A fish-light complementary unmanned aerial vehicle feeding strategy dynamic optimization method and system

Abstract

The present application relates to the technical field of unmanned aerial vehicle operation control, and particularly relates to a fish-light complementary unmanned aerial vehicle feeding strategy dynamic optimization method and system, aiming at the composite attribute of the fish-light complementary scene, the scheme couples the photovoltaic power compensation power margin, the disturbance power consumption and the total load of operation, constructs the field energy ratio as a unified quantitative index, realizes the collaborative judgment of multi-dimensional core parameters, can generate hierarchical control instructions based on the interval distribution of the energy ratio, dynamically adjusts the unmanned aerial vehicle flight and feeding operation behavior, fully utilizes the energy redundancy to improve the operation efficiency, can avoid operation risks when the energy is insufficient, adapts to the complex and changeable operation conditions of the fish-light complementary scene, and comprehensively improves the efficiency and operation stability of the feeding operation.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) operation control technology, and in particular to a dynamic optimization method and system for feeding strategies of UAVs that combine fishing and solar power. Background Technology

[0002] Solar-aquaculture integration is a composite agricultural model that combines above-water photovoltaic power generation with underwater aquaculture, representing a core development direction for the green and large-scale transformation of aquaculture. With the continuous expansion of aquaculture scale, precise and efficient feed delivery has become a key factor in improving aquaculture efficiency. Multi-rotor drones, with their advantages of maneuverability and strong site adaptability, have gradually become the mainstream equipment for feeding operations in solar-aquaculture integration scenarios. However, solar-aquaculture integration scenarios have unique characteristics such as photovoltaic array spatial obstruction, strong local airflow disturbances, dynamic fluctuations in irradiance conditions, and limited aquatic operating space. Existing drone feeding solutions mostly adopt fixed-path, fixed-parameter operation modes, which cannot adapt to the dynamically changing energy conditions and environmental interference within the scenario. This easily leads to problems such as low operating efficiency, insufficient battery life, and operational interruptions, making it difficult to meet the normalized and highly reliable feeding operation requirements of solar-aquaculture integration scenarios. Therefore, a targeted dynamic optimization solution for feeding strategies is urgently needed.

[0003] Existing drone delivery strategies mostly employ preset paths and fixed parameters, failing to consider the photovoltaic (PV) energy replenishment attributes of the fishery-solar hybrid scenario. They cannot perceive the real-time replenishment capacity of PV modules or dynamically adjust operational behavior based on available power margins. This results in an inability to fully utilize surplus PV energy to improve operational efficiency and a high risk of operational interruptions or even crashes due to insufficient energy supply. Secondly, existing solutions do not quantitatively assess the impact of environmental disturbances such as local turbulence and wind resistance generated by PV arrays on operational power consumption in fishery-solar hybrid scenarios. They rely solely on rough power consumption estimates based on the workload and remaining battery power, failing to account for additional power consumption caused by environmental disturbances. This leads to significant power consumption calculation errors and lacks reliable data support for reasonable adjustments to operational strategies. Furthermore, existing solutions fail to achieve synergistic coupling among the three core dimensions of energy replenishment, environmental disturbance, and workload. They lack unified quantitative evaluation indicators, making it impossible to dynamically match operational intensity with energy supply, balance operational efficiency and safety simultaneously, and adapt to the complex and variable operational conditions of fishery-solar hybrid scenarios. Summary of the Invention

[0004] The main objective of this invention is to provide a dynamic optimization method for the feeding strategy of a solar-fishery complementary drone, and further to provide a dynamic optimization system for the feeding strategy of a solar-fishery complementary drone capable of running and implementing the above method, effectively solving the problems mentioned in the background art.

[0005] The technical solution of the present invention is as follows:

[0006] Firstly, a dynamic optimization method for a fishery-solar hybrid drone feeding strategy is proposed, which includes the following steps:

[0007] S1. Real-time acquisition of total solar radiation irradiance, photovoltaic module backsheet temperature, and rotor motor real-time current, and obtain total operating load;

[0008] S2. Based on the collected total solar irradiance and photovoltaic module backsheet temperature, the photovoltaic supplementary power margin used to characterize the available power margin is calculated.

[0009] S3. Based on the total workload and the real-time current of the rotor motor, calculate the disturbance power consumption used to characterize the degree of environmental interference to the UAV operation.

[0010] S4. Based on the photovoltaic power margin, disturbance power consumption and total operating load, synthesize the field energy ratio to characterize the matching relationship between the operating intensity and energy supply under unit operating load consumption;

[0011] S5. Based on the numerical range of the field computing power ratio, generate corresponding hierarchical control commands to dynamically adjust the flight and feeding operation behavior of the UAV.

[0012] A further improvement of the present invention is that the specific content of S1 is: real-time collection of total solar irradiance using a radiometer. The temperature of the backsheet of the photovoltaic module is collected using a surface-mount platinum resistance temperature sensor. The real-time current of the rotor motor is obtained through the telemetry interface of the UAV's electronic speed controller. The total workload is acquired and updated through the task assignment interface and onboard weight sensors. .

[0013] A further improvement of the present invention is that step S2 includes the following specific steps:

[0014] S21. Based on the collected total solar irradiance and the temperature of the backsheet of the photovoltaic module Calculate the real-time output power of photovoltaic modules The expression for the real-time output power of the photovoltaic module is:

[0015] ;

[0016] in, The effective light-receiving area of ​​a photovoltaic module. For reference standard battery efficiency, For power temperature coefficient, This is the reference temperature under standard test conditions;

[0017] S22, Further obtain the photovoltaic power margin used to characterize the available power margin. The expression is: ;in, This represents the basic power consumption for the operation.

[0018] A further improvement of the present invention is that step S3 includes the following specific steps:

[0019] S31, Based on the total workload of the operation Calculate the theoretical current of the drone in a hovering state during point-to-point feeding. The expression for the theoretical current is:

[0020] ;

[0021] in, For hovering induced speed, It is the acceleration due to gravity. This refers to the rated voltage of the drone's onboard battery. The overall conversion efficiency of the rotor motor and ESC;

[0022] S32. Theoretical current based on the UAV hovering in a fixed-point feeding state. Real-time current of rotor motor and the rated voltage of the drone's onboard battery Furthermore, the disturbance power consumption used to characterize the degree of environmental interference with UAV operations was obtained. The expression is: .

[0023] A further improvement of this invention is that the expression for the field computing power ratio in S4 is:

[0024] ;

[0025] in, For calculating the energy ratio of the field, This refers to the scene adaptation coefficient.

[0026] A further improvement of the present invention is that the specific content of S5 is as follows: based on the field computing power ratio, a range judgment is performed. When the field computing power ratio is in a preset high range, the preset energy-saving constraint is released, and the UAV is controlled to perform feed feeding operations at the maximum safe operating altitude, and corresponding control commands for flight speed, operating altitude, and feeding progress are output. When the field computing power ratio is in a preset middle range, the preset normal operating parameters are maintained to perform feeding, and the energy-saving and time constraints are not adjusted. When the field computing power ratio is in a preset low range, the preset time constraint is released, and a command is output to instruct the UAV to land first at a designated parking position with photovoltaic energy replenishment conditions. Only when the remaining energy is insufficient to support landing, the UAV is selected to hover and wait until the photovoltaic energy replenishment power margin is not less than 0 and the photovoltaic module backsheet temperature drops back to the preset normal operating range before resuming operation. When the field computing power ratio is in a negative range, safety protection is immediately triggered, and the UAV is controlled to land at the nearest safe parking position, prohibiting hovering and waiting.

[0027] Secondly, a dynamic optimization system for the feeding strategy of a fishery-solar complementary UAV is proposed. The system includes: a parameter acquisition module, an energy and disturbance parameter modeling module, a feature fusion module, and a hierarchical control execution module.

[0028] The parameter acquisition module is used to collect the total solar radiation irradiance, photovoltaic module backsheet temperature and rotor motor real-time current, and obtain the total operating load.

[0029] The energy and disturbance parameter modeling module is used to calculate the photovoltaic power margin, which characterizes the available power margin, based on the collected total solar irradiance and photovoltaic module backsheet temperature; and to calculate the disturbance power consumption, which characterizes the degree of environmental interference to UAV operations, based on the total operating load and rotor motor real-time current.

[0030] The feature fusion module is used to synthesize the field energy ratio based on the photovoltaic power margin, disturbance power consumption and total operating load, which characterizes the matching relationship between the operating intensity and energy supply under unit operating load consumption.

[0031] The hierarchical control execution module is used to generate corresponding hierarchical control commands based on the numerical range of the field computing power ratio, so as to dynamically adjust the flight and feeding operation behavior of the UAV.

[0032] The technical effects of this invention are as follows:

[0033] A dynamic optimization method for the feeding strategy of a solar-fishery complementary UAV is constructed. This method, considering the complex attributes of the solar-fishery complementary scenario, establishes a photovoltaic power margin calculation model based on real-time irradiance and module temperature. This effectively quantifies the available power margin for real-time photovoltaic power replenishment, enabling real-time perception of energy supply capacity during operation and providing reliable energy-dimensional support for dynamic adjustment of the feeding strategy. Secondly, this scheme constructs a disturbance power consumption calculation model using the difference between the hovering theoretical current and the real-time motor current. This effectively eliminates the additional power consumption caused by environmental disturbances, quantifies the degree of interference from the field environment on UAV operations, and significantly reduces the deviation in operational power consumption calculation. Simultaneously, this scheme couples the photovoltaic power margin, disturbance power consumption, and total operational load to construct a unified quantitative index—the field computing power ratio. This enables collaborative judgment of multi-dimensional core parameters and can generate graded control commands based on the interval distribution of the computing power ratio, dynamically adjusting the UAV's flight and feeding operation behavior. This fully utilizes energy redundancy to improve operational efficiency while mitigating operational risks when energy is insufficient, adapting to the complex and variable operational conditions of the solar-fishery complementary scenario, and comprehensively improving the efficiency and operational stability of feeding operations. Attached Figure Description

[0034] 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:

[0035] Figure 1 This is a flowchart illustrating a dynamic optimization method for a fishery-solar hybrid drone feeding strategy according to Embodiment 1 of the present invention.

[0036] Figure 2 This is a schematic diagram of the structure of a dynamic optimization system for a fishery-solar hybrid drone feeding strategy according to Embodiment 2 of the present invention. Detailed Implementation

[0037] Example 1: This example constructs a dynamic optimization method for the feeding strategy of a solar-fishery complementary UAV. This method, considering the complex attributes of the solar-fishery complementary scenario, builds a photovoltaic power margin calculation model based on real-time irradiance and component temperature. This effectively quantifies the available power margin for real-time photovoltaic power replenishment, enabling real-time perception of energy supply capability during operation and providing reliable energy-dimensional support for dynamic adjustment of the feeding strategy. Secondly, this scheme constructs a disturbance power consumption calculation model using the difference between the hovering theoretical current and the real-time motor current. This effectively isolates the additional power consumption caused by environmental disturbances, quantifies the degree of interference from the field environment on UAV operation, and significantly reduces the deviation in operation power consumption calculation. Simultaneously, this scheme couples the photovoltaic power margin, disturbance power consumption, and total operation load to construct a unified quantitative index—the field computing power ratio. This enables collaborative judgment of multi-dimensional core parameters and can generate graded control commands based on the interval distribution of the computing power ratio, dynamically adjusting the UAV's flight and feeding operation behavior. This fully utilizes energy redundancy to improve operational efficiency while mitigating operational risks when energy is insufficient, adapting to the complex and variable operating conditions of the solar-fishery complementary scenario, and comprehensively improving the efficiency and operational stability of feeding operations.

[0038] A dynamic optimization method for a solar-fishery complementary drone-based feeding strategy, such as Figure 1 As shown, the specific steps include the following:

[0039] S1. Real-time acquisition of total solar radiation irradiance, photovoltaic module backsheet temperature, and rotor motor real-time current, and obtain total operating load;

[0040] S2. Based on the collected total solar irradiance and photovoltaic module backsheet temperature, the photovoltaic supplementary power margin used to characterize the available power margin is calculated.

[0041] S3. Based on the total workload and the real-time current of the rotor motor, calculate the disturbance power consumption used to characterize the degree of environmental interference to the UAV operation.

[0042] S4. Based on the photovoltaic power margin, disturbance power consumption and total operating load, synthesize the field energy ratio to characterize the matching relationship between the operating intensity and energy supply under unit operating load consumption;

[0043] S5. Based on the numerical range of the field computing power ratio, generate corresponding hierarchical control commands to dynamically adjust the flight and feeding operation behavior of the UAV.

[0044] In this embodiment, the specific content of S1 is: real-time collection of total solar irradiance using a radiometer. The temperature of the backsheet of the photovoltaic module is collected using a surface-mount platinum resistance temperature sensor. The real-time current of the rotor motor is obtained through the telemetry interface of the UAV's electronic speed controller. The total workload is acquired and updated through the task assignment interface and onboard weight sensors. .

[0045] In this embodiment, the total solar irradiance in the work area is first collected in real time using an airborne radiometer, measured in W / m². The radiometer is installed on the same plane as the photovoltaic module's light-receiving surface mounted on the UAV to ensure that the collected irradiance data matches the actual irradiance level received by the photovoltaic module. The temperature of the photovoltaic module's backsheet is then collected using at least two surface-mount platinum resistance temperature sensors attached to it, one in the center and one at the edge of the backsheet. The arithmetic mean of the collected temperature data is taken as the final photovoltaic module backsheet temperature. The dimension is K. The real-time current of the rotor motor is obtained through the telemetry interface of the UAV's electronic speed controller. The dimension is A. The ESC corresponding to each rotor motor synchronously outputs the real-time current data of the corresponding channel. The arithmetic mean of the current data from multiple channels is taken as the final real-time current of the rotor motor. The system acquires and updates the total workload through the task distribution interface and the onboard weight sensor. The total workload is measured in kg and includes the weight of the UAV itself, the weight of the onboard battery, and the weight of the remaining feed in the feed storage bin. The onboard weight sensor is installed at the connection point between the feed storage bin and the UAV body. It updates the weight of the remaining feed in real time after each feeding action and maintains the current workload value throughout the feeding cycle, thereby completing the real-time update of the total workload.

[0046] In this embodiment, step S2 includes the following specific steps:

[0047] S21. Based on the collected total solar irradiance and the temperature of the backsheet of the photovoltaic module Calculate the real-time output power of photovoltaic modules The expression for the real-time output power of the photovoltaic module is:

[0048] ;

[0049] in, The effective light-receiving area of ​​a photovoltaic module. For reference standard battery efficiency, For power temperature coefficient, This is the reference temperature under standard test conditions;

[0050] S22, Further obtain the photovoltaic power margin used to characterize the available power margin. The expression is: ;in, This represents the basic power consumption for the operation.

[0051] In this embodiment, based on the collected total solar irradiance and photovoltaic module backsheet temperature, a photovoltaic power margin for characterizing the available power reserve is calculated, with the dimension in W. In the scenario of fishery-solar complementary operation, the photovoltaic module carried by the UAV can realize real-time power replenishment during flight operations. The power replenishment is directly affected by the on-site irradiance conditions and module temperature. However, there is a fixed basic power consumption during UAV operation. Only when the real-time output power of photovoltaic is higher than the basic power consumption can there be a usable power reserve to support additional operational intensity. Based on this, the calculation logic of photovoltaic power margin is constructed. First, the real-time output power of the photovoltaic module is calculated based on the collected total solar irradiance and the backsheet temperature of the photovoltaic module. The effective light-receiving area of ​​the photovoltaic module is consistent with the actual light-receiving area of ​​the photovoltaic module carried by the drone, with the dimension being square meters. The reference standard cell efficiency is the cell conversion efficiency under the standard test conditions provided by the photovoltaic module manufacturer. The power temperature coefficient has the dimension of 1 / K and is matched with the cell type of the photovoltaic module. The preferred value for monocrystalline silicon cells is -0.0038 / K. The reference temperature under the standard test conditions has the dimension of K and is fixed at 298.15K. The calculation logic of this formula is as follows: the output power of a photovoltaic module is positively correlated with the incident irradiance. Simultaneously, it is affected by the module's operating temperature; when the temperature deviates from the reference temperature, the cell conversion efficiency will show corresponding decreases or increases. This formula allows for real-time calculation of the actual output power of the photovoltaic module under the current operating environment. After calculating the real-time output power of the photovoltaic module, the photovoltaic supplementary power margin is further calculated. In the photovoltaic supplementary power margin expression, the operating baseline power consumption is used. This value is obtained through a static test of a drone hovering in idle condition. During the test, the drone maintains a stable hovering state, the feeding actuator is turned off, and only the flight control, data transmission, and acquisition equipment are turned on. The average power consumption under stable operating conditions is recorded as... The value of is determined when the photovoltaic power margin is... When the value is positive, it means that the photovoltaic power supply can cover the basic power consumption of the operation, and there is an extra power margin that can be used to support the feeding operation. When the value is negative, it means that the photovoltaic power supply cannot cover the basic power consumption of the operation, and the energy storage of the onboard battery needs to be consumed to maintain the operation of the equipment.

[0052] In this embodiment, step S3 includes the following specific steps:

[0053] S31, Based on the total workload of the operation Calculate the theoretical current of the drone in a hovering state during point-to-point feeding. The expression for the theoretical current is:

[0054] ;

[0055] in, For hovering induced speed, It is the acceleration due to gravity. This refers to the rated voltage of the drone's onboard battery. The overall conversion efficiency of the rotor motor and ESC;

[0056] S32. Theoretical current based on the UAV hovering in a fixed-point feeding state. Real-time current of rotor motor and the rated voltage of the drone's onboard battery Furthermore, the disturbance power consumption used to characterize the degree of environmental interference with UAV operations was obtained. The expression is: .

[0057] In this embodiment, based on the total workload and the real-time current of the rotor motor, the disturbance power consumption, which characterizes the degree of environmental interference on the drone's operation, is calculated. When the drone operates in a solar-aquaculture hybrid scenario, it is affected by environmental factors such as wind disturbance and airflow disturbance formed by the photovoltaic array. These disturbances are directly reflected in the real-time current changes of the rotor motor. The theoretical current of the drone in the hovering state is only related to the total workload. By the difference between the real-time current and the theoretical current, the additional power consumption caused by environmental interference can be extracted. This is used to construct the calculation logic of the disturbance power consumption. First, based on The theoretical current of the UAV in a fixed-point feeding hovering state is calculated based on the total workload. In the expression, the preferred value for the hovering induced velocity is 1.2 m / s², the gravitational acceleration is 9.8 m / s², the rated voltage of the UAV's onboard battery is consistent with the nominal rated voltage of the power battery carried by the UAV, and the comprehensive conversion efficiency of the rotor motor and ESC is obtained through UAV hovering test on a test bench. During the test, the UAV is hovering stably with a fixed weight load, and the ratio of motor input power to output shaft power in a stable state is recorded. The average value of multiple tests is taken as the result. The calculation logic of this formula is as follows: In the hovering state of the drone, the required lift output needs to balance the gravity of the total load. Combining the conversion efficiency of the motor and ESC, and the rated voltage of the battery, the theoretical current required for hovering in the absence of environmental interference can be calculated. After calculating the theoretical current, based on the theoretical current of the drone in the fixed-point feeding hovering state, the real-time current of the rotor motor, and the rated voltage of the drone's onboard battery, the disturbance power consumption, used to characterize the degree of environmental interference on drone operations, is further obtained. The value directly reflects the additional power consumption caused by environmental interference. The larger the value, the stronger the interference factors such as airflow turbulence and wind resistance in the current working environment, and the higher the additional power consumption required for the drone to maintain its working state.

[0058] In this embodiment, the expression for the field computing power ratio in S4 is:

[0059] ;

[0060] in, For calculating the energy ratio of the field, This refers to the scene adaptation coefficient.

[0061] In this embodiment, based on the photovoltaic power margin, disturbance power consumption, and total workload, a field computing power ratio is synthesized to characterize the matching relationship between the workload and energy supply under a unit workload consumption. In a solar-fishery complementary scenario, drone-based feeding operations need to simultaneously consider energy supply capability, power loss due to environmental interference, and the basic power consumption corresponding to the workload. The matching relationship among these three directly determines the workload that the drone can perform. By coupling the photovoltaic power margin, disturbance power consumption, and total workload for calculation, a comprehensive characterization of the energy supply and workload matching relationship under the current field spatiotemporal conditions can be obtained. The quantitative index of the matching degree of energy consumption is expressed in the formula of the field energy ratio. The value of the scene adaptation coefficient is matched with the photovoltaic array layout density and aquaculture water area of ​​the fishery-solar complementary field. For open water and sparse photovoltaic array layout, the preferred value is 0.8 / kg-1 / kg. For dense photovoltaic array layout and narrow water space, the preferred value is 1.1 / kg-1.5 / kg. This formula achieves the coupling of energy supply capability, environmental interference intensity, and operational load level by using the available power margin as the numerator and the product of disturbance power consumption and total operating load as the denominator. It is then corrected by the scene adaptation coefficient to obtain the final result. The higher the value, the higher the energy supply redundancy corresponding to the unit operating load under the current field conditions, and the greater the operating intensity that can be supported. Conversely, the lower the value, the lower the energy supply redundancy, and the more constraints need to be placed on the operating behavior.

[0062] In this embodiment, the specific content of S5 is as follows: Based on the field computing power ratio, a range judgment is performed. When the field computing power ratio is in a preset high range, the preset energy-saving constraint is released, and the UAV is controlled to perform feed feeding operations at the maximum safe operating altitude, and corresponding control commands for flight speed, operating altitude, and feeding progress are output. When the field computing power ratio is in a preset middle range, the preset normal operating parameters are maintained to perform feeding, and the energy-saving and time constraints are not adjusted. When the field computing power ratio is in a preset low range, the preset time constraint is released, and a command is output to instruct the UAV to land first at a designated parking position with photovoltaic energy replenishment conditions. Only when the remaining energy is insufficient to support landing, the UAV is selected to hover and wait until the photovoltaic energy replenishment power margin is not less than 0 and the photovoltaic module backsheet temperature drops back to the preset normal operating range before resuming operation. When the field computing power ratio is in a negative range, safety protection is immediately triggered, and the UAV is controlled to land at the nearest safe parking position, prohibiting hovering and waiting.

[0063] In this embodiment, based on the numerical range of the field computing power ratio, corresponding hierarchical control commands are generated to dynamically adjust the flight and feeding operation behavior of the UAV. Three numerical ranges are preset: a preset high value range, a preset middle value range, and a preset low value range. The lower threshold of the preset high value range is preferably 1.2, and the upper threshold of the preset low value range is preferably 0.6. After calculating the field computing power ratio in each sampling period, the calculation result is compared with the preset range, and the corresponding control strategy is executed: when the field computing power ratio is in the preset high value range (reaching 1.2), the preset control is released. The system controls the drone to perform feed delivery at its maximum safe operating altitude, and outputs corresponding control commands for flight speed, operating altitude, and feeding progress. The preset energy-saving constraints include upper limits for flight speed, operating altitude, and single-feed quantity. The maximum safe operating altitude is 1.2 times the maximum installation height of the photovoltaic modules in the aquaculture-solar hybrid area, ensuring that the feed delivered by the drone evenly covers the aquaculture water area while avoiding collisions with the photovoltaic modules. In the control commands, the flight speed increases synchronously with the increase in the energy density of the area, while the operating altitude remains at the maximum safe level. The operating altitude and feeding progress are executed according to the preset fastest operating rhythm. When the field computing power ratio is in the preset middle range (greater than 0.6 and less than 1.2), the preset normal operating parameters are maintained for feeding, without adjusting energy saving and time constraints, and the operation is completed according to the preset operating path and feeding plan. When the field computing power ratio is in the preset low range (greater than 0 and less than 0.6), the preset time constraint is released, and the command is output to instruct the UAV to land first at a designated parking position with photovoltaic power replenishment conditions. Only when the remaining energy is insufficient to support landing will it choose to hover and wait until the photovoltaic power replenishment margin is not less than 1.2. 0, and resume operation after the temperature of the photovoltaic module backsheet drops back to the preset normal operating range; wherein the preset time constraint is the pre-set operation completion time limit and the constraint rules of the minimum feeding amount for a single operation, the designated parking position is a parking platform with photovoltaic energy replenishment conditions pre-deployed in the fishery-solar complementary field, and the preset normal operating range temperature range is 263.15K-323.15K; when the field energy ratio is in the negative range (less than 0), the safety protection is immediately triggered, and the drone is controlled to land to the nearest safe parking position, and hovering is prohibited to avoid the risk of crash caused by excessive discharge of the airborne battery.

[0064] Example 2: This example proposes a dynamic optimization system for a fishery-solar hybrid UAV feeding strategy, such as... Figure 2 As shown, it includes: a parameter acquisition module, an energy and disturbance parameter modeling module, a feature fusion module, and a hierarchical control execution module;

[0065] The parameter acquisition module is used to collect the total solar radiation irradiance, photovoltaic module backsheet temperature and rotor motor real-time current, and obtain the total operating load.

[0066] The energy and disturbance parameter modeling module is used to calculate the photovoltaic power margin, which characterizes the available power margin, based on the collected total solar irradiance and photovoltaic module backsheet temperature; and to calculate the disturbance power consumption, which characterizes the degree of environmental interference to UAV operations, based on the total operating load and rotor motor real-time current.

[0067] The feature fusion module is used to synthesize the field energy ratio based on the photovoltaic power margin, disturbance power consumption and total operating load, which characterizes the matching relationship between the operating intensity and energy supply under unit operating load consumption.

[0068] The hierarchical control execution module is used to generate corresponding hierarchical control commands based on the numerical range of the field computing power ratio, so as to dynamically adjust the flight and feeding operation behavior of the UAV.

[0069] The parameters and steps of each unit module in the dynamic optimization system for feeding strategy of a fishery-solar hybrid UAV described above can be referred to the parameters and steps in the embodiment of the dynamic optimization method for feeding strategy of a fishery-solar hybrid UAV in Embodiment 1 above.

[0070] Example 3: This example provides an electronic device, including a processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes the above-described dynamic optimization method for a fishery-solar hybrid UAV feeding strategy by calling the computer program stored in the memory.

[0071] The electronic device can vary considerably depending on its configuration or performance. It may include one or more Central Processing Units (CPUs) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the dynamic optimization method for fish-solar hybrid UAV feeding strategy provided in the above-described embodiment. The electronic device may also include other components for implementing its functions; for example, it may have wired or wireless network interfaces and input / output interfaces for data input and output. Details will not be elaborated upon in this embodiment.

[0072] Those skilled in the art will recognize that this invention can be implemented as a system, method, or computer program product. Therefore, this invention can be implemented in the following forms: it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, this invention can also be implemented as a computer program product contained in one or more computer-readable media, which includes computer-readable program code.

[0073] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0074] This invention is described with reference to flowchart illustrations and block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and block diagrams, as well as combinations of blocks in the flowchart illustrations and block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0075] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and boxes Figure 1 The steps of the function specified in one or more boxes.

[0076] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A dynamic optimization method for a fishery-solar hybrid drone feeding strategy, characterized in that: The specific steps include the following: S1. Real-time acquisition of total solar radiation irradiance, photovoltaic module backsheet temperature, and rotor motor real-time current, and obtain total operating load; S2. Based on the collected total solar irradiance and photovoltaic module backsheet temperature, the photovoltaic supplementary power margin used to characterize the available power margin is calculated. S3. Based on the total workload and the real-time current of the rotor motor, calculate the disturbance power consumption used to characterize the degree of environmental interference to the UAV operation. S4. Based on the photovoltaic power margin, disturbance power consumption and total operating load, synthesize the field energy ratio to characterize the matching relationship between the operating intensity and energy supply under unit operating load consumption; S5. Based on the numerical range of the field computing power ratio, generate corresponding hierarchical control commands to dynamically adjust the flight and feeding operation behavior of the UAV. S2 includes the following specific steps: S21. Based on the collected total solar irradiance and the temperature of the backsheet of the photovoltaic module Calculate the real-time output power of photovoltaic modules The expression for the real-time output power of the photovoltaic module is: ； in, The effective light-receiving area of ​​a photovoltaic module. For reference standard battery efficiency, For power temperature coefficient, This is the reference temperature under standard test conditions; S22, Further obtain the photovoltaic power margin used to characterize the available power margin. The expression is: ;in, This is the base power consumption for the operation; S3 includes the following specific steps: S31, Based on the total workload of the operation Calculate the theoretical current of the drone in a hovering state during point-to-point feeding. The expression for the theoretical current is: ； in, For hovering induced speed, It is the acceleration due to gravity. This refers to the rated voltage of the drone's onboard battery. The overall conversion efficiency of the rotor motor and ESC; S32. Theoretical current based on the UAV hovering in a fixed-point feeding state. Real-time current of rotor motor and the rated voltage of the drone's onboard battery Furthermore, the disturbance power consumption used to characterize the degree of environmental interference with UAV operations was obtained. The expression is: ; The expression for the S4 field computing power ratio is: ； in, For calculating the energy ratio of the field, This refers to the scene adaptation coefficient.

2. The dynamic optimization method for a fishery-solar hybrid UAV feeding strategy according to claim 1, characterized in that: The specific content of S1 is: real-time collection of total solar irradiance using a radiometer. The temperature of the backsheet of the photovoltaic module is collected using a surface-mount platinum resistance temperature sensor. The real-time current of the rotor motor is obtained through the telemetry interface of the UAV's electronic speed controller. The total workload is acquired and updated through the task assignment interface and onboard weight sensors. .

3. The method for dynamic optimization of a fish-solar hybrid UAV feeding strategy according to claim 2, characterized in that, The specific content of S5 is as follows: Based on the field computing power ratio, interval judgment is performed. When the field computing power ratio is in the preset high value interval, the preset energy-saving constraint is released, and the UAV is controlled to perform feed feeding operation at the maximum safe operating altitude, and corresponding control commands for flight speed, operating altitude, and feeding progress are output. When the field computing power ratio is in the preset middle interval, the preset normal operating parameters are maintained to perform feeding, and the energy-saving and time constraints are not adjusted. When the field computing power ratio is in the preset low value interval, the preset time constraint is released, and the command is output to instruct the UAV to land first to a designated parking position with photovoltaic energy replenishment conditions. Only when the remaining energy is insufficient to support landing, the UAV is selected to hover and wait until the photovoltaic energy replenishment power margin is not less than 0 and the photovoltaic module backsheet temperature drops back to the preset normal operating range before resuming operation. When the field computing power ratio is in the negative value interval, the safety protection is immediately triggered, and the UAV is controlled to land to the nearest safe parking position, and hovering and waiting are prohibited.

4. A dynamic optimization system for a fishery-solar hybrid drone feeding strategy, implemented based on the dynamic optimization method for a fishery-solar hybrid drone feeding strategy as described in any one of claims 1-3, characterized in that, The system includes: a parameter acquisition module, an energy and disturbance parameter modeling module, a feature fusion module, and a hierarchical control execution module; The parameter acquisition module is used to collect the total solar radiation irradiance, photovoltaic module backsheet temperature and rotor motor real-time current, and obtain the total operating load. The energy and disturbance parameter modeling module is used to calculate the photovoltaic power margin, which characterizes the available power margin, based on the collected total solar irradiance and photovoltaic module backsheet temperature; and to calculate the disturbance power consumption, which characterizes the degree of environmental interference to UAV operations, based on the total operating load and rotor motor real-time current. The feature fusion module is used to synthesize the field energy ratio based on the photovoltaic power margin, disturbance power consumption and total operating load, which characterizes the matching relationship between the operating intensity and energy supply under unit operating load consumption. The hierarchical control execution module is used to generate corresponding hierarchical control commands based on the numerical range of the field computing power ratio, so as to dynamically adjust the flight and feeding operation behavior of the UAV.

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