A Monte Carlo-based simulation method for high-performance UAV mission reliability

Abstract

This invention discloses a high-performance Monte Carlo-based method for simulating the reliability of unmanned aerial vehicle (UAV) missions, belonging to the research field of UAV mission reliability simulation. The invention divides the UAV system into unit, system, and mission levels, encapsulating each level of the system as a separate simulation sampling function, which is then integrated into the overall aircraft reliability simulation. Furthermore, during the simulation of the mission profile, the invention proposes simulation sampling at time intervals and recording the simulation sampling status. The simulation sampling at the next moment must take into account the status at the previous moment.

Description

Technical Field

[0001] The patent of this invention 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 impacts the mode and scale of the mission, as well as its ability to sustain mission execution, and thus has a direct impact on the improvement and performance of the mission. Current PMS reliability analysis methods can be broadly divided into two categories: analytical models and simulation methods. Analytical models can only solve small or medium-scale problems. For example, the Markov method has high memory usage and long computational time, making it suitable only for small-scale problems. The PMS-BDD and PMS-MMDD models offer some approximation and can handle medium-scale problems. Among simulation methods, the most commonly used is the Monte Carlo simulation algorithm, which, while computationally demanding, can handle large problems.

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

[0004] To address the shortcomings of the prior art, this invention divides the UAV system into unit, system, and mission levels. Each level is encapsulated as a separate simulation sampling function, which is then integrated into the overall aircraft reliability simulation. Furthermore, during the simulation of the mission profile, this invention proposes simulation sampling at time intervals and records the simulation sampling status. The next simulation sampling step must take into account the previous state.

[0005] Aiming at the complex system composition in high-performance UAV mission reliability simulation, the present invention proposes a high-performance UAV mission reliability simulation method based on Monte Carlo, 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 airframe platform layer includes subsystems that affect the normal flight of the aircraft, including: 2-redundant hydraulic system, 2-redundant flight control system, 3-redundant fuel tank, 2-redundant fuel pump, single or twin engine system, 2-redundant power supply system and landing gear system.

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

[0009] The perception system is a phased array radar, including: active detection system and passive detection system; the active detection system consists of an information processing subsystem, a power subsystem and a dual-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 with a total of 5 antenna units. The normal operation of the passive detection antenna subsystem requires the normal operation of 4 or more units.

[0010] The operating system is used to connect, transport, launch, and deploy the operating unit to the aircraft, and to operate the operating unit according to the timing, accurate direction, required accuracy, and duration to complete the established operating tasks. The operating system includes: operating unit controller, dual-redundant door drive system, launch device, and operating 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, with different subsystems 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: Based on the temperature, load, and vibration effects on the drone, the environmental impact level is divided into three levels: "good", "medium", and "bad", respectively. It is assumed that different units are subject to the same environmental impact level at the same time. Thresholds are set for different environmental factors to obtain the impact level of each environmental factor. The weighted average is then calculated to obtain the overall environmental impact level.

[0014] Step 2.2: Calculate the unit-level failure rate. Assume that the unit-level failures follow an exponential distribution and describe the unit-level failure conditions in terms of the time between serious failures. The failure rate and the time 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, respectively representing the environmental impact levels of “good”, “medium”, and “bad”; i Represents the unit failure rate corresponding to the environmental level i; MTBCF i represents the time between serious failures when the environmental level is 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 unit state values ​​0 and 1, where 0 represents unit failure and 1 represents normal operation.

[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. The unit state at the next sampling moment needs to consider the unit state at the previous 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 operating logic. Parallel redundancy of units is achieved by connecting in parallel, while 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 logic 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 the platform and communication layer and the mission payload layer. The platform and communication layer is the foundation for the UAV to successfully execute missions. It is composed of systems that affect the aircraft's normal flight and link communication, including the engine, flight control system, hydraulic system, and information and communication system. The mission payload layer includes the operation system and detection system. When executing a mission, the specific mission payload layer is called, and different UAVs can carry different mission payloads.

[0028] Step 4.2: Divide the mission phases. The mission phases are divided into takeoff, landing, cruise, 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. Determine the environmental level for each mission phase according to the method in step 2.1.

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

[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, and setting the simulation sampling interval t0. 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 as 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, and 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: Use the Monte Carlo failure link to sample the state 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 phase 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 go to step 5.4;

[0041] Step 5.9: Let the number of successful task executions 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 R = Ns / N is calculated, where N is the total number of simulations. If n < N, set n = n + 1 and proceed to step 5.3.

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

[0044] The relationship between failure rate and temperature shows a U-shaped curve, which is relatively flat within the normal operating temperature range, but will increase significantly 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 for high temperature environments have the following relationship:

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

[0048] The relationship between failure rate and load is positively correlated. The greater the load, the higher the failure rate. The environmental grade scores for setting thresholds 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 into the following thresholds:

[0053]

[0054] Among them, V score V is the environmental grade score 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] Furthermore, 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:

[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, x 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 represents the number of mission payloads used in the mission phase. i|P It means that the i-th task payload can only work normally under the premise that the platform and communication layer P are available.

[0068] The present invention establishes a Monte Carlo-based single-machine mission reliability assessment 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 figure is a flow chart of a specific embodiment of the method of the present invention.

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

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

[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.

[0075] Figure 7 This is the task reliability simulation flow chart. DETAILED DESCRIPTION

[0076] 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:

[0077] Table 1 Single redundancy MTBCF of each subsystem of the platform system

[0078]

[0079] Table 2 Single redundancy MTBCF of each submodule of the information communication system

[0080]

[0081]

[0082] Table 3 Single redundancy MTBCF of each subsystem of the perception system

[0083]

[0084] Table 4 Single redundancy MTBCF of each submodule of the operating system

[0085]

[0086] Table 5 Mission profile definition

[0087] 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

[0088] According to the simulation method defined in the present invention, the UAV mission reliability simulation result is R=0.945833, and the reliability data range is consistent with the actual engineering situation.

[0089] 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:

[0090] Assume that a certain type of drone 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:

[0091] Engine status classification: 1 engine idling, 2 engine cruising, 3 engine maximum, 4 engine failure.

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

[0093] The status of the No. 2 oil supply pump is divided into: 1 intact, 2 faulty.

[0094] Fuel tank status classification: 1 intact, 2 faulty.

[0095] The mission profile is defined as follows: the mission is divided into three phases: takeoff 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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 patented method.

[0101] Table 6 Reliability comparison between PMS-MMDD method and simulation method

[0102]

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 airframe platform layer includes subsystems that affect the normal flight of the aircraft, including: It consists of a 2-redundant hydraulic system, a 2-redundant flight control system, a 3-redundant fuel tank, a 2-redundant fuel pump, a single or twin engine system, a 2-redundant power supply system and a landing gear system. The information communication system realizes the communication between the aircraft and the ground, and between aircraft, and is composed of a low-frequency rack, a dual-redundant antenna device, and a transceiver; The perception system is a phased array radar, including: active detection system and passive detection system; the active detection system consists of information processing subsystem, power subsystem and dual-redundant active detection antenna subsystem; the passive detection system consists of information processing subsystem, power subsystem and passive detection antenna subsystem, of which the passive detection antenna subsystem is a voting system with a total of 5 antenna units. The normal operation of the passive detection antenna subsystem requires the normal operation of more than 4 units. The operating system is used to connect, transport, launch, and deploy the operating unit to the aircraft, and to operate the operating unit with accurate timing, direction, required accuracy, and duration to complete the specified operating tasks. The operating system includes: operating unit controller, dual-redundant door drive system, launch device, and operating 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, with different subsystems 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: Based on the temperature, load, and vibration effects on the drone, the environmental impact level is divided into three levels: "good", "medium", and "bad", assuming that different units are subject to the same environmental impact level at the same time. Thresholds are set for different environmental factors to obtain the impact level of each environmental factor, and then a weighted average is calculated to obtain the overall environmental impact level. Step 2.2: Calculate the unit-level failure rate. Assume that the unit-level failures follow an exponential distribution and describe the unit-level failure conditions in terms of the time between serious failures. The failure rate and the time 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, respectively representing the environmental impact levels of "good", "medium", and "bad"; i Represents the unit failure rate corresponding to the environmental level i; MTBCF i represents the time between serious failures when the environmental level is 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 unit state values ​​0 and 1, where 0 represents unit failure and 1 represents normal operation. 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. The unit state at the next sampling moment needs to consider the unit state at the previous 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 operating logic. Parallel redundancy of units is achieved by connecting in parallel, while 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 logic function determined by the reliability block diagram of the system; Step 4: Divide the task-level modules according to task requirements and define the task success criteria; Step 4.1: Divide the UAV into the platform and communication layer and the mission payload layer. The platform and communication layer is the foundation for the UAV to successfully execute missions. It is composed of systems that affect the aircraft's normal flight and link communication, including the engine, flight control system, hydraulic system, and information and communication system. The mission payload layer includes the operation system and detection system. When executing a mission, the specific mission payload layer is called, and different UAVs can carry different mission payloads. Step 4.2: Divide the mission phases. The mission phases are divided into takeoff, landing, cruise, 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. Determine the environmental level for each mission phase according to the method in step 2.

1. Step 4.3: Determine the mission success criteria. The mission success criteria are based on the normal operation of the platform and communication layer, and the mission payload to be used is selected based on the characteristics of the mission phase. 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, and setting the simulation sampling interval t0. 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 as 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, and 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: Use the Monte Carlo failure link to sample the state 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 phase 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 go to step 5.4; Step 5.9: Let the number of successful task executions 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 R = Ns / N is calculated, where N is the total number of simulations. If n < N, set n = n + 1 and proceed to step 5.

3.

2. A Monte Carlo-based high-performance UAV mission reliability simulation method according to claim 1, characterized in that: The specific method of step 2.1 is: The relationship between failure rate and temperature shows a U-shaped curve, which is relatively flat within the normal operating temperature range, but will increase significantly 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 for high temperature environments have the following relationship: T l_min <T l_max <T h_min <T h_max ; The relationship between failure rate and load is positively correlated. The greater the load, the higher the failure rate. The environmental grade scores for setting thresholds to divide the load are as follows: Among them, L score L 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 L 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 into the following thresholds: Among them, V score V is the vibration environment grade score, 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: 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 grade, with values ​​of "good", "medium" and "bad"; FS is the overall environmental grade score.

3. The Monte Carlo-based high-performance UAV mission reliability simulation method according to 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, x 天线1 and x 天线2 Represents the state sampling results of the two redundancy units of the antenna subsystem.

4. The Monte Carlo-based high-performance UAV mission reliability simulation method according to 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 represents the number of mission payloads used in the mission phase. i|P It means that the i-th task payload can only work normally under the premise that the platform and communication layer P are available.

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