A method for determining the polymorphic availability of unmanned aerial vehicles

Abstract

This invention provides a method for determining the polymorphic availability of unmanned aerial vehicles (UAVs). This method comprehensively considers the available, degraded, and faulty states of UAVs, identifies the primary influencing factors and other related states for each of these three states, and constructs a polymorphic availability model for the UAV using a generalized stochastic Petri net. This model is then solved using a Markov chain to determine the probabilities of the UAV's available, degraded, and faulty states. The method also analyzes the relationship between combinations of different fault detection rates and spare parts availability rates and the UAV's available, degraded, and faulty states. This method can be further applied to equipment such as unmanned vehicles and unmanned boats, providing important professional technical support for their development, construction, and operational support.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicle (UAV) availability and relates to a method for determining the polymorphic availability of a UAV. Background Art

[0002] Drone availability refers to the ratio of the time a drone can work to the sum of the time it can work and the time it cannot work. This indicator describes the drone's availability rate. Based on engineering practice, drone status can be divided into available state, degraded state, and faulty state. Factors related to drone availability include: average time from normal state to degraded state, average time from normal state to faulty state, average time from degraded state to faulty state, average fault testing time, average fault isolation time, average supply response time, average repair time with sufficient spare parts, average repair time with insufficient spare parts, average whole-unit replacement time, fault detection rate, fault missed detection rate, spare parts guarantee rate, spare parts shortage rate, etc.

[0003] Current UAV research mainly focuses on UAV reliability and unmanned swarm behavior, as well as scheduling management, communication links, collaborative control and other performance. The relationship between the UAV operating status and the fault recovery process has not yet been established. Summary of the Invention

[0004] The technical problem solved by the present invention is: to overcome the shortcomings of the existing technology, provide a method for determining the polymorphic availability of drones, construct an integrated drone multi-state model, including available state, degraded state, fault state, and fault recovery process model, and carry out multi-state fluctuation analysis of drones under different combinations of failure rates and spare parts guarantee rates.

[0005] The technical solution of the present invention is: a method for determining the polymorphic availability of a drone, comprising the following steps:

[0006] Step 1: According to the actual operation of the drone, the drone status is divided into available status P normal (t), reduced-order state P degrade (t), fault status P down (t), determine the main influencing factors and other related states of the three types of states; the main influencing factors include: the average normal state to degraded state time T notde , Average time from normal state to fault state T notdo , average degradation state to fault state time T detdo , mean time to failure test T mtt , mean fault isolation time T ir , average supply response time T msrt , Average repair time T with sufficient spare parts smttr , the average repair time T when spare parts are insufficient nsmttr, Average machine replacement time T mrtt , fault detection rate f fd , Fault missed detection rate f nfd , spare parts guarantee rate f ssr , Spare parts shortage rate f nssr ; The other related states include: test state P mtt (t), fault detection state P fd (t), fault missed detection state P nfd (t), fault isolation state P ir (t), Spare parts sufficient status P ssr (t), spare parts shortage status P nssr (t), average supply response state P msrt (t), where t is the time variable;

[0007] Step 2: Based on the three types of states, main influencing factors and other related states determined in step 1, a multi-state availability model of the UAV is constructed using a generalized stochastic Petri net to determine the stable state in the multi-state availability model; the differential equation of the multi-state availability model of the UAV is determined by the isomorphic Markov chain state transition process, and then the available state P of the UAV is determined. normal The transient solution A of (t) normal (t), steady-state solution A normal (∞), reduced-order state P degrade The transient solution A of (t) degrade (t), steady-state solution A degrade (∞), fault state P down The transient solution A of (t) down (t), steady-state solution A down (∞);

[0008] Step 3: Use the differential equation of the multi-state availability model established in step 2 to solve different fault detection rates f fd , spare parts guarantee rate f ssr Available states P under their combination normal The steady-state solution A of (t) normal (∞), reduced-order state P degrade The steady-state solution A of (t) degrade (∞), fault state P down The steady-state solution A of (t) down (∞).

[0009] Furthermore, the polymorphic availability model of the drone constructed by the generalized random Petri net in step 2 includes:

[0010] UAV available status content, including: available status P normal (t), average time from normal state to degraded state Tnotde , Average time from normal state to fault state T notdo ;

[0011] The contents of the drone's downgraded status include: downgraded status P degrade (t), average time from degradation state to fault state T detdo ;

[0012] UAV fault status content, including: fault status P down (t), test state P mtt (t), fault detection state P fd (t), fault missed detection state P nfd (t), fault isolation state P ir (t), Spare parts sufficient status P ssr (t), spare parts shortage status P nssr (t), average supply response state P msrt (t), mean fault test time T mtt , mean fault isolation time T ir , average supply response time T msrt , Average repair time T with sufficient spare parts smttr , the average repair time T when spare parts are insufficient nsmttr , Average machine replacement time T mrtt , fault detection rate f fd , Fault missed detection rate f nfd , spare parts guarantee rate f ssr , Spare parts shortage rate f nssr ;

[0013] The method of constructing the multi-state availability model of UAV is as follows: the initial state of the UAV is the available state P normal (t), the initial state passes through T notde Entering the degraded state P degrade (t), the initial state passes through T notdo Entering fault state P down (t), reduced-order state P degrade (t) Elapsed time T detdo Entering fault state P down (t);

[0014] Entering fault state P down (t) later, the UAV passes T mtt Enter test state P mtt (t), test state P mtt (t) with the fault detection rate f fd The probability of entering the fault detection state P fd (t), fault detection state P fd (t) After Tir Entering the fault isolation state P ir (t), fault isolation status P ir (t) Spare parts support rate f ssr The probability of entering the spare parts sufficient state P ssr (t), spare parts sufficient status P ssr (t) After T smttr Enter the available state P normal (t); Fault isolation status P ir (t) Spare parts shortage rate f nssr The probability of entering the spare parts shortage state P nssr (t), spare parts shortage status P nssr (t) After T msrt Entering the average supply response state P msrt (t), average supply response state P msrt (t) After T nsmttr Enter the available state P normal (t);

[0015] Test status P mtt (t) Taking the fault missed detection rate f nfd The probability of entering the fault missed detection state P nfd (t), fault missed detection state P nfd (t) After T mrtt Enter the available state P normal (t).

[0016] Furthermore, the stable state in the polymorphic availability model described in step 2 is as follows:

[0017] Available status P normal (t), reduced-order state P degrade (t), fault status P down (t), fault detection state P fd (t), fault missed detection state P nfd (t), Spare parts sufficient status P ssr (t), spare parts shortage status P nssr (t), average supply response state P msrt (t).

[0018] Furthermore, the principles of the isomorphic Markov chain state transition process described in step 2 are as follows:

[0019] Step S1: Divide the place state in the UAV polymorphic availability model based on generalized stochastic Petri nets into a stable state and an unstable state, where a stable state refers to a directed arc outputted by a place pointing to a time transition, and an unstable state refers to a directed arc outputted by a place pointing to an instantaneous transition;

[0020] Step S2: All stable-state places are set to Markov states, and the states between the stable-state places are converted to Markov transitions. The transition value corresponding to the state transition between two stable-state places is called the transition probability. The transition probability is determined as follows: a) if only time transitions exist between the stable-state places, the transition probability is the failure rate corresponding to the time transition; b) if time transitions and instantaneous transitions exist between the stable-state places, the transition probability is the product of the failure rate corresponding to the time transition and the value corresponding to the instantaneous transition.

[0021] Furthermore, the available state P normal (t), reduced-order state P degrade (t), fault status P down The values ​​of (t) are all between 0 and 1.

[0022] Furthermore, the differential equation of the UAV multi-state availability model is:

[0023]

[0024] P normal (t)+P degrade (t)+P down (t)+P nfd (t)+P fd (t)+P ssr (t)+P nssr (t)+P msrt (t)=1 (2)

[0025] in: P normal (t), P degrade (t), P down (t), P nfd (t), P fd (t), P ssr (t), P nssr (t), P msrt The first derivative of (t);

[0026] Where:

[0027] λ notde is the average normal to degraded state failure rate, λ notde =1 / T notde ;

[0028] λ notdo is the average normal to fault state failure rate, λ notdo =1 / T notdo ;

[0029] λ detdo is the average degradation rate from the fault state to the fault state, λdetdo =1 / T detdo ;

[0030] λ mtt is the average failure test rate, λ mtt =1 / T mtt ;

[0031] λ ir is the average fault isolation rate, λ ir =1 / T ir ;

[0032] λ msrt is the average supply response rate, λ msrt =1 / T msrt ;

[0033] λ smttr is the average repair rate under sufficient spare parts conditions, λ smttr =1 / T smttr ;

[0034] λ nsmttr is the average repair rate under spare parts shortage, λ nsmttr =1 / T nsmttr ;

[0035] λ mrtt is the average replacement rate of the whole machine, λ mrtt =1 / T mrtt .

[0036] Furthermore, f fd +f nfd =1,f ssr +f nssr =1.

[0037] Furthermore, the available status P of the drone is determined in step 2. normal The transient solution A of (t) normal (t), steady-state solution A normal The method of (∞) is:

[0038] The Runge-Kutta method is used to solve formulas (1) and (2) to obtain the available state P of the UAV. normal The transient solution of (t) is A normal (t); When time t→∞, the available state P normal The transient solution A of (t) normal (t) becomes available state P normal The steady-state solution A of (t) normal (∞):

[0039]

[0040] Where:

[0041] A=λ detdo ·l mtt ·l mrtt ·l ir ·l smttr ·l msrt ·l nsmttr ,

[0042] B=λ notde ·l mtt ·l mrtt ·l ir ·l smttr ·l msrt ·l nsmttr ,

[0043] C=(λ notdo +λ notde )·l detdo ·l mrtt ·l ir ·l smttr ·l msrt ·l nsmttr ,

[0044] D=(λ notdo +λ notde )·l detdo ·l mtt ·l ir ·l smttr ·l msrt ·l nsmttr ·f nfd ,

[0045] E=(λ notdo +λ notde )·l detdo ·l mtt ·l mrtt ·l smttr ·l msrt ·l nsmttr ·f fd ,

[0046] F=(λ notdo +λ notde )·l detdo ·l mtt ·l mrtt ·l ir ·l msrt ·l nsmttr ·f fd ·f ssr ,

[0047] G=(λ notdo +λ notde )·l detdo ·l mtt·λ mrtt ·λ ir ·λ smttr ·λ nsmttr ·f fd ·f nssr ,

[0048] H=(λ notdo +λ notde )·λ detdo ·λ mtt ·λ mrtt ·λ ir ·λ smttr ·λ msrt ·f fd ·f nssr .

[0049] Furthermore, step 2 determines the reduced-order state P degrade The transient solution A of (t) degrade (t), steady-state solution A degrade The method of (∞) is:

[0050] The Runge-Kutta method is used to solve equations (1) and (2) to obtain the UAV reduced-order state P degrade The transient solution of (t) is A degrade (t); When time t→∞, the reduced-order state P degrade The transient solution A of (t) degrade (t) changes to the reduced-order state P degrade The steady-state solution A of (t) degrade (∞):

[0051]

[0052] Furthermore, step 2 determines the fault state P down The transient solution A of (t) down (t), steady-state solution A down The method of (∞) is:

[0053] The Runge-Kutta method is used to solve formulas (1) and (2) to obtain the UAV fault state P down The transient solution of (t) is A down (t); When time t→∞, the fault state P down The transient solution A of (t) down (t) changes to fault state P down The steady-state solution A of (t) down (∞):

[0054]

[0055] The advantages of the present invention compared with the prior art are:

[0056] (1) According to the actual operation of the drone, the present invention divides the drone status into: available state, degraded state, and fault state, and gives the transmission relationship between the above states.

[0057] (2) The present invention comprehensively considers factors such as the average time from normal state to degraded state, the average time from normal state to faulty state, the average time from degraded state to faulty state, the average fault testing time, the average fault isolation time, the average supply response time, the average repair time under sufficient spare parts, the average repair time under insufficient spare parts, the average whole machine replacement time, the fault detection rate, the fault missed detection rate, the spare parts guarantee rate, and the spare parts shortage rate of the drone, and uses the generalized random Petri net to construct a polymorphic availability model for the drone; and establishes a transmission relationship between the drone's available state probability, degraded state probability, fault state probability and the drone's fault detection rate and spare parts guarantee rate.

[0058] (3) This method and model can not only determine the polymorphic availability of UAVs, but can also be further extended to unmanned vehicles, unmanned boats and other equipment, providing important professional technical support for their development, construction and operation guarantee. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a schematic diagram of the implementation process of the method according to the embodiment of the present invention;

[0060] Figure 2 Schematic diagram of a UAV polymorphic availability model based on generalized stochastic Petri nets according to an embodiment of the present invention;

[0061] Figure 3 Schematic diagram of various stable state transitions in the UAV polymorphic availability model according to an embodiment of the present invention;

[0062] Figure 4 Schematic diagram of the probability of a drone's available state for each combination of fault detection rate and spare parts guarantee rate according to an embodiment of the present invention;

[0063] Figure 5 Schematic diagram of the probability of a UAV degraded state under the combination of fault detection rate and spare parts guarantee rate according to an embodiment of the present invention;

[0064] Figure 6 Schematic diagram of the probability of drone failure status for the combination of fault detection rate and spare parts guarantee rate according to an embodiment of the present invention. DETAILED DESCRIPTION

[0065] like Figure 1 As shown, the present invention provides a method for determining the polymorphic availability of a drone, the steps of which are as follows:

[0066] Step 1: According to the actual operation of the drone, the drone status is divided into available status P normal (t), reduced-order state Pdegrade (t), fault status P down (t), determine the main influencing factors and other related states of the three types of states; the main influencing factors include: the average normal state to degraded state time T notde , Average time from normal state to fault state T notdo , average degradation state to fault state time T detdo , mean time to failure test T mtt , mean fault isolation time T ir , average supply response time T msrt , Average repair time T with sufficient spare parts smttr , the average repair time T when spare parts are insufficient nsmttr , Average machine replacement time T mrtt , fault detection rate f fd , Fault missed detection rate f nfd , spare parts guarantee rate f ssr , Spare parts shortage rate f nssr ; The other related states include: test state P mtt (t), fault detection state P fd (t), fault missed detection state P nfd (t), fault isolation state P ir (t), Spare parts sufficient status P ssr (t), spare parts shortage status P nssr (t), average supply response state P msrt (t), where t is the time variable.

[0067] Among them, the available state P normal (t), reduced-order state P degrade (t), fault status P down The values ​​of (t) are all between 0 and 1.

[0068] Step 2: Based on the three types of states, main influencing factors and other related states determined in step 1, a multi-state availability model of the UAV is constructed using a generalized stochastic Petri net to determine the stable state in the multi-state availability model; the differential equation of the multi-state availability model of the UAV is determined by the isomorphic Markov chain state transition process, and then the available state P of the UAV is determined. normal The transient solution A of (t) normal (t), steady-state solution A normal (∞), reduced-order state P degrade The transient solution A of (t) degrade (t), steady-state solution A degrade (∞), fault state P down The transient solution A of (t) down (t), steady-state solution A down(∞).

[0069] The generalized random Petri net constructs a polymorphic availability model for drones, which includes:

[0070] UAV available status content, including: available status P normal (t), average time from normal state to degraded state T notde , Average time from normal state to fault state T notdo ;

[0071] The contents of the drone's downgraded status include: downgraded status P degrade (t), average time from degradation state to fault state T detdo ;

[0072] UAV fault status content, including: fault status P down (t), test state P mtt (t), fault detection state P fd (t), fault missed detection state P nfd (t), fault isolation state P ir (t), Spare parts sufficient status P ssr (t), spare parts shortage status P nssr (t), average supply response state P msrt (t), mean fault test time T mtt , mean fault isolation time T ir , average supply response time T msrt , Average repair time T with sufficient spare parts smttr , the average repair time T when spare parts are insufficient nsmttr , Average machine replacement time T mrtt , fault detection rate f fd , Fault missed detection rate f nfd , spare parts guarantee rate f ssr , Spare parts shortage rate f nssr .

[0073] like Figure 2 As shown in the figure, the method of constructing the multi-state availability model of the UAV is as follows: the initial state of the UAV is the available state P normal (t), the initial state passes through T notde Entering the degraded state P degrade (t), the initial state passes through T notdo Entering fault state P down (t), reduced-order state P degrade (t) Elapsed time T detdo Entering fault state P down (t);

[0074] Entering fault state Pdown (t) later, the UAV passes T mtt Enter test state P mtt (t), test state P mtt (t) with the fault detection rate f fd The probability of entering the fault detection state P fd (t), fault detection state P fd (t) After T ir Entering the fault isolation state P ir (t), fault isolation status P ir (t) Spare parts support rate f ssr The probability of entering the spare parts sufficient state P ssr (t), spare parts sufficient status P ssr (t) After T smttr Enter the available state P normal (t); Fault isolation status P ir (t) Spare parts shortage rate f nssr The probability of entering the spare parts shortage state P nssr (t), spare parts shortage status P nssr (t) After T msrt Entering the average supply response state P msrt (t), average supply response state P msrt (t) After T nsmttr Enter the available state P normal (t);

[0075] Test status P mtt (t) Taking the fault missed detection rate f nfd The probability of entering the fault missed detection state P nfd (t), fault missed detection state P nfd (t) After T mrtt Enter the available state P normal (t).

[0076] In this embodiment, the stable state in the polymorphic availability model is determined as follows:

[0077] Available status P normal (t), reduced-order state P degrade (t), fault status P down (t), fault detection state P fd (t), fault missed detection state P nfd (t), Spare parts sufficient status P ssr (t), spare parts shortage status P nssr (t), average supply response state P msrt (t).

[0078] Furthermore, the principles of the isomorphic Markov chain state transition process are as follows:

[0079] Step S1: Divide the place state in the UAV polymorphic availability model based on generalized stochastic Petri nets into a stable state and an unstable state, wherein: the stable state refers to the directed arc output by the place pointing to the time transition, and the unstable state refers to the directed arc output by the place pointing to the instantaneous transition.

[0080] Step S2: All stable-state places are set to Markov states, and the states between the stable-state places are converted to Markov transitions. The transition value corresponding to the state transition between two stable-state places is called the transition probability. The transition probability is determined as follows: a) if only time transitions exist between the stable-state places, the transition probability is the failure rate corresponding to the time transition; b) if time transitions and instantaneous transitions exist between the stable-state places, the transition probability is the product of the failure rate corresponding to the time transition and the value corresponding to the instantaneous transition.

[0081] The differential equation for determining the multi-state availability model of drones is:

[0082]

[0083] P normal (t)+P degrade (t)+P down (t)+P nfd (t)+P fd (t)+P ssr (t)+P nssr (t)+P msrt (t)=1 (2)

[0084] in: P normal (t), P degrade (t), P down (t), P nfd (t), P fd (t), P ssr (t), P nssr (t), P msrt The first derivative of (t);

[0085] Where:

[0086] λ notde is the average normal to degraded state failure rate, λ notde =1 / T notde ;

[0087] λ notdo is the average normal to fault state failure rate, λ notdo =1 / T notdo ;

[0088] λ detdo is the average degradation rate from the fault state to the fault state, λ detdo =1 / T detdo ;

[0089] λ mtt is the average failure test rate, λ mtt =1 / T mtt ;

[0090] λ ir is the average fault isolation rate, λ ir =1 / T ir ;

[0091] λ msrt is the average supply response rate, λ msrt =1 / T msrt ;

[0092] λ smttr is the average repair rate under sufficient spare parts conditions, λ smttr =1 / T smttr ;

[0093] λ nsmttr is the average repair rate under spare parts shortage, λ nsmttr =1 / T nsmttr ;

[0094] λ mrtt is the average replacement rate of the whole machine, λ mrtt =1 / T mrtt ;

[0095] f fd +f nfd =1,f ssr +f nssr =1.

[0096] Then, determine the available state P of the drone normal The transient solution A of (t) normal (t), steady-state solution A normal (∞), reduced-order state P degrade The transient solution A of (t) degrade (t), steady-state solution A degrade (∞), fault state P down The transient solution A of (t) down (t), steady-state solution A down The method of (∞) is:

[0097] The Runge-Kutta method is used to solve formulas (1) and (2) to obtain the available state P of the UAV. normal The transient solution of (t) is A normal(t); when time t → ∞, the available state P normal The transient solution A of normal (t) becomes the available state P normal The steady-state solution A of normal (∞):

[0098]

[0099] Where:

[0100] A = λ detdo ·λ mtt ·λ mrtt ·λ ir ·λ smttr ·λ msrt ·λ nsmttr ,

[0101] B = λ notde ·λ mtt ·λ mrtt ·λ ir ·λ smttr ·λ msrt ·λ nsmttr ,

[0102] C = (λ notdo + λ notde )·λ detdo ·λ mrtt ·λ ir ·λ smttr ·λ msrt ·λ nsmttr ,

[0103] D = (λ notdo + λ notde )·λ detdo ·λ mtt ·λ ir ·λ smttr ·λ msrt ·λ nsmttr ·f nfd ,

[0104] E = (λ notdo + λ notde )·λ detdo ·λ mtt ·λ mrtt ·λ smttr ·λ msrt ·λ nsmttr ·f fd ,

[0105] F = (λ notdo + λ notde )·λ detdo ·λ mtt·λ mrtt ·λ ir ·λ msrt ·λ nsmttr ·f fd ·f ssr ,

[0106] G=(λ notdo +λ notde )·λ detdo ·λ mtt ·λ mrtt ·λ ir ·λ smttr ·λ nsmttr ·f fd ·f nssr ,

[0107] H=(λ notdo +λ notde )·λ detdo ·λ mtt ·λ mrtt ·λ ir ·λ smttr ·λ msrt ·f fd ·f nssr .

[0108] The Runge-Kutta method is used to solve equations (1) and (2) to obtain the UAV reduced-order state P degrade The transient solution of (t) is A degrade (t); When time t→∞, the reduced-order state P degrade The transient solution A of (t) degrade (t) changes to the reduced-order state P degrade The steady-state solution A of (t) degrade (∞):

[0109]

[0110] The Runge-Kutta method is used to solve formulas (1) and (2) to obtain the UAV fault state P down The transient solution of (t) is A down (t); When time t→∞, the fault state P down The transient solution A of (t) down (t) changes to fault state P down The steady-state solution A of (t) down (∞):

[0111]

[0112] Step 3: Use the differential equation of the multi-state availability model established in step 2 to solve different fault detection rates f fd , Spare parts guarantee rate f ssrAvailable states P under their combination n o rmal The steady-state solution A of (t) n o rmal (∞), reduced-order state P degrade The steady-state solution A of (t) degrade (∞), fault state P down The steady-state solution A of (t) down (∞).

[0113] Example 1

[0114] (a) Determine the available state P of the drone based on its actual operation status normal (t), reduced-order state P degrade (t), fault status P down (t) Various major influencing factors and related states.

[0115] (b) According to the above-mentioned related states, a UAV multi-state availability model based on generalized random Petri nets is constructed, such as Figure 2 Determine the conversion relationship between various stable states of the drone, such as Figure 3 shown.

[0116] (c) According to the generalized random Petri net isomorphic Markov chain, the differential equation of the multi-state availability of the UAV is determined, and then the available state P of the UAV is determined. normal The transient solution A of (t) normal (t), its steady-state solution A normal (∞)=0.9188;

[0117] Drone downgrade status P degrade The transient solution A of (t) degrade (t), its steady-state solution A degrade (∞)=0.0574;

[0118] UAV fault status P down The transient solution A of (t) down (t), its steady-state solution A down (∞)=0.0003.

[0119] (d) Analyze the available state steady-state solution A of the UAV under different combinations of fault detection rate and spare parts guarantee rate normal (∞) Figure 4 As shown, the horizontal axis is the fault detection rate, and the vertical axis is the available state steady-state solution A normal (∞); Reduced-order steady-state solution A of the UAV degrade (∞) Figure 5 As shown, the horizontal axis is the fault detection rate, and the vertical axis is the reduced-order steady-state solution A degrade(∞); steady-state solution of the UAV’s fault state A down (∞) Figure 6 As shown, the horizontal axis is the fault detection rate, and the vertical axis is the fault state steady-state solution A down (∞).

[0120] Parts of the present invention that are not described in detail belong to common knowledge among those skilled in the art.

[0121] Although the present invention has been disclosed above in terms of preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art may make possible changes and modifications to the technical solutions of the present invention by using the methods and technical contents disclosed above without departing from the spirit and scope of the present invention. Therefore, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solutions of the present invention shall fall within the scope of protection of the technical solutions of the present invention.

Claims

1. A method for determining the polymorphic availability of a drone, characterized in that: The steps include: Step 1: According to the actual operation of the drone, the drone status is divided into available status P normal (t), reduced-order state P degrade (t), fault status P down (t), determine the main influencing factors and other related states of the three categories; The main influencing factors include: average normal state to degraded state time T notde , Average time from normal state to fault state T notdo , average degradation state to fault state time T detdo , mean time to failure test T mtt , mean fault isolation time T ir , average supply response time T msrt , Average repair time T with sufficient spare parts smttr , the average repair time T when spare parts are insufficient nsmttr , average machine replacement time T mrtt , fault detection rate f fd , fault missed detection rate f nfd , Spare parts guarantee rate f ssr , Spare parts shortage rate f nssr ; The other related states include: test state P mtt (t), fault detection state P fd (t), fault missed detection state P nfd (t), fault isolation state P ir (t), Spare parts sufficient status P ssr (t), spare parts shortage status P nssr (t), average supply response state P msrt (t), where t is the time variable; Step 2: Based on the three types of states, main influencing factors and other related states determined in step 1, a multi-state availability model of the UAV is constructed using a generalized stochastic Petri net to determine the stable state in the multi-state availability model; the differential equation of the multi-state availability model of the UAV is determined by the isomorphic Markov chain state transition process, and then the available state P of the UAV is determined. normal The transient solution A of (t) normal (t), steady-state solution A normal (∞), reduced-order state P degrade The transient solution A of (t) degrade (t), steady-state solution A degrade (∞), fault state P down The transient solution A of (t) down (t), steady-state solution A down (∞); Step 3: Use the differential equation of the multi-state availability model established in step 2 to solve different fault detection rates f fd , Spare parts guarantee rate f ssr Available states P under their combination normal The steady-state solution A of (t) normal (∞), reduced-order state P degrade The steady-state solution A of (t) degrade (∞), fault state P down The steady-state solution A of (t) down (∞); The differential equation of the UAV multi-state availability model is: P normal (t)+P degrade (t)+P down (t)+P nfd (t)+P fd (t)+P ssr (t)+P nssr (t)+P msrt (t)=1 (2) in: P normal (t), P degrade (t), P down (t), P nfd (t), P fd (t), P ssr (t), P nssr (t), P msrt The first derivative of (t); Where: λ notde is the average normal to degraded state failure rate, λ notde =1 / T notde ; λ notdo is the average normal to fault state failure rate, λ notdo =1 / T notdo ; λ detdo is the average degradation rate from the fault state to the fault state, λ detdo =1 / T detdo ; λ mtt is the average failure test rate, λ mtt =1 / T mtt ; λ ir is the average fault isolation rate, λ ir =1 / T ir ; λ msrt is the average supply response rate, λ msrt =1 / T msrt ; λ smttr is the average repair rate under sufficient spare parts conditions, λ smttr =1 / T smttr ; λ nsmttr is the average repair rate under spare parts shortage, λ nsmttr =1 / T nsmttr ; λ mrtt is the average replacement rate of the whole machine, λ mrtt =1 / T mrtt .

2. The method for determining the polymorphic availability of a drone according to claim 1, wherein: The polymorphic availability model of drones constructed using the generalized random Petri net described in step 2 includes: UAV available status content, including: available status P normal (t), average time from normal state to degraded state T notde , Average time from normal state to fault state T notdo ; The contents of the drone's downgraded status include: downgraded status P degrade (t), average time from degradation state to fault state T detdo ; UAV fault status content, including: fault status P down (t), test state P mtt (t), fault detection state P fd (t), fault missed detection state P nfd (t), fault isolation state P ir (t), Spare parts sufficient status P ssr (t), spare parts shortage status P nssr (t), average supply response state P msrt (t), mean fault test time T mtt , mean fault isolation time T ir , average supply response time T msrt , Average repair time T with sufficient spare parts smttr , the average repair time T when spare parts are insufficient nsmttr , average machine replacement time T mrtt , fault detection rate f fd , fault missed detection rate f nfd , Spare parts guarantee rate f ssr , Spare parts shortage rate f nssr ; The method of constructing the multi-state availability model of UAV is as follows: the initial state of the UAV is the available state P normal (t), the initial state passes through T notde Entering the degraded state P degrade (t), the initial state passes through T notdo Entering fault state P down (t), reduced-order state P degrade (t) Elapsed time T detdo Entering fault state P down (t); Entering fault state P down (t) later, the UAV passes T mtt Enter test state P mtt (t), test state P mtt (t) with the fault detection rate f fd The probability of entering the fault detection state P fd (t), fault detection state P fd (t) After T ir Entering the fault isolation state P ir (t), fault isolation status P ir (t) Spare parts support rate f ssr The probability of entering the spare parts sufficient state P ssr (t), spare parts sufficient status P ssr (t) After T smttr Enter the available state P normal (t); Fault isolation status P ir (t) Spare parts shortage rate f nssr The probability of entering the spare parts shortage state P nssr (t), spare parts shortage status P nssr (t) After T msrt Entering the average supply response state P msrt (t), average supply response state P msrt (t) After T nsmttr Enter the available state P normal (t); Test status P mtt (t) Taking the fault missed detection rate f nfd The probability of entering the fault missed detection state P nfd (t), fault missed detection state P nfd (t) After T mrtt Enter the available state P normal (t).

3. The method for determining the polymorphic availability of a drone according to claim 1, wherein: The stable state in the polymorphic availability model described in step 2 is as follows: Available status P normal (t), reduced-order state P degrade (t), fault status P down (t), fault detection state P fd (t), fault missed detection state P nfd (t), Spare parts sufficient status P ssr (t), spare parts shortage status P nssr (t), average supply response state P msrt (t).

4. The method for determining the polymorphic availability of a drone according to claim 3, wherein: The principles of the isomorphic Markov chain state transition process described in step 2 are as follows: Step S1: Divide the place state in the UAV polymorphic availability model based on generalized stochastic Petri nets into a stable state and an unstable state, where a stable state refers to a directed arc outputted by a place pointing to a time transition, and an unstable state refers to a directed arc outputted by a place pointing to an instantaneous transition; Step S2: All stable-state places are set to Markov states, and the states between the stable-state places are converted to Markov transitions. The transition value corresponding to the state transition between two stable-state places is called the transition probability. The transition probability is determined as follows: a) if only time transitions exist between the stable-state places, the transition probability is the failure rate corresponding to the time transition; b) if time transitions and instantaneous transitions exist between the stable-state places, the transition probability is the product of the failure rate corresponding to the time transition and the value corresponding to the instantaneous transition.

5. The method for determining the polymorphic availability of a drone according to claim 1, wherein: The available state P normal (t), reduced-order state P degrade (t), fault status P down The values ​​of (t) are all between 0 and 1.

6. The method for determining the polymorphic availability of a drone according to claim 1, wherein: f fd +f nfd =1,f ssr +f nssr =1。 7. The method for determining the polymorphic availability of a drone according to claim 1, wherein: Step 2: Determine the available status of the drone P normal The transient solution A of (t) normal (t), steady-state solution A normal The method of (∞) is: The Runge-Kutta method is used to solve formulas (1) and (2) to obtain the available state P of the UAV. normal The transient solution of (t) is A normal (t); When time t→∞, the available state P normal The transient solution A of (t) normal (t) becomes available state P normal The steady-state solution A of (t) normal (∞): Where: A=λ detdo ·l mtt ·l mrtt ·l ir ·l smttr ·l msrt ·l nsmttr , B=λ notde ·l mtt ·l mrtt ·l ir ·l smttr ·l msrt ·l nsmttr , C=(λ notdo +λ notde )·l detdo ·l mrtt ·l ir ·l smttr ·l msrt ·l nsmttr , D=(λ notdo +λ notde )·l detdo ·l mtt ·l ir ·l smttr ·l msrt ·l nsmttr ·f nfd , E=(λ notdo +λ notde )·l detdo ·l mtt ·l mrtt ·l smttr ·l msrt ·l nsmttr ·f fd , F=(λ notdo +λ notde )·l detdo ·l mtt ·l mrtt ·l ir ·l msrt ·l nsmttr ·f fd ·f ssr , G=(λ notdo +λ notde )·l detdo ·l mtt ·l mrtt ·l ir ·l smttr ·l nsmttr ·f fd ·f nssr , H=(λ notdo +λ notde )·l detdo ·l mtt ·l mrtt ·l ir ·l smttr ·l msrt ·f fd ·f nssr 。 8. The method for determining the polymorphic availability of a drone according to claim 7, wherein: Step 2: Determine the reduced-order state P degrade The transient solution A of (t) degrade (t), steady-state solution A degrade The method of (∞) is: The Runge-Kutta method is used to solve equations (1) and (2) to obtain the UAV reduced-order state P degrade The transient solution of (t) is A degrade (t); When time t→∞, the reduced-order state P degrade The transient solution A of (t) degrade (t) changes to the reduced-order state P degrade The steady-state solution A of (t) degrade (∞):

9. The method for determining the polymorphic availability of a drone according to claim 7, wherein: Step 2: Determine the fault status P down The transient solution A of (t) down (t), steady-state solution A down The method of (∞) is: The Runge-Kutta method is used to solve formulas (1) and (2) to obtain the UAV fault state P down The transient solution of (t) is A down (t); When time t→∞, the fault state P down The transient solution A of (t) down (t) changes to fault state P down The steady-state solution A of (t) down (∞):

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