Mine unmanned aerial vehicle emergency charging method based on target optimization

Abstract

The invention provides a mine unmanned aerial vehicle emergency charging method based on target optimization, and relates to the technical field of unmanned aerial vehicle charging, and the method mainly comprises the steps: carrying out the modeling of a battery of an unmanned aerial vehicle, and obtaining an equivalent circuit model of the battery; according to the equivalent circuit model and the charging power of the battery, establishing a first objective function taking the shortest charging time as an optimization objective; establishing a second objective function taking the longest flight time as an optimization objective according to the electric quantity of the battery after charging is completed; setting constraint conditions for optimization of the first objective function and the second objective function; and solving the first objective function and the second objective function. According to the method, under the condition that the total charging power is limited, the charging power distributed to the battery of the unmanned aerial vehicle is adjusted, it is guaranteed that the electric quantity of the unmanned aerial vehicle meets the take-off and endurance requirements in the shortest time, it is guaranteed that the unmanned aerial vehicle can be effectively recycled under the emergency conditions such as faults or mine accidents, air crash is avoided, and meanwhile the safety of the unmanned aerial vehicle is guaranteed. The unmanned aerial vehicle flies away from the mine in time.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle charging, and in particular to an emergency charging method for a mine unmanned aerial vehicle based on target optimization. Background Art

[0002] The mine unmanned inspection system can simulate the working mode of people, integrate data collection, video inspection, data analysis, fault alarm and other functions, and is used for routine inspection of various large equipment in the mine, realizing unmanned operation, reducing staff and increasing efficiency, reducing maintenance, and can find faults faster and complete maintenance work faster. At the same time, for some work sites where few people arrive or the working environment is harsh, the mine unmanned inspection system can be on duty for a long time, and coal mine workers can find problems through remote monitoring, effectively ensuring the safety of coal mine workers.

[0003] In recent years, the rapid development of drone technology has brought new opportunities to this field. The drone inspection system has the advantages of flexibility, convenience, and no restrictions on sites and environments. It can not only perform tasks in complex and dangerous environments, but also has a wide inspection coverage area. It can also transmit data in real time to improve the efficiency and accuracy of inspections.

[0004] However, there are also some problems and limitations in the development of drone inspection systems. The mine explosion-proof standards strictly limit the electrical equipment and power supply power entering the mine, which means that drones must be equipped with multiple batteries. How to charge multiple batteries of drones at the same time when the total power is limited so that the drone inspection system can maintain optimal performance is an urgent problem to be solved.

[0005] Among the existing solutions, there is a mathematical model for electric vehicle charging and discharging scheduling that takes the minimum charging and discharging cost of electric vehicles and the maximum charging pile revenue as the optimization goals, and performs case analysis based on the improved White Shark optimization algorithm; there is also a solution that proposes a paid charging service model, and based on this model, studies the problem of minimizing the charging cost for low-duty-cycle sensor networks. None of the above solutions has a complete charging strategy for drones in mines. Due to the explosion-proof requirements in mines, the electrical equipment and power supply power entering the mine are strictly restricted, resulting in drones having to carry multiple batteries. The current technical solutions cannot meet the needs of drones with split batteries to charge in mines.

[0006] The present invention is proposed based on this. Summary of the invention

[0007] The purpose of the present invention is to provide an emergency charging method for a mine UAV based on target optimization, so as to ensure that the UAV power meets the take-off and endurance requirements in the shortest time under emergency conditions, and enable the UAV to fly away from the mine in time, so as to meet the charging needs of UAVs using split batteries in mines.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] The method for emergency charging of a mine UAV based on target optimization includes the following steps: S1: modeling the battery of the UAV to obtain an equivalent circuit model of the battery; S2: establishing a first objective function with the shortest charging time as the optimization target according to the equivalent circuit model and the charging power of the battery; S3: establishing a second objective function with the longest flight time as the optimization target according to the battery power after charging; S4: setting constraints for the optimization of the first objective function and the second objective function, the constraints including maximum power constraints, minimum power constraints and maximum power constraints; S5: solving the first objective function and the second objective function.

[0010] The present invention provides a preferred solution, wherein the equivalent circuit model of the battery adopts a first-order RC equivalent circuit model, including the voltage U at both ends of the battery t , the equivalent voltage source voltage U inside the battery ocv , ohmic internal resistance R0, polarization internal resistance R1 and polarization capacitance C1 of the battery; R1 and C1 are connected in parallel and in series with R0, U t , U ocv , R0 forms a series circuit with the parallel connection of R1 and C1. The current flowing through the resistor R1 is i1, and the current flowing through the equivalent voltage source is i.

[0011] The present invention provides a preferred solution, wherein the UAV is equipped with four propeller motors as power sources, each propeller motor is configured with a battery for independent power supply, one of the five batteries is an onboard computer battery, and the remaining four are propeller motor batteries; the four propeller motor batteries are distributed on the four wings of the UAV, respectively supplying power to the four propeller motors, and the onboard computer battery is distributed in the center of the UAV, supplying power to the onboard computer and onboard sensors; the method is based on a power management system to allocate and charge all batteries on the UAV.

[0012] The present invention provides a preferred solution, in step S3, the constraint condition further includes a non-negative power constraint.

[0013] The present invention provides a preferred solution, in step S4, the optimization problem of the first objective function and the second objective function is obtained by solving the Pareto optimization.

[0014] Compared with the prior art, the above technical solution has the following advantages:

[0015] The present invention is based on the target optimization of the mine UAV emergency charging method. When the total charging power is limited, by adjusting the charging power allocated to the UAV battery, it is achieved to ensure that the UAV power meets the take-off and endurance requirements in the shortest time, and ensure that the UAV can be effectively recovered in emergency situations such as failures or accidents in the mine, so as to avoid crashes in the mine and enable the UAV to fly away from the mine in time. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0017] Figure 1 A schematic diagram of the distribution of a UAV power supply system for a mine UAV emergency charging method based on target optimization provided by a specific embodiment of the present invention;

[0018] Figure 2 A circuit diagram of an equivalent model of a first-order RC circuit in a target-optimized mine drone emergency charging method provided by a specific embodiment of the present invention;

[0019] Figure 3 The present invention is a flowchart of a method for emergency charging of a mine drone based on target optimization provided in a specific embodiment of the present invention.

[0020] Figure numerals: drone 1, onboard computer battery 2, propeller motor battery 3, power management system 4. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] The application scenario of the emergency charging method of the present invention is that the inspection drone needs to return temporarily in an emergency, such as a failure of the drone hardware system, an accident in a mine, a disconnection between the dispatch center and the drone, and a series of other events that make the drone unable to continue normal inspections. At this time, in order to ensure the effective recovery of the drone and avoid crashes in the mine, the drone starts the emergency charging strategy, that is, executes the emergency charging method of the present invention.

[0023] Please refer to Figure 1, taking the four-rotor drone used for underground inspection in mines as an example, the battery distribution scheme of the drone 1 is first given. The drone is equipped with multiple propeller motors as power sources, and each propeller motor needs to be powered by a battery separately. One of the five batteries is an onboard computer battery 2, and the other four are propeller motor batteries 3; the four propeller motor batteries 3 are distributed on the four wings of the drone, respectively powering the four propeller motors, and the onboard computer battery 2 is distributed in the center of the drone to power the onboard computer and onboard sensors. This embodiment provides an emergency charging method for mine drones based on target optimization, and emergency charging deployment of drones can be performed based on this power management system 4.

[0024] Please refer to Figure 2 In this embodiment, the battery of the drone is a lithium battery. There are many forms of lithium-ion battery models. As a preferred implementation, this embodiment uses the first-order RC equivalent circuit model in the equivalent circuit model to model the battery. The first-order RC equivalent circuit model has the advantages of being able to effectively characterize the polarization effect of the lithium battery, reflect the dynamic response of the battery, and can approximately represent the external characteristics of the battery. ocv is the voltage of the equivalent voltage source inside the battery, R0 is the ohmic internal resistance, R1 and C1 are the polarization internal resistance and polarization capacitance of the battery respectively, i1 is the current flowing through the resistor R1, i is the current flowing through the equivalent voltage source, U t is the voltage across the battery. R1 and C1 are connected in parallel and then in series with R0. t , U ocv , R0 and the parallel connected R1 and C1 form a series circuit.

[0025] Please refer to Figure 3 This embodiment provides a mine drone emergency charging method based on target optimization, which is mainly implemented through the following steps:

[0026] S1: Model the battery of the drone to obtain an equivalent circuit model of the battery. Furthermore, the first-order RC equivalent circuit model is preferably established by the following process:

[0027] S11: Kirchhoff's law of voltage gives:

[0028] U t =U OCV +iR0+i1R1 (1-1)

[0029] S12: The current across C1 is expressed as:

[0030]

[0031] S13: When the power management system charges the battery, solving equation (1-2) yields:

[0032]

[0033] Wherein, τ is the time constant of the first-order RC equivalent circuit model.

[0034] S2: Establishing a first objective function with the shortest charging time as the optimization target according to the equivalent circuit model and the charging power of the battery. Further, the first objective function is preferably established by the following process:

[0035] S21: Assume that the battery is charged at a constant voltage and only the charging current is changed; combine equations (1-1) to (1-3) to calculate the charging current i(t);

[0036] S22: The power allocated to the battery is obtained by formula (1-4):

[0037] P(t)=U t i(t) (1-4)

[0038] S23: During the charging process, the battery power is obtained by integrating the charging current, which is expressed by formula (1-5):

[0039]

[0040] Wherein, E(t) is the battery power at time t, E(0) is the initial power of the drone when it enters the cabin, C0 is the rated capacity of the battery, I is the charging current, and η is the coulomb efficiency coefficient, which is taken as 1 in the subsequent calculations in this embodiment;

[0041] S24: Combine the above formulas (1-1) to (1-5) to obtain the first objective function of the charging power and charging time of each battery, which is expressed by formula (1-5). In the optimization, it is hoped that the objective function is as small as possible:

[0042] f1(P1, P2, P3, P4, P5) = max(t1(P1), t2(P2), t3(P3), t4(P4), t5(P5)) (1-6)

[0043] Among them, t1, t2, t3, t4, and t5 are the charging times of the five batteries respectively, and P1, P2, P3, P4, and P5 are the charging powers allocated to an onboard computer battery and four paddle motor batteries respectively; here, the maximum value of the battery charging time is taken to obtain the total charging time, and then the total charging time is optimized with the shortest total charging time as the optimization goal.

[0044] S3: Establishing a second objective function with the longest flight time as the optimization target according to the battery power after charging is completed. Preferably, the following establishment process is adopted:

[0045] S31: Assume that the power of each battery after charging is completed is E1, E2, E3, E4, E5 respectively. The working time of each battery can be calculated by formula (1-7):

[0046]

[0047] Where i = 2, 3, 4, 5, t w1 , t w2 , t w3 , t w4 , t w5 The working time of five batteries, P c1 is the power consumption of the onboard computer, P mi is the power of any propeller motor, P c1 and P mi The actual data can be obtained by checking the data sheet.

[0048] S32: The flight time of the drone is expressed as formula (1-8), and the objective function is expected to be as large as possible during optimization:

[0049] f2(E1, E2, E3, E4, E5)=min(t w1 (E1), t w2 (E2), t w3 (E3), t w4 (E4), t w5 (E5)) (1-8).

[0050] S4: setting constraints for the optimization of the first objective function and the second objective function, wherein the constraints include a maximum power constraint, a minimum power constraint, and a maximum power constraint.

[0051] ① Maximum power constraint: In view of the energy limit requirements of the battery pack in the mine, the total charging power provided by the power management system for the five batteries must be limited to the maximum safe power P m Therefore, the maximum power constraint condition (1-9) is:

[0052] P1+P2+P3+P4+P5≤P m (1-9).

[0053] ② Non-negative power constraint: In actual situations, the charging power cannot be negative, so there is a non-negative power constraint formula (1-10):

[0054] P1≥0, P2≥0, P3≥0, P4≥0, P5≥0 (1-10)

[0055] Among them, P1, P2, P3, P4, and P5 are the charging powers allocated to an onboard computer battery and four paddle motor batteries respectively.

[0056] The above two constraints are charging power constraints. The battery power constraints will be specifically introduced below.

[0057] ③ The specific minimum power constraint conditions are as follows: the power of each battery after charging is required to be greater than the minimum working power, ensuring that each battery can output the minimum voltage to maintain the operation of the drone. Therefore, there are minimum power constraint conditions (1-11) to (1-15):

[0058] E1≥E cmin (1-11)

[0059] E2≥E m1min (1-12)

[0060] E3≥E m1min (1-13)

[0061] E4≥E m2min (1-14)

[0062] E5≥E m2min (1-15)

[0063] Among them, E1, E2, E3, E4, and E5 are the power of each battery after charging. E1 is the power of the onboard computer battery after charging, E2 and E3 are the power of the two propeller motor batteries on the nose side after charging, and E4 and E5 are the power of the two propeller motor batteries on the tail side after charging. cmin is the minimum working capacity of the onboard computer battery, E m1min is the minimum working power of the two propeller motor batteries located on the nose side, E m2min It is the minimum operating power of the two propeller motor batteries located on the tail side of the aircraft.

[0064] ④ The maximum power constraint condition is as follows: the actual final power of the battery cannot exceed its rated capacity, so there are maximum power constraint conditions (1-16) to (1-20):

[0065] E1≤E cmax (1-16)

[0066] E2≤E pmax (1-17)

[0067] E3≤E pmax (1-18)

[0068] E4≤E pmax (1-19)

[0069] E5≤E pmax (1-20)

[0070] Among them, E cmax is the rated capacity of the onboard computer battery, E pmax is the rated capacity of the propeller motor battery.

[0071] S5: Solve the first objective function and the second objective function. The optimization problem of the first objective function and the second objective function is obtained by solving the Pareto optimization. The solution process is summarized as follows:

[0072] Multi-objective optimization problems are often composed of multiple objectives that conflict and influence each other. These objectives cannot reach the optimal state at the same time, so usually we try to make these objectives reach the optimal state within a certain area. In the optimization problem of the first objective function and the second objective function of this embodiment, if for a solution A within the constraints, there is no other solution whose charging time and flight time are better than solution A, then solution A is called a Pareto optimal solution to this problem. There is more than one such solution in the solution space. All Pareto optimal solutions constitute the Pareto optimal solution set. These solutions constitute the Pareto optimal frontier or Pareto front surface of the problem after being mapped by the objective function. In this embodiment, a genetic algorithm (non-dominated sorting genetic algorithm second generation NSGA-II) is used to solve the Pareto optimal frontier of the multi-objective optimization problem of the emergency charging strategy. The process is as follows:

[0073] S51: The objective function is set to the drone charging time f1 and the flight time f2, and the decision quantity is set to the output power P1, P2, P3, P4, and P5 of the chargers of the five batteries, and the constraints and initial states are set according to the aforementioned modeling and actual conditions.

[0074] S52: Initialize the population. Set appropriate basic parameters such as population size N, iteration number t, range of decision variables, and gene manipulation parameters. As the most basic parameters of the algorithm, population size N and iteration number t ensure when the algorithm jumps out of the iteration loop and the size of the population. The range of values ​​of the randomly generated decision variables provided can generate decision variables randomly and uniformly within the specified range. Gene manipulation parameters include crossover probability and mutation probability. These two parameters will perform genetic manipulation on the genes of each individual during the iteration process to increase the diversity of the population, thereby avoiding falling into the local optimal solution.

[0075] S53: Calculate the crowding degree of the initialized population and perform non-dominated quick sorting on it. In the first generation population, the population is first non-dominated sorted according to the objective function, and the levels are divided. Then the crowding degree of each level is calculated and sorted.

[0076] S54: Generate offspring. Generate decision variables to offspring by crossover and mutation. After completing the crossover and mutation of genes, a population of N offspring is generated, which is then merged with the parent generation to generate a new population of 2N, and non-dominated sorting and crowding calculation operations are performed in this population.

[0077] S55: Elite selection strategy. According to the non-dominated sorting results of the new population, individuals with a dominance level of 1 are selected into the next generation first. If the individuals in this dominance level exceed the population size N, they are sorted from large to small according to the crowding degree, and those with large crowding degree are given priority to enter the next generation; if the individuals in this dominance level are less than the population size N, all individuals with a dominance level of 1 are selected into the next generation, and then individuals with a dominance level of 2 are queried, and so on until the number of offspring populations reaches N.

[0078] S56: Loop and output the results. After the first generation of the new population is completed, the steps S54 and S55 are looped until the preset number of iterations is reached, and the results including the optimized decision variables, objective function, dominance level and crowding degree are output for selection. After the last iteration and sorting, the solutions with the highest dominance level are all Pareto optimal solutions to the problem, and they together form the Pareto optimal solution set of the problem.

[0079] S57: According to the actual demand for the objective function, a Pareto optimal solution that meets the demand is rotated from the Pareto optimal solution set, and the decision variables corresponding to the optimal solution are calculated according to the aforementioned modeling, and this set of decision variables is used as the emergency charging strategy in this case.

[0080] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the above-mentioned embodiments of the methods. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0081] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0082] The above-described embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the attached claims.

Claims

1. The method for emergency charging of mine drones based on target optimization is characterized by: The steps include: S1: Model the battery of the drone and obtain the equivalent circuit model of the battery; S2: establishing a first objective function with the shortest charging time as the optimization goal according to the equivalent circuit model and the charging power of the battery; S3: establishing a second objective function with the longest flight time as the optimization goal according to the battery power after charging is completed; S4: setting constraints for the optimization of the first objective function and the second objective function, wherein the constraints include a maximum power constraint, a minimum power constraint, and a maximum power constraint; S5: Solve the first objective function and the second objective function.

2. The method for emergency charging of a mine drone based on target optimization according to claim 1 is characterized in that: The equivalent circuit model of the battery adopts a first-order RC equivalent circuit model, including the voltage U at both ends of the battery t , the equivalent voltage source voltage U inside the battery ocv , ohmic internal resistance R0, polarization internal resistance R1 and polarization capacitance C1 of the battery; R1 and C1 are connected in parallel and in series with R0, U t , U ocv , R0 forms a series circuit with the parallel connection of R1 and C1. The current flowing through the resistor R1 is i1, and the current flowing through the equivalent voltage source is i.

3. The method for emergency charging of a mine drone based on target optimization according to claim 2 is characterized in that: The UAV is equipped with four propeller motors as power sources, each propeller motor is equipped with a battery for independent power supply, one of the five batteries is an onboard computer battery, and the remaining four are propeller motor batteries; the four propeller motor batteries are distributed on the four wings of the UAV, respectively supplying power to the four propeller motors, and the onboard computer battery is distributed in the center of the UAV, supplying power to the onboard computer and onboard sensors; the method is based on a power management system to allocate and charge all batteries on the UAV.

4. The method for emergency charging of a mine drone based on target optimization according to claim 3 is characterized in that: In step S1, the process of establishing the equivalent circuit model is as follows: S11: Kirchhoff's law of voltage gives: IN t =U OCV +iR0+i1R1 (1-1) S12: The current across C1 is expressed as: S13: When the power management system charges the battery, solving equation (1-2) yields: Wherein, τ is the time constant of the first-order RC equivalent circuit model.

5. The method for emergency charging of a mine drone based on target optimization according to claim 4 is characterized in that: In step S2, the process of establishing the first objective function is as follows: S21: Assuming that the battery charging method is constant voltage charging, only the charging current is changed, and equations (1-1) to (1-3) are combined to calculate the charging current i(t); S22: The power allocated to the battery is obtained by formula (1-4): P(t)=U t i(t) (1-4) S23: During the charging process, the battery power is obtained by integrating the charging current, which is expressed by formula (1-5): Where E(t) is the battery charge at time t, E(0) is the initial charge of the drone when it enters the cabin, C0 is the rated capacity of the battery, I is the charging current, and η is the Coulomb efficiency coefficient; S24: Combining the above formulas (1-1) to (1-5), the first objective function of the charging power and charging time of each battery is obtained, which is expressed by formula (1-5): f1(P1, P2, P3, P4, P5) = max(t1(P1), t2(P2), t3(P3), t4(P4), t5(P5)) (1-6) Among them, t1, t2, t3, t4, and t5 are the charging times of the five batteries respectively, and P1, P2, P3, P4, and P5 are the charging powers allocated to an onboard computer battery and four paddle motor batteries respectively.

6. The method for emergency charging of a mine drone based on target optimization according to claim 5 is characterized in that: In step S3, the process of establishing the second objective function is as follows: S31: Assume that the power of each battery after charging is completed is E1, E2, E3, E4, E5 respectively. The working time of each battery can be calculated by formula (1-7): Where i = 2, 3, 4, 5, t w1 , t w2 , t w3 , t w4 , t w5 The working time of five batteries, P c1 is the power consumption of the onboard computer, P mi is the power of any propeller motor; S32: Obtain the flight time of the drone as expressed in formula (1-8): f2(E1,E2,E3,E4,E5)=min(t w1 (E1),t w2 (E2),t w3 (E3),t w4 (E4),t w5 (E5)) (1–8)。 7. The method for emergency charging of a mine drone based on target optimization according to claim 6 is characterized in that: In step S3, the maximum power constraint condition is as follows: the total charging power provided by the power management system to the five batteries must be limited to the maximum safe power P m Therefore, the maximum power constraint condition (1-9) is: P1+P2+P3+P4+P5≤P m (1-9) The constraint condition also includes a non-negative power constraint, which is as follows: In actual situations, the charging power cannot be a negative number, so there is a non-negative power constraint condition formula (1-10): P1≥0, P2≥0, P3≥0, P4≥0, P5≥0 (1-10) Among them, P1, P2, P3, P4, and P5 are the charging powers allocated to an onboard computer battery and four paddle motor batteries respectively.

8. The method for emergency charging of a mine drone based on target optimization according to claim 6 is characterized in that: The minimum power constraint condition is as follows: the power of each battery after charging is required to be greater than the minimum working power, ensuring that each battery can output the minimum voltage to maintain the operation of the drone. Therefore, there are minimum power constraint conditions (1-11) to (1-15): E1≥E cmin (1-11) E2≥E m1min (1-12) E3≥E m1min (1-13) E4≥E m2min (1-14) E5≥E m2min (1-15) Among them, E1, E2, E3, E4, and E5 are the power of each battery after charging. E1 is the power of the onboard computer battery after charging, E2 and E3 are the power of the two propeller motor batteries on the nose side after charging, and E4 and E5 are the power of the two propeller motor batteries on the tail side after charging. cmin is the minimum working capacity of the onboard computer battery, E m1min is the minimum working power of the two propeller motor batteries located on the nose side, E m2min It is the minimum operating power of the two propeller motor batteries located on the tail side of the aircraft.

9. The method for emergency charging of a mine drone based on target optimization according to claim 6, characterized in that: The maximum power constraint condition is as follows: the actual final power of the battery cannot exceed its rated capacity, so there are maximum power constraint condition equations (1-16) to (1-20): E1≤E cmax (1-16) E2≤E pmax (1-17) E3≤E pmax (1-18) E4≤E pmax (1-19) E5≤e pmax (1-20) Among them, E cmax is the rated capacity of the onboard computer battery, E pmax is the rated capacity of the propeller motor battery.

10. The method for emergency charging of a mine drone based on target optimization according to claim 6, characterized in that: In step S4, the optimization problem of the first objective function and the second objective function is obtained by solving the Pareto optimization.

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