High-performance unmanned aerial vehicle task reliability simulation method based on Monte Carlo

Abstract

The invention discloses a Monte Carlo-based high-performance unmanned aerial vehicle task reliability simulation method, and belongs to the research field of unmanned aerial vehicle task reliability simulation. According to the method, an unmanned aerial vehicle system is divided into a unit level, a system level and a task level in sequence, and each level of system is packaged into an independent simulation sampling function and then integrated into reliability simulation of the whole aircraft. In the simulation process of the mission profile, simulation sampling is proposed according to the time interval, the simulation sampling state is recorded, and the simulation sampling at the next moment needs to consider the state at the previous moment.

Description

Technical Field

[0001] The invention patent relates to the research field of UAV mission reliability simulation, specifically a high-performance UAV mission reliability simulation method based on Monte Carlo. Background Art

[0002] When a drone performs a mission, mission reliability directly affects the mode and scale of the drone's mission, as well as its ability to continuously perform missions, and has a direct impact on the improvement and performance of mission efficiency. The current PMS reliability analysis methods are roughly divided into two categories: analytical models and simulation methods. Analytical models can only solve small-scale or medium-scale problems. For example, the Markov method has a large memory footprint and a long calculation time, and can only handle small-scale problems; the PMS-BDD model and the PMS-MMDD model have certain approximate processing and can handle medium-scale problems; the most commonly used simulation method is the Monte Carlo simulation algorithm, which takes a long time to calculate, but can handle large problems.

[0003] High-performance UAVs have numerous components and systems, and are not suitable for calculating mission reliability using analytical methods such as Markov and PMS-MMDD. In addition, the traditional Monte Carlo method makes it difficult to simultaneously consider environmental factors and system status when high-performance UAVs perform missions. Directly establishing a UAV simulation model is not conducive to the scalability of the model. Summary of the invention

[0004] In view of the shortcomings of the background technology, the present invention divides the UAV system into unit level, system level and mission level in turn, and encapsulates each level system into a separate simulation sampling function, which is then integrated into the reliability simulation of the overall aircraft. In the simulation process of the mission profile, the present invention proposes simulation sampling at time intervals, and records the simulation sampling state, and the simulation sampling at the next moment needs to consider the state of the previous moment.

[0005] Aiming at the complex system composition in the reliability simulation of high-performance UAV mission, the present invention proposes a Monte Carlo-based reliability simulation method for high-performance UAV mission, which includes the following steps:

[0006] Step 1: Build a high-performance UAV system. The high-performance UAV system is divided into four parts: the body platform layer, the information and communication system, the perception system, and the operation system.

[0007] The fuselage platform layer includes subsystems that affect the normal flight of the aircraft, including: 2 redundant hydraulic systems, 2 redundant flight control systems, 3 redundant fuel tanks, 2 redundant fuel supply pumps, single or twin engine systems, 2 redundant power supply systems and landing gear systems;

[0008] The information communication system realizes the mutual communication between the aircraft and the ground, and between aircrafts, and is composed of a low-frequency rack, a 2-degree redundant antenna device, and a transceiver;

[0009] The perception system is a phased array radar including: an active detection system and a passive detection system; the active detection system consists of an information processing subsystem, a power subsystem and a 2-redundant active detection antenna subsystem; the passive detection system consists of an information processing subsystem, a power subsystem and a passive detection antenna subsystem, of which the passive detection antenna subsystem is a voting system, containing a total of 5 antenna units, and 4 or more units need to work normally to ensure the normal operation of the passive detection antenna subsystem;

[0010] The operation system is used to connect, carry, launch and release the operation unit to the aircraft, and operate the operation unit according to the timing, accurate direction, required accuracy and duration to complete the established operation tasks; the operation system includes: operation unit controller, 2-degree redundant door drive system, launch device and operation unit;

[0011] Except for the passive detection antenna subsystem which is a voting system, the rest of the redundancy systems are simplified to parallel redundant systems, and different subsystems are in series relationship.

[0012] Step 2: Calculate the unit-level failure rate, where the unit level is the smallest component of the system;

[0013] Step 2.1: According to the temperature, load and vibration of the drone, the environmental impact level is divided into three levels, namely "good", "medium" and "bad", and it is assumed that the environmental impact level of different units at the same time is the same; set thresholds for different environmental factors, obtain the impact level value of each environmental factor, and then calculate the weighted average to obtain the overall environmental impact level;

[0014] Step 2.2: Calculate the unit-level failure rate. Assume that the unit-level failure satisfies the exponential distribution. The unit-level failure situation is described in the form of the interval between serious failures. The failure rate and the interval between serious failures are inversely proportional. The failure rate calculation formula is:

[0015]

[0016] Wherein, the subscript i represents the environmental level, and its values ​​are 1, 2, and 3, which represent the environmental impact levels of “good”, “medium”, and “bad” respectively; i Represents the unit failure rate corresponding to the environmental level i; MTBCF i represents the serious failure interval corresponding to the environmental level i; the unit reliability function that satisfies the exponential distribution is:

[0017]

[0018] Where t represents the sampling interval, R i Represents the task reliability corresponding to the environment level i;

[0019] Step 2.3: Unit-level state simulation sampling. The reliability of each unit task satisfies the exponential distribution. Assuming that the probability of normal operation of the unit is reliability R, the probability of unit failure is 1-R. The unit state is randomly sampled to obtain the 0 and 1 values ​​of the unit state. 0 represents unit failure and 1 represents normal operation of the unit.

[0020] Assume that all units are non-repairable components, and each unit state has a storage function. The unit state sampled at the current moment is stored, and the unit state at the previous moment needs to be considered at the next sampling moment. For the faulty unit, the state sampling is no longer performed, and the fault is directly returned;

[0021] Step 3: Calculate the system-level failure rate;

[0022] Step 3.1: Draw a system reliability block diagram based on the system's working logic. The parallel redundancy of the units is connected in parallel, and different units are connected in series.

[0023] Step 3.2: Based on the unit-level state sampling results of step 2.3, the system-level state sampling results are obtained through the logical relationship of the system reliability block diagram. The relationship between the system-level state simulation sampling and the unit-level state simulation sampling is as follows:

[0024] S=f(x1,x2,…,x n )

[0025] Among them, S represents the system state; x i represents the unit state; f is a logical function determined by the reliability block diagram of the system;

[0026] Step 4: Divide the task-level modules according to task requirements and define the task success criteria;

[0027] Step 4.1: Divide the UAV into: platform and communication layer, mission load layer; the platform and communication layer is the basis for the successful execution of the UAV mission, and is composed of systems that affect the normal flight and link communication of the aircraft, including: engine, flight control system, hydraulic system and information communication system; the mission load layer includes: operation system, detection system; when executing a mission, the specific mission load layer will be called, and different UAVs can carry different mission loads;

[0028] Step 4.2: Divide the mission phases. The mission phases are divided into take-off, landing, cruising and operation. All mission phases will use the airframe platform, and different mission phases use different mission payloads. Different mission phases have different environmental levels. The environmental level of each mission phase is determined according to the method in step 2.1.

[0029] Step 4.3: Determine the criteria for mission success. The criteria for mission success are based on the normal operation of the platform and communication layer, and the mission payload to be used is selected according to the characteristics of the mission stage;

[0030] Step 5: Calculate task reliability using the Monte Carlo method;

[0031] Step 5.1: Set constants, including determining the unit-level failure rate according to step 2.2, building the system-level reliability block diagram according to step 3.1, defining the method task-level modules and task stages according to step 4, setting the simulation sampling interval t0, and the total task duration is expressed as follows:

[0032]

[0033] Among them, T total represents the total duration of the mission, H represents the mission profile contains H stages, h represents one of the stages, T h represents the task time of stage h;

[0034] Step 5.2: Set the initial variables, let n = 0, Ns = 0, where n represents the number of simulations performed and Ns represents the number of successful task executions;

[0035] Step 5.3: Initialize the simulation time t, set t = 0, clear the stored system state; set h = 1, start the simulation from the first task stage;

[0036] Step 5.4: For the current task stage h, determine the task stage success criteria and the task-level modules to be used;

[0037] Step 5.5: Using the Monte Carlo failure link, perform a single state sampling of the UAV system required for the mission and store the sampled state;

[0038] Step 5.6: Determine whether the sampling status can meet the requirements of the task stage. If it can, go to step 5.7; if not, go to step 5.10;

[0039] Step 5.7: Determine whether the task of this stage is completed. When t≥T h , indicating that the calculation of this task stage is completed, let h=h+1, and go to step 5.8; when t<T h , then let t = t + t0 and go to step 5.5;

[0040] Step 5.8: Determine whether all task stages have been completed. total , then all task stages are completed and go to step 5.9; when t<T total , then it is necessary to enter the next task stage to continue the calculation, set t = t + t0, h = h + 1, and enter step 5.4;

[0041] Step 5.9: Let the number of successful executions of the task Ns = Ns + 1;

[0042] Step 5.10: Determine whether the number of calculations has reached the requirement. When n≥N, the calculation ends and the task reliability is calculated as R=Ns / N, where N is the total number of simulations. If n<N, set n=n+1 and go to step 5.3.

[0043] Furthermore, the specific method of step 2.1 is:

[0044] The relationship between failure rate and temperature presents a U-shaped curve, which is relatively flat within the normal operating temperature range, but will significantly increase the failure rate when the temperature is too high or too low; the environmental grade scores for setting thresholds to divide the temperature are as follows:

[0045]

[0046] Among them, T score is the environmental grade score of temperature, with values ​​of 3, 2, and 1, corresponding to the temperature grades of “good”, “medium”, and “bad” respectively; T l_min and T l_max In the temperature threshold set for low temperature environment, T h_min and T h_max The temperature thresholds set in a high temperature environment, their magnitude relationships are as follows:

[0047] T l_min <T l_max <T h_min <T h_max ;

[0048] The relationship between the failure rate and the load is positively correlated. The greater the load, the higher the failure rate. The environmental grade scores for setting the threshold to divide the load are as follows:

[0049]

[0050] Among them, L score V is the environmental grade score of the load, with values ​​of 3, 2, and 1, corresponding to the load grades of "good", "medium", and "bad" respectively; l and V h The load thresholds, their magnitude relationships are as follows:

[0051] Ll <L h ;

[0052] The relationship between failure rate and vibration is positively correlated. The greater the vibration, the higher the failure rate. The environmental grade scores for vibration are divided by setting the threshold as follows:

[0053]

[0054] Among them, V score V is the environmental grade of vibration, with values ​​of 3, 2, and 1, corresponding to the vibration grades of “good”, “medium”, and “bad” respectively; l and V h The vibration thresholds are related as follows:

[0055] V l <V h ;

[0056] Then, the weighted average is calculated to obtain the overall environmental grade score, which is calculated as follows:

[0057]

[0058] Among them, FS is the overall environmental grade score, T score is the environmental grade score of temperature, L score is the environmental grade score of the load, V score is the environmental grade score of vibration; w1, w2, w3 are T score , L score , V score The weight of

[0059] Finally, the overall environmental grade is obtained according to the overall environmental grade score FS. The specific standards are as follows:

[0060]

[0061] Where F represents the overall environmental level, with values ​​of "good", "medium" and "bad"; FS is the overall environmental level score.

[0062] Further, according to the method of step 3.2, the relationship between the active detection system state simulation sampling and the unit level state simulation sampling is as follows:

[0063] S detection =x 信号处理子系统 ∧x 电源子系统 ∧(x 天线1 ∨x 天线2 )

[0064] Among them, S detection is the state sampling result of the active detection system, x信号处理子系统 and x 电源子系统 Represent the state sampling results of the signal processing subsystem and the power subsystem, respectively, 天线1 and x 天线2 Represents the state sampling results of the two redundancy units of the antenna subsystem.

[0065] Furthermore, the criteria for successful execution of the task in step 4.3 are as follows:

[0066]

[0067] Among them, M represents the success of the mission, P represents the normal operation of the platform and communication layer, K represents the normal operation of the mission payload, n represents the number of mission payloads used in the mission phase, and K i|P It means that the i-th task load can work normally only when the platform and communication layer P are available.

[0068] The present invention establishes a Monte Carlo-based single-machine mission reliability evaluation method for high-performance UAVs performing multi-stage missions, divides the simulation model into multiple levels, takes into account the environmental impact level and sampling state storage, and makes the reliability analysis closer to the actual situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 The corresponding discount diagrams for the reliability calculations of the PMS-MMDD method and the simulation method.

[0070] Figure 2 The present invention is a flow chart of a specific implementation method.

[0071] Figure 3 Flowchart of the unit-level simulation modeling approach.

[0072] Figure 4 Flowchart of the modeling approach for system-level simulation.

[0073] Figure 5 The following is an example of a reliability block diagram, taking the body platform layer of a reconnaissance UAV as an example.

[0074] Figure 6 Flowchart of the modeling approach for task-level simulation. Figure 7 This is the task reliability simulation flow chart. DETAILED DESCRIPTION

[0075] The present invention is a multiple simulation experiment, the sampling number is set to 10,000 times, the sampling interval t0 is set to 1 minute, FH represents the number of intervals, and the MTBCF and mission profile of each system unit of the UAV in the experiment are defined as follows:

[0076] Table 1 Single redundancy MTBCF of each subsystem of the platform system

[0077]

[0078] Table 2 Single redundancy MTBCF of each submodule of information communication system

[0079]

[0080]

[0081] Table 3 Single redundancy MTBCF of each subsystem of the perception system

[0082]

[0083] Table 4 Single redundancy MTBCF of each submodule of the operating system

[0084]

[0085] Table 5 Mission profile definition

[0086] Mission Phase Number Task Phase Name Time (min) Engine status Environmental level 1 Takeoff Roll 2 maximum bad 2 cruise 15 cruise middle 3 Operation 15 maximum bad 4 Downward 5 local middle 5 cruise 10 cruise good 6 Operation 15 maximum bad 7 cruise 25 cruise middle 8 Downward 16 local good 9 Landing Roll 2 local bad

[0087] According to the simulation method defined in the present invention, the simulation result of the UAV mission reliability is R=0.945833, and the reliability data range is consistent with the actual engineering situation.

[0088] In order to verify the correctness of this method, the model can be further simplified, and the calculation results of the PMS-MMDD analytical method and the Monte Carlo simulation method can be used for comparative verification. The simplified model is as follows:

[0089] Assume that a certain type of UAV is equipped with a single engine (A), two redundant fuel pumps (B\C), and a fuel tank (D). The status of each subsystem is divided as follows:

[0090] Engine status classification: 1 engine slow, 2 engine cruising, 3 engine maximum, 4 engine failure.

[0091] The status of No. 1 fuel supply pump is divided into: 1 intact, 2 faulty.

[0092] The status of the No. 2 fuel supply pump is divided into: 1 intact, 2 faulty.

[0093] Fuel tank status classification: 1 intact, 2 faulty.

[0094] The mission profile is defined as follows: the mission is divided into three phases: take-off phase, cruise phase and operation phase. The mission environments of the three mission phases are medium, good and bad respectively; the mission phase times are 2 minutes, 25 minutes and 15 minutes respectively.

[0095] The takeoff phase requires A to be in state 3, B to be in state 1, C to be in state 1, and D to be in state 1.

[0096] The cruise phase requires A to be in state 2 and B to be in state 1 or C to be in state 1 and D to be in state 1.

[0097] The combat phase requires A to be in state 3, B to be in state 1, C to be in state C1, and D to be in state 1.

[0098] The number of simulations is set to 10,000 times, and the reliability result of the simulation method of this patent is R=0.988180, which is basically consistent with the task reliability calculation result R=0.988403271 based on the PMS-MMDD model method.

[0099] In order to make a more accurate comparison, the time of task phase 2 is set as a variable, and the reliability calculations of the PMS-MMDD method and the simulation method are performed respectively. The calculation results are shown in Table 6, and the corresponding discount diagram is shown in Figure 1 According to the reliability comparison results, it can be seen that the reliability evaluation results of the PMS-MMDD method and the Monte Carlo method proposed in this patent are basically consistent, which verifies the correctness of the method of this patent.

[0100] Table 6 Reliability comparison between PMS-MMDD method and simulation method

[0101]

Claims

1. A Monte Carlo-based high-performance UAV mission reliability simulation method, the method comprising the following steps: Step 1: Build a high-performance UAV system. The high-performance UAV system is divided into four parts: the body platform layer, the information and communication system, the perception system, and the operation system. The fuselage platform layer includes subsystems that affect the normal flight of the aircraft, including: It consists of 2 redundant hydraulic systems, 2 redundant flight control systems, 3 redundant fuel tanks, 2 redundant fuel supply pumps, single or twin engine systems, 2 redundant power supply systems and landing gear systems; The information communication system realizes the mutual communication between the aircraft and the ground, and between aircrafts, and is composed of a low-frequency rack, a 2-degree redundant antenna device, and a transceiver; The perception system is a phased array radar including: an active detection system and a passive detection system; the active detection system consists of an information processing subsystem, a power subsystem and a 2-redundant active detection antenna subsystem; the passive detection system consists of an information processing subsystem, a power subsystem and a passive detection antenna subsystem, of which the passive detection antenna subsystem is a voting system, containing a total of 5 antenna units, and 4 or more units need to work normally to ensure the normal operation of the passive detection antenna subsystem; The operation system is used to connect, carry, launch and release the operation unit to the aircraft, and operate the operation unit according to the timing, accurate direction, required accuracy and duration to complete the established operation tasks; the operation system includes: operation unit controller, 2-degree redundant door drive system, launch device and operation unit; Except for the passive detection antenna subsystem which is a voting system, the rest of the redundancy systems are simplified to parallel redundant systems, and different subsystems are in series relationship. Step 2: Calculate the unit-level failure rate, where the unit level is the smallest component of the system; Step 2.1: According to the temperature, load and vibration of the drone, the environmental impact level is divided into three levels, namely "good", "medium" and "bad", and it is assumed that the environmental impact level of different units at the same time is the same; set thresholds for different environmental factors, obtain the impact level value of each environmental factor, and then calculate the weighted average to obtain the overall environmental impact level; Step 2.2: Calculate the unit-level failure rate. Assume that the unit-level failure satisfies the exponential distribution. The unit-level failure situation is described in the form of the interval between serious failures. The failure rate and the interval between serious failures are inversely proportional. The failure rate calculation formula is: Wherein, the subscript i represents the environmental level, and its values ​​are 1, 2, and 3, which represent the environmental impact levels of "good", "medium", and "bad" respectively; i Represents the unit failure rate corresponding to the environmental level i; MTBCF i represents the serious failure interval corresponding to the environmental level i; the unit reliability function that satisfies the exponential distribution is: Where t represents the sampling interval, R i Represents the task reliability corresponding to the environment level i; Step 2.3: Unit-level state simulation sampling. The reliability of each unit task satisfies the exponential distribution. Assuming that the probability of normal operation of the unit is reliability R, the probability of unit failure is 1-R. The unit state is randomly sampled to obtain the 0 and 1 values ​​of the unit state. 0 represents unit failure and 1 represents normal operation of the unit. Assume that all units are non-repairable components, and each unit state has a storage function. The unit state sampled at the current moment is stored, and the unit state at the previous moment needs to be considered at the next sampling moment. For the faulty unit, the state sampling is no longer performed, and the fault is directly returned; Step 3: Calculate the system-level failure rate; Step 3.1: Draw a system reliability block diagram based on the system's working logic. The parallel redundancy of the units is connected in parallel, and different units are connected in series. Step 3.2: Based on the unit-level state sampling results of step 2.3, the system-level state sampling results are obtained through the logical relationship of the system reliability block diagram. The relationship between the system-level state simulation sampling and the unit-level state simulation sampling is as follows: S=f(x1,x2,…,x n ) Among them, S represents the system state; x i represents the unit state; f is a logical function determined by the reliability block diagram of the system; Step 4: Divide task-level modules according to task requirements and define task success criteria; Step 4.1: Divide the UAV into: platform and communication layer, mission load layer; the platform and communication layer is the basis for the successful execution of the UAV mission, and is composed of systems that affect the normal flight and link communication of the aircraft, including: engine, flight control system, hydraulic system and information communication system; the mission load layer includes: operation system, detection system; when executing a mission, the specific mission load layer will be called, and different UAVs can carry different mission loads; Step 4.2: Divide the mission phases. The mission phases are divided into take-off, landing, cruising and operation. All mission phases will use the airframe platform, and different mission phases use different mission payloads. Different mission phases have different environmental levels. The environmental level of each mission phase is determined according to the method in step 2.

1. Step 4.3: Determine the criteria for mission success. The criteria for mission success are based on the normal operation of the platform and communication layer, and the mission payload to be used is selected according to the characteristics of the mission stage; Step 5: Calculate task reliability using the Monte Carlo method; Step 5.1: Set constants, including determining the unit-level failure rate according to step 2.2, building the system-level reliability block diagram according to step 3.1, defining the method task-level modules and task stages according to step 4, setting the simulation sampling interval t0, and the total task duration is expressed as follows: Among them, T total represents the total duration of the mission, H represents the mission profile contains H stages, h represents one of the stages, T h represents the task time of stage h; Step 5.2: Set the initial variables, let n = 0, Ns = 0, where n represents the number of simulations performed and Ns represents the number of successful task executions; Step 5.3: Initialize the simulation time t, set t = 0, clear the stored system state; set h = 1, start the simulation from the first task stage; Step 5.4: For the current task stage h, determine the task stage success criteria and the task-level modules to be used; Step 5.5: Using the Monte Carlo failure link, perform a single state sampling of the UAV system required for the mission and store the sampled state; Step 5.6: Determine whether the sampling status can meet the requirements of the task stage. If it can, go to step 5.7; if not, go to step 5.10; Step 5.7: Determine whether the task of this stage is completed. When t≥T h , indicating that the calculation of this task stage is completed, let h=h+1, and go to step 5.8; when t<T h , then let t = t + t0 and go to step 5.5; Step 5.8: Determine whether all task stages have been completed. total , then all task stages are completed and go to step 5.9; when t<T total , then it is necessary to enter the next task stage to continue the calculation, set t = t + t0, h = h + 1, and enter step 5.4; Step 5.9: Let the number of successful executions of the task Ns = Ns + 1; Step 5.10: Determine whether the number of calculations has reached the requirement. When n≥N, the calculation ends and the task reliability is calculated as R=Ns / N, where N is the total number of simulations. If n<N, set n=n+1 and go to step 5.

3.

2. A Monte Carlo-based high-performance UAV mission reliability simulation method as claimed in claim 1, characterized in that: The specific method of step 2.1 is: The relationship between failure rate and temperature presents a U-shaped curve, which is relatively flat within the normal operating temperature range, but will significantly increase the failure rate when the temperature is too high or too low; the environmental grade scores for setting thresholds to divide the temperature are as follows: Among them, T score is the environmental grade score of temperature, with values ​​of 3, 2, and 1, corresponding to the temperature grades of "good", "medium", and "bad" respectively; T l_min and T l_max In the temperature threshold set for low temperature environment, T h_min and T h_max The temperature thresholds set in a high temperature environment, their magnitude relationships are as follows: T l_min <T l_max <T h_min <T h_max ; The relationship between the failure rate and the load is positively correlated. The greater the load, the higher the failure rate. The environmental grade scores for setting the threshold to divide the load are as follows: Among them, L score V is the environmental grade score of the load, with values ​​of 3, 2, and 1, corresponding to the load grades of "good", "medium", and "bad" respectively; l and V h The load thresholds, their magnitude relationships are as follows: L l <L h ; The relationship between failure rate and vibration is positively correlated. The greater the vibration, the higher the failure rate. The environmental grade scores for vibration are divided by setting the threshold as follows: Among them, V score V is the vibration environment level score, with values ​​of 3, 2, and 1, corresponding to the vibration levels of "good", "medium", and "bad" respectively; l and V h The vibration thresholds are related as follows: V l <V h ; Then, the weighted average is calculated to obtain the overall environmental grade score, which is calculated as follows: Among them, FS is the overall environmental grade score, T score is the environmental grade score of temperature, L score is the environmental grade score of the load, V score is the environmental grade score of vibration; w1, w2, w3 are T score , L score , V score The weight of Finally, the overall environmental grade is obtained according to the overall environmental grade score FS. The specific standards are as follows: Where F represents the overall environmental level, with values ​​of "good", "medium" and "bad"; FS is the overall environmental level score.

3. A Monte Carlo-based high-performance UAV mission reliability simulation method as claimed in claim 1, characterized in that: According to the method in step 3.2, the relationship between the active detection system state simulation sampling and the unit level state simulation sampling is as follows: S detection =x 信号处理子系统 ∧x 电源子系统 ∧(x 天线1 ∨x 天线2 ) Among them, S detection is the state sampling result of the active detection system, x 信号处理子系统 and x 电源子系统 Represent the state sampling results of the signal processing subsystem and the power subsystem, respectively, 天线1 and x 天线2 Represents the state sampling results of the two redundancy units of the antenna subsystem.

4. A Monte Carlo-based high-performance UAV mission reliability simulation method as claimed in claim 1, characterized in that: The criteria for successful execution of the task in step 4.3 are as follows: Among them, M represents the success of the mission, P represents the normal operation of the platform and communication layer, K represents the normal operation of the mission payload, n represents the number of mission payloads used in the mission phase, and K i|P It means that the i-th task load can work normally only when the platform and communication layer P are available.

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