude; Determine whether oxygenation is needed based on the fish pond image information; if it is determined that oxygenation is needed, determine the amount of oxygenation based on the fish pond image information and the ambient temperature; determine whether to adjust the amount of oxygenation based on the air pressure; if it is determined that adjustment is needed, adjust the amount of oxygenation based on the air pressure to obtain a final value of the amount of oxygenation; transmit the final value of the amount of oxygenation to the oxygenation equipment, so that the oxygenation equipment oxygenates the fish pond according to the final value of the amount of oxygenation.

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