A method and system for optimizing maintenance strategy of a hexacopter unmanned aerial vehicle

Abstract

The application provides a kind of optimization method of six-rotor unmanned aerial vehicle maintenance strategy, including determining the kind of maintenance strategy and the energy consumption cost of each maintenance strategy;Wherein, maintenance strategy includes preventive maintenance strategy, preventive replacement strategy, corrective maintenance strategy and start new component strategy;According to the energy consumption cost of each maintenance strategy, and combined with the preset variable to be solved, the objective function with the minimum system operation and maintenance cost rate as the target is constructed;Wherein, the variable to be solved includes unit state difference threshold, preventive maintenance frequency threshold and detection interval;The minimum solution of target function is iterated, and the optimal solution of unit state difference threshold, detection interval and preventive maintenance frequency threshold in the variable to be solved is obtained, to optimize and adjust the corresponding maintenance strategy. By implementing the application, the problem of unbalance of fault components and unbalance of unit state difference in six-rotor unmanned aerial vehicle can be solved at the same time, and the effectiveness and feasibility of maintenance strategy are improved.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to an optimization method and system for maintenance strategies of a six-rotor UAV. Background Technology

[0002] With the advancement of technology and social development, the application of balancing systems is gradually becoming more civilian, such as in aerial photography products and agricultural drones. A balancing system generally consists of 2n components, where each component is symmetrically arranged into n pairs of units. If at least one unit in the balancing system malfunctions, the balancing system will fail.

[0003] like Figure 1 As shown, the hexacopter UAV is a balancing system consisting of six spatially distributed arrays. Components 1 and 4, positioned symmetrically, form one pair of units; components 2 and 5 form another pair; and components 3 and 6 form yet another pair. If any component in any unit malfunctions, all components in that unit will cease operation, and appropriate actions will be taken to restore the balance of that unit.

[0004] Currently, existing maintenance strategies for hexacopter UAVs only consider the imbalance of faulty components within symmetrical units. However, in practical engineering applications, if the state difference of a particular unit is too large, that unit will also become unbalanced, thus affecting the system's equilibrium. Therefore, adjusting the unit state difference threshold is particularly important to improve the effectiveness and feasibility of maintenance strategies. Summary of the Invention

[0005] The technical problem to be solved by the embodiments of the present invention is to provide an optimization method and system for the maintenance strategy of a hexacopter UAV, which can simultaneously solve the problems of imbalance of faulty components and imbalance of unit state difference in hexacopter UAV, thereby improving the effectiveness and feasibility of the maintenance strategy.

[0006] To address the aforementioned technical problems, this invention provides an optimization method for the maintenance strategy of a six-rotor unmanned aerial vehicle (UAV), the method comprising the following steps:

[0007] The types of maintenance strategies and the energy consumption cost of each maintenance strategy are determined; there are four types of maintenance strategies, including preventive maintenance strategy, preventive replacement strategy, corrective maintenance strategy and new component activation strategy.

[0008] Based on the energy consumption cost of each maintenance strategy and combined with the preset variables to be solved, an objective function is constructed with the goal of minimizing the system operation and maintenance cost rate; wherein, the variables to be solved include the unit state difference threshold required in the preventive maintenance strategy, the preventive maintenance frequency threshold required in the preventive replacement strategy, and the detection interval required for polling each maintenance strategy.

[0009] The objective function is iteratively solved to find the minimum solution, thereby obtaining the optimal solutions for the unit state difference threshold, detection interval, and preventive maintenance frequency threshold among the variables to be solved, so as to optimize and adjust the corresponding maintenance strategy.

[0010] The objective function is expressed as follows:

[0011]

[0012]

[0013] R S (d,t)≥R min

[0014] Where C represents the system operation and maintenance cost rate; C w Energy cost per unit time; C w1 C represents the total energy cost consumed during system operation. w2 To save total energy costs in maintenance strategies; C pm The cost per preventative maintenance; C cm The cost per corrective maintenance; C pr The cost per preventative replacement / maintenance; d is the unit condition difference threshold; t is the inspection interval; m is the preventative maintenance frequency threshold; t max t represents the total system runtime. m Let P(t) be the total system maintenance time; P(t) is a function of the power change over time during system operation, and P xi (t) is a function of the power of the j-th component changing with time; Po ut This is the output power for normal system operation;

[0015] Total cost of preventative maintenance Total cost of preventative replacement for

[0016] Total cost of corrective maintenance; Indicates system availability;

[0017] This is the time for the j-th preventative maintenance. The time for the j-th preventative replacement; This is the time for the j-th corrective maintenance. The probability of the j-th preventative maintenance; The probability of the j-th preventative replacement; The probability of the j-th corrective maintenance.

[0018] The specific steps of iteratively finding the minimum solution for the objective function to obtain the optimal solutions for the unit state difference threshold, detection interval, and preventive maintenance frequency threshold among the variables to be solved, in order to optimize and adjust the corresponding maintenance strategy, include:

[0019] The minimum solution of the objective function is obtained by value iteration method, and it is determined that the values ​​corresponding to the unit state difference threshold, detection interval and preventive maintenance number threshold are all optimal when the objective function is minimized.

[0020] Based on the optimal solution of the unit state difference threshold, the unit state difference threshold in the preventive maintenance strategy is adjusted; and based on the optimal solution of the preventive maintenance frequency threshold, the preventive maintenance frequency threshold in the preventive replacement strategy is adjusted.

[0021] This invention also provides an optimization system for the maintenance strategy of a six-rotor unmanned aerial vehicle, comprising:

[0022] The maintenance strategy determination unit is used to determine the types of maintenance strategies and the energy consumption cost of each maintenance strategy; wherein, there are four types of maintenance strategies, including preventive maintenance strategy, preventive replacement strategy, corrective maintenance strategy and new component activation strategy.

[0023] The objective function construction unit is used to construct an objective function with the goal of minimizing the system operation and maintenance cost rate, based on the energy consumption cost of each maintenance strategy and in combination with preset variables to be solved; wherein, the variables to be solved include the unit state difference threshold required in the preventive maintenance strategy, the preventive maintenance frequency threshold required in the preventive replacement strategy, and the detection interval required for polling each maintenance strategy.

[0024] The maintenance strategy optimization and adjustment unit is used to iteratively find the minimum solution of the objective function, and obtain the optimal solutions of the unit state difference threshold, detection interval and preventive maintenance number threshold among the variables to be solved, so as to optimize and adjust the corresponding maintenance strategy.

[0025] The objective function is expressed as follows:

[0026]

[0027]

[0028] R S (d,t)≥R min

[0029] Where C represents the system operation and maintenance cost rate; C w Energy cost per unit time; C w1 C represents the total energy cost consumed during system operation.w2 To save total energy costs in maintenance strategies; C pm The cost per preventative maintenance; C cm The cost per corrective maintenance; C pr The cost per preventative replacement / maintenance; d is the unit condition difference threshold; t is the inspection interval; m is the preventative maintenance frequency threshold; t max t represents the total system runtime. m Let P(t) be the total system maintenance time; P(t) is a function of the power change over time during system operation, and P xi (t) is a function of the power of the j-th component changing with time; Po ut This is the output power for normal system operation;

[0030] Total cost of preventative maintenance; Total cost of preventative replacement; Total cost of corrective maintenance; Indicates system availability;

[0031] This is the time for the j-th preventative maintenance. The time for the j-th preventative replacement; This is the time for the j-th corrective maintenance. The probability of the j-th preventative maintenance; The probability of the j-th preventative replacement; The probability of the j-th corrective maintenance.

[0032] The maintenance strategy optimization and adjustment unit includes:

[0033] The objective function solution module is used to find the minimum solution of the objective function through the value iteration method, and to determine that the minimum solution of the objective function is the optimal solution for the values ​​corresponding to the unit state difference threshold, detection interval, and preventive maintenance number threshold.

[0034] The maintenance strategy optimization and adjustment module is used to adjust the unit state difference threshold in the preventive maintenance strategy according to the optimal solution of the unit state difference threshold, and to adjust the preventive maintenance frequency threshold in the preventive replacement strategy according to the optimal solution of the preventive maintenance frequency threshold.

[0035] Implementing the embodiments of the present invention has the following beneficial effects:

[0036] This invention proposes a maintenance strategy for hexacopter UAVs by combining the branching and energy-saving approach. An objective function is established, and the optimal solutions for the unit state difference threshold and detection interval are obtained with the goal of minimizing the system operation and maintenance cost rate. The maintenance strategy is then optimized and adjusted based on the obtained optimal solutions, thereby simultaneously solving the problems of imbalance of faulty components and imbalance of unit state difference in hexacopter UAVs, and improving the effectiveness and feasibility of the maintenance strategy. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, obtaining other drawings based on these drawings without creative effort still falls within the scope of the present invention.

[0038] Figure 1 A model diagram illustrating the system balance of a hexarotor unmanned aerial vehicle (UAV) in the prior art;

[0039] Figure 2 A flowchart illustrating an optimization method for a maintenance strategy of a hexarotor unmanned aerial vehicle (UAV) provided in an embodiment of the present invention;

[0040] Figure 3 The graph shows the changing trend of the objective function when the threshold value of preventive maintenance times M = 2 in a simulation scenario of an optimization method for maintenance strategy of a six-rotor UAV provided in an embodiment of the present invention.

[0041] Figure 4 The graph shows the trend of system operation and maintenance cost rate when the threshold value of preventive maintenance times M=3 in a simulation scenario of an optimization method for maintenance strategy of a six-rotor UAV provided in an embodiment of the present invention.

[0042] Figure 5 The graph shows the trend of system reliability changes when the preventive maintenance frequency threshold M=2, the detection interval t=11, and the unit state difference threshold d=12 in a simulation scenario of an optimization method for maintenance strategy of a hexarotor UAV provided in an embodiment of the present invention.

[0043] Figure 6 This is a schematic diagram of the structure of an optimization system for a maintenance strategy of a hexacoach UAV provided in an embodiment of the present invention. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings.

[0045] like Figure 2As shown in the figure, an optimization method for maintenance strategy of a hexacoach UAV is provided in an embodiment of the present invention, including the following steps:

[0046] Step S1: Determine the types of maintenance strategies and the energy consumption cost of each maintenance strategy; wherein, there are four types of maintenance strategies, including preventive maintenance strategy, preventive replacement strategy, corrective maintenance strategy and new component activation strategy.

[0047] The specific process is as follows: First, establish the following four types of maintenance strategies:

[0048] (1) Preventive maintenance strategy: When a unit is in an unbalanced state, that is, the state difference between the two components in a pair of units is too large and exceeds the preset unit state difference threshold, it is necessary to perform operations such as increasing or decreasing the power of the components to achieve the rebalancing of the unit.

[0049] (2) Preventive replacement strategy: When the number of preventive maintenance cycles for a component is m i When a preset threshold for preventative maintenance is reached, preventative replacement is performed. After this operation, the number of preventative maintenance cycles for that component is reset to zero. This strategy is m. i =0.

[0050] (3) Corrective maintenance strategy: When a component is in a faulty state, corrective maintenance is performed on the component to restore it to its original condition. After corrective maintenance, the number of preventive maintenance operations for the component is reduced to zero, i.e., m i =0.

[0051] (4) Strategy for starting new components: When the output of the system does not meet the minimum output requirement, a strategy of starting new or better paired components is combined with the branching method.

[0052] Secondly, it is assumed that in the initial stage, all components are in a brand new and intact state, and their initial degradation degree is zero; it is assumed that in the system, the degradation distribution of all components follows the same parameter and is obtained at discrete and equidistant detection time points; it is assumed that the maintenance measures of all components are perfect maintenance, and different types of maintenance operations can be performed simultaneously; it is assumed that the maintenance time is a random variable, following an exponential distribution with parameter γ, and that the single maintenance time is the longest time among the different types of maintenance operations.

[0053] At this point, energy consumption is calculated in two categories. The first category is the energy consumption cost W1 during normal equipment operation, and the other part is the energy consumption cost W2 incurred during preventive maintenance, which involves adjusting the energy consumption of various components. This latter part represents energy savings, and its calculation formulas are shown below:

[0054]

[0055]

[0056] The total cost of preventive maintenance was determined to be The total cost of preventative replacement is And the total cost of corrective maintenance is Among them, C pm The cost per preventative maintenance; C pr The cost per preventative replacement / maintenance; C cm The cost per corrective maintenance; This is the time for the j-th preventative maintenance. The time for the j-th preventative replacement; This is the time for the j-th corrective maintenance. The probability of the j-th preventative maintenance; The probability of the j-th preventative replacement; The probability of the j-th corrective maintenance.

[0057] It should be noted that the cost per preventive maintenance, the cost per preventive replacement, and the cost per corrective maintenance are known, the degradation follows a gamma distribution, and its shape and size parameters can also be found.

[0058] Step S2: Based on the energy consumption cost of each maintenance strategy and combined with the preset variables to be solved, construct an objective function with the goal of minimizing the system operation and maintenance cost rate; wherein, the variables to be solved include the unit state difference threshold required in the preventive maintenance strategy, the preventive maintenance frequency threshold required in the preventive replacement strategy, and the detection interval required for polling each maintenance strategy.

[0059] The specific process is as follows: the normal operation of the system requires certain output requirements to be met. Therefore, the system output is used as one of the constraints for starting the components in the maintenance strategy, as shown below:

[0060]

[0061] Where P(t) is a function of the power change over time during system operation, and P xi (t) is a function of the power of the j-th component changing with time; Po ut This is the output power for normal system operation;

[0062] Furthermore, higher system availability generally correlates with better system performance. Therefore, system availability is considered as one of the constraints, and its expression is as follows:

[0063]

[0064] Based on the maintenance strategy of this system, the system availability can be further derived as follows:

[0065]

[0066] At this point, combining the idea of ​​branching and splitting, a maintenance strategy for starting new components is proposed to solve for the minimum system maintenance cost rate, as shown below:

[0067] j = minC w y1-C w y2+C pm y3+C pr y4

[0068] stdy1-dy1+100y3-d2y4≥P out

[0069] y1<d

[0070] y2<d

[0071] y1-y2+2y3>0

[0072]

[0073] Where y1 represents the number of times energy consumption is increased during preventive maintenance, y2 represents the number of times energy consumption is reduced during preventive maintenance, y3 represents the number of times new components are started, and y4 represents the number of times preventive replacements are performed.

[0074] Therefore, considering the constraints mentioned above, the objective function can be expressed as follows:

[0075]

[0076]

[0077] R S (d,t)≥R min

[0078] Where C represents the system operation and maintenance cost rate; C w Energy cost per unit time; C w1 C represents the total energy cost consumed during system operation. w2 The total cost of energy saving for maintenance strategies; d is the unit state difference threshold; t is the detection interval; m is the threshold for preventive maintenance times; t max t represents the total system runtime. m This represents the total system maintenance time.

[0079] Step S3: Iterate the objective function to find the minimum solution, and obtain the optimal solutions for the unit state difference threshold, detection interval and preventive maintenance number threshold among the variables to be solved, so as to optimize and adjust the corresponding maintenance strategy.

[0080] The specific process is as follows: First, the minimum solution of the objective function is obtained by value iteration method, and it is determined that the minimum solution of the objective function is the optimal solution for the values ​​corresponding to the unit state difference threshold, the detection interval, and the preventive maintenance number threshold. Second, the unit state difference threshold in the preventive maintenance strategy is adjusted according to the optimal solution of the unit state difference threshold, and the preventive maintenance number threshold in the preventive replacement strategy is adjusted according to the optimal solution of the preventive maintenance number threshold.

[0081] like Figures 3 to 5 As shown, the simulation scenario for optimizing a maintenance strategy for a hexacopter UAV in an embodiment of the present invention is further illustrated below:

[0082] Taking a hexacopter drone as an example, it consists of six identical and symmetrical components, each of which is prone to degradation and failure. Assuming the degradation process is a gamma process, its shape parameter α is 4.092e. -4 The scaling parameter β is 8.1428. The minimum system maintenance cost rate is found by optimizing the state difference threshold Δd and the detection interval Δt. Maintenance parameters related to the system are referenced from the maintenance costs of DJI agricultural drones, and can be collected and calculated as follows: C1 = 60, C... p =240, C pr =420, C c =3599, C w =31.5074592.

[0083] Combining the maintenance strategy proposed by the branch cutting method, the optimal solution of the minimum operation and maintenance cost rate model of the system is found by jointly optimizing the detection interval t and the state difference threshold d. t∈[1,20], d∈[10,20].

[0084] Depend on Figure 3 It can be seen that as the detection time t increases, the system maintenance cost rate changes in a parabolic curve opening upwards, with the objective function having a minimum point in the interval [1, 20]. As the state difference threshold d increases, the system maintenance cost first decreases and then continuously increases, with a minimum point in the interval [1, 11]. Therefore, by jointly optimizing the decision variables, the minimum system maintenance cost rate is found, i.e., when the detection interval t = 11 and the unit state difference threshold d = 12, the minimum system maintenance cost rate C = 32.1456. At this time, the system preventive maintenance frequency threshold M = 2.

[0085] When the threshold for preventive maintenance frequency M = 3, the objective function also shows a corresponding trend, such as... Figure 4As shown. At this time, when the detection interval t = 11 and the unit state difference threshold d = 11, the minimum system operation and maintenance cost rate C = 32.1514.

[0086] The comparison shows that when M=3, the change trend of system operation and maintenance costs is relatively gradual, and its rate of change is less than that when M=2. Furthermore, when M=2, the joint optimization yields better results.

[0087] Next, we will verify the system reliability when M=2, t=11, and d=12. Based on the schematic diagram of the hexacopter UAV system component distribution, the system reliability formula can be obtained as shown below.

[0088]

[0089] The system reliability data obtained from the simulation experiment is plotted as follows: Figure 5 As shown, the minimum system reliability is greater than 0.9, which meets the system's reliability requirements. Therefore, the scheme is effective. Figure 5 It can be seen that the highest system reliability is R = 0.91343, at which point the detection interval t = 10 and the state difference threshold is 12. However, the optimal solution for the minimum system maintenance cost rate is t = 11 and d = 12, at which point the reliability R = 0.91342. Comparing the two, for every unit reduction in the detection interval of 1, the cost increases by 68.18, an increase of 0.090906666666667%, while the reliability increases by 0.00001, an increase of 0.000010947866261%.

[0090] like Figure 6 As shown in the figure, an optimization system for maintenance strategy of a hexacoach UAV is provided in an embodiment of the present invention, comprising:

[0091] The maintenance strategy determination unit 110 is used to determine the types of maintenance strategies and the energy consumption cost of each maintenance strategy; wherein, there are four types of maintenance strategies, including preventive maintenance strategy, preventive replacement strategy, corrective maintenance strategy and new component activation strategy.

[0092] The objective function construction unit 120 is used to construct an objective function with the goal of minimizing the system operation and maintenance cost rate based on the energy consumption cost of each maintenance strategy and in combination with the preset variables to be solved; wherein, the variables to be solved include the unit state difference threshold required in the preventive maintenance strategy, the preventive maintenance number threshold required in the preventive replacement strategy, and the detection interval required for polling each maintenance strategy.

[0093] The maintenance strategy optimization and adjustment unit 130 is used to iteratively find the minimum solution of the objective function to obtain the optimal solutions of the unit state difference threshold, detection interval and preventive maintenance number threshold among the variables to be solved, so as to optimize and adjust the corresponding maintenance strategy.

[0094] The objective function is expressed as follows:

[0095]

[0096]

[0097] R S (d,t)≥R min

[0098] Where C represents the system operation and maintenance cost rate; C w Energy cost per unit time; C w1 C represents the total energy cost consumed during system operation. w2 To save total energy costs in maintenance strategies; C pm The cost per preventative maintenance; C cm The cost per corrective maintenance; C pr The cost per preventative replacement / maintenance; d is the unit condition difference threshold; t is the inspection interval; m is the preventative maintenance frequency threshold; t max t represents the total system runtime. m Let P(t) be the total system maintenance time; P(t) is a function of the power change over time during system operation, and P xi (t) is a function of the power of the j-th component changing with time; Po ut This is the output power for normal system operation;

[0099] Total cost of preventative maintenance; Total cost of preventative replacement; Total cost of corrective maintenance; Indicates system availability;

[0100] This is the time for the j-th preventative maintenance. The time for the j-th preventative replacement; This is the time for the j-th corrective maintenance. The probability of the j-th preventative maintenance; The probability of the j-th preventative replacement; The probability of the j-th corrective maintenance.

[0101] The maintenance strategy optimization and adjustment unit includes:

[0102] The objective function solution module is used to find the minimum solution of the objective function through the value iteration method, and to determine that the minimum solution of the objective function is the optimal solution for the values ​​corresponding to the unit state difference threshold, detection interval, and preventive maintenance number threshold.

[0103] The maintenance strategy optimization and adjustment module is used to adjust the unit state difference threshold in the preventive maintenance strategy according to the optimal solution of the unit state difference threshold, and to adjust the preventive maintenance frequency threshold in the preventive replacement strategy according to the optimal solution of the preventive maintenance frequency threshold.

[0104] Implementing the embodiments of the present invention has the following beneficial effects:

[0105] This invention proposes a maintenance strategy for hexacopter UAVs by combining the branching and energy-saving approach. An objective function is established, and the optimal solutions for the unit state difference threshold and detection interval are obtained with the goal of minimizing the system operation and maintenance cost rate. The maintenance strategy is then optimized and adjusted based on the obtained optimal solutions, thereby simultaneously solving the problems of imbalance of faulty components and imbalance of unit state difference in hexacopter UAVs, and improving the effectiveness and feasibility of the maintenance strategy.

[0106] It is worth noting that the various units included in the above system embodiments are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0107] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as ROM / RAM, disk, optical disk, etc.

[0108] The above description discloses only preferred embodiments of the present invention and should not be construed as limiting the scope of the present invention. Therefore, equivalent variations made in accordance with the claims of the present invention are still within the scope of the present invention.

Claims

1. A method for optimizing the maintenance strategy of a hexacopter unmanned aerial vehicle (UAV), wherein the hexacopter UAV comprises three sets of diagonally arranged symmetrical power units, each set of symmetrical power units including a pair of rotor assemblies, the method comprising collecting the degradation state of each rotor assembly and calculating the difference in degradation state between two rotor assemblies in the same set, characterized in that, include: An optimization model for maintenance strategy of a six-rotor UAV was established, with the unit state difference threshold, system detection cycle, and cumulative number of preventive maintenance as joint optimization variables; When the difference in degradation state between the two rotor components of the same symmetrical power unit exceeds the unit state difference threshold, the symmetrical power unit is determined to be an unbalanced power unit. A maintenance strategy optimization objective function is established with the goal of minimizing the system's unit time maintenance cost rate. Under the condition of meeting the minimum system availability requirements and the minimum output power requirements of the whole machine, the joint optimization variables are jointly optimized. The additional energy consumption caused by the mismatch of the output power of the same group of symmetrical power units is included in the objective function. Based on the optimization results, the corresponding maintenance strategy is determined. When the unbalanced power unit reaches the maintenance conditions, preventive maintenance is performed. When the cumulative number of preventive maintenance reaches the corresponding threshold, preventive replacement is performed. When a rotor assembly failure is detected, corrective maintenance is performed. During maintenance and operation, when there is an unbalanced power unit or the overall output power is lower than the preset requirements, the backup paired rotor assembly is activated to participate in power compensation, balance the unbalanced power unit, and maintain the flight stability of the six-rotor UAV.

2. The optimization method for the maintenance strategy of a six-rotor UAV as described in claim 1, characterized in that, The expression for the objective function is shown below: in, The system operation and maintenance cost rate; Energy cost per unit time; This represents the total energy consumption cost during system operation. The total cost of energy saving in maintaining the strategy; Cost per preventative maintenance; The cost per corrective maintenance; Cost per preventative replacement / maintenance; The threshold for the difference in unit state; For detection interval; This is a threshold for the number of preventative maintenance operations. Total system uptime; Total system maintenance time; Let be a function of power changing with time during system operation, and ; Let be a function of the power of the j-th component as a function of time; This is the output power for normal system operation; Total cost of preventative maintenance; Total cost of preventative replacement; Total cost of corrective maintenance; This indicates the availability of the system. This is the time for the j-th preventative maintenance. The time for the j-th preventative replacement; This is the time for the j-th corrective maintenance. The probability of the j-th preventative maintenance; The probability of the j-th preventative replacement; The probability of the j-th corrective maintenance.

3. The optimization method for the maintenance strategy of a six-rotor UAV as described in claim 1, characterized in that, The joint optimization of the joint optimization variables includes: The minimum solution of the objective function is obtained by value iteration method, and it is determined that the values ​​corresponding to the unit state difference threshold, detection interval and preventive maintenance number threshold are all optimal when the objective function is minimized. Based on the optimal solution of the unit state difference threshold, the unit state difference threshold in the preventive maintenance strategy is adjusted; and based on the optimal solution of the preventive maintenance frequency threshold, the preventive maintenance frequency threshold in the preventive replacement strategy is adjusted.

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