CN112287591A - Ship formation grade repair plan compiling method based on expected system efficiency - Google Patents

Ship formation grade repair plan compiling method based on expected system efficiency Download PDF

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CN112287591A
CN112287591A CN202011129446.6A CN202011129446A CN112287591A CN 112287591 A CN112287591 A CN 112287591A CN 202011129446 A CN202011129446 A CN 202011129446A CN 112287591 A CN112287591 A CN 112287591A
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蒋铁军
周成杰
张怀强
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Naval University of Engineering PLA
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Abstract

The invention discloses a naval vessel formation grade repair plan compilation method based on expected system efficiency, which comprises the following steps: step 1: establishing a basic database of a vessel formation grade repair plan; step 2: establishing a ship formation expected system efficiency evaluation model; and step 3: establishing a ship formation grade repair plan optimization model by utilizing a ship formation expected system efficiency evaluation model, aiming at maximizing the expected system efficiency of the ship formation in a certain period, taking whether the ship is repaired and the repair starting time as decision variables, and considering the repair period constraint, the over-period repair cost constraint, the repair total cost constraint, the efficiency constraint and the aircraft rate constraint; and 4, step 4: and determining a ship formation grade repair plan. The invention can enhance the scientificity and effectiveness of the repair plan formulation of the ship formation level.

Description

Ship formation grade repair plan compiling method based on expected system efficiency
Technical Field
The invention relates to the technical field of ship maintenance support, in particular to a ship formation grade repair plan compilation method based on expected system efficiency.
Background
The vessel level repair plan is a preventive repair arrangement made according to vessel strength, relevant regulations and instructions, combined with combat readiness, training and other mission conditions. In the equipment maintenance and guarantee work, the scientific formulation of the plan of the naval vessel level repair is very important due to long repair period, high expense requirement, more consumed resources and great influence on the use. With the rapid development of naval equipment construction, a large number of novel naval vessels are intensively placed in service, China increasingly attaches importance to ocean safety, naval vessel training tasks are obviously increased, the use strength is increased, higher requirements are provided for the maintenance and recovery of naval vessel capacity, and particularly the requirement of considering grade repair arrangement from the formation perspective is more prominent through systematic application.
Currently, one of the basic modes of vessel level repair planning is: the repair timing is determined at a relatively fixed time interval range, while the repair level, the repair period, etc. are specified (control plan), and the repair range and the repair depth are set according to given budget indices (control expenditure), thereby scheduling the repair activities. Under the framework, different planning methods are proposed in consideration of different goals and effects, such as a maintenance interval determination method based on reliability and cost, a planning method aiming at risk minimization, and the like; in consideration of systematic application of ships, plan optimization methods with ship formation as an object are also proposed, such as an optimization method for reducing fluctuation range of the number of available ships in a formation service period, a repair structure determination method for improving formation deployment time, an optimization method based on deployment capabilities of multiple ships of the same type, and the like.
The existing method for compiling the ship formation grade repair plan has the following defects: firstly, the repair activities are optimized mainly from the perspectives of the rate, the integrity and the like, the repair arrangement of the ship formation is not considered from the perspective of the system efficiency, the relevance between the use and the repair of ships of different types and different models is not considered, and the requirement for the ship formation to execute the combat readiness training task is difficult to meet; secondly, the overall efficiency of the naval vessel is regarded as a constant value, and the objective condition that the efficiency of the naval vessel shows a decay trend along with use cannot be effectively adapted; thirdly, due to the lack of consideration of the efficiency of a ship formation system and the attenuation condition thereof, the influence of the system efficiency on the subjective expectation of a decision maker and the risk preference cannot be considered.
Disclosure of Invention
The invention aims to provide a ship formation rank repair plan compiling method based on expected system efficiency, the method is based on the existing ship rank repair mode, the systematic application of the ship formation, the efficiency attenuation characteristic of the ship and the expectation and preference of a decision maker are considered, an expected system efficiency evaluation model of the ship formation is constructed, the expected system efficiency maximization of the ship formation in a certain period is further targeted, whether the ship is repaired or not and the repair starting time are taken as decision variables, the constraint conditions of the repair period, the repair over-period cost, the repair total cost, the efficiency, the aircraft rate and the like are comprehensively considered, a ship formation rank repair plan optimization model is established, a mixed coding particle swarm algorithm is adopted to optimize and solve the model, and finally the ship formation rank repair plan arrangement is obtained. The method can provide powerful technical support for the scientific planned ship formation grade repair plan and the effective fulfillment of the mission task of the ship.
In order to achieve the purpose, the invention provides a method for compiling a vessel formation grade repair plan based on expected system efficiency, which comprises the following steps:
step 1: establishing a ship formation grade repair plan basic database, wherein the ship formation grade repair plan basic database comprises the initial efficiency of a ship at a decision starting time point and ship repair data parameters;
step 2: establishing a ship formation expectation system performance evaluation model according to the contribution degree of each subentry capacity in the initial performance to the overall performance, the attenuation characteristic of the ship performance over time, the expectation of each subentry capacity value of the ship formation mission task and the preference degree of a decision maker to risks;
and step 3: establishing a ship formation grade repair plan optimization model by utilizing a ship formation expected system efficiency evaluation model, aiming at maximizing the expected system efficiency of the ship formation in a certain period, taking whether the ship is repaired and the repair starting time as decision variables, and considering the repair period constraint, the over-period repair cost constraint, the repair total cost constraint, the efficiency constraint and the aircraft rate constraint;
and 4, step 4: and solving the optimization model of the ship formation grade repair plan by using a hybrid coding particle swarm algorithm to obtain whether the ships are repaired or not and the repair starting time, and further determining the ship formation grade repair plan.
The invention has the beneficial effects that:
according to the method, the influence of activities such as use, repair and the like of the naval vessel on the self state is reflected more truly and comprehensively by establishing a naval vessel efficiency attenuation model and considering attenuation characteristics existing objectively in naval vessel efficiency; by establishing a ship formation expectation system efficiency evaluation model and considering subjective expectation and risk preference of a ship repair plan compilation decision maker, the essential pursuit of ship use and repair activities on efficiency is better embodied, and the management practice of the current ship repair plan is better met; the inherent characteristic of attenuation of the efficiency of the ships, the basic expectation of the efficiency of the ship formation and various constraints of the repair plan arrangement are comprehensively considered, the ship formation grade repair plan is compiled and arranged with the maximum expectation system efficiency as a target, the defects of the traditional compiling method can be avoided, and the scientificity and the effectiveness of the ship formation grade repair plan formulation are enhanced.
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FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is an illustration of the initial performance and itemized performance values of a naval vessel according to the present invention;
FIG. 3 is a naval vessel repair data parameter of the present invention;
FIG. 4 is a naval vessel performance decay function of the present invention;
FIG. 5 is a vessel fleet level repair plan based on desired system performance in accordance with the present invention;
FIG. 6 is a ship formation itemized capacity value function based on the efficiency of the expected system in the present invention;
FIG. 7 is a state of availability of a fleet of vessels based on desired system performance in accordance with the present invention;
FIG. 8 is a vessel fleet level repair plan based on the conventional method in the present invention;
FIG. 9 is a value function of ship formation itemized capacity based on a conventional method in the present invention;
fig. 10 shows the availability of a fleet of vessels based on the conventional method in the present invention.
In FIG. 6, UX、UM、UF、UJAnd ULRespectively representing the value functions of command control capability, maneuvering capability, defense capability, attack capability and guarantee capability of the ship formation;
in FIG. 9, UX、UM、UFUJ and ULRespectively representing the value functions of command control capability, maneuvering capability, defense capability, attack capability and guarantee capability of the ship formation.
Detailed Description
The invention is described in further detail below with reference to the following figures and specific examples:
the invention relates to a ship formation grade repair planning method based on expected system efficiency, which comprises the following steps as shown in figure 1:
step 1, establishing a ship formation grade repair plan basic database according to the actual technical state of a ship and the current ship grade repair regulations, wherein the ship formation grade repair plan basic database comprises the initial efficiency of the ship at a decision starting time point, is used for describing the performance of the ship at the time point, and ship repair data parameters, and is used as basic data of a ship formation grade repair plan optimization model;
step 2: establishing a ship formation expectation system performance evaluation model according to the contribution degree of each subentry capacity in the initial performance to the overall performance, the attenuation characteristic of the ship performance over time, the expectation of each subentry capacity value of the ship formation mission task and the preference degree of a decision maker to risks;
and step 3: establishing a ship formation grade repair plan optimization model by utilizing a ship formation expected system efficiency evaluation model, aiming at maximizing the expected system efficiency of the ship formation in a certain period, taking whether the ship is repaired and the repair starting time as decision variables, and considering the repair period constraint, the over-period repair cost constraint, the repair total cost constraint, the efficiency constraint and the aircraft rate constraint;
and 4, step 4: and solving the optimization model of the ship formation grade repair plan by using a hybrid coding particle swarm algorithm to obtain whether the ships are repaired or not and the repair starting time, and further determining the ship formation grade repair plan.
In the step 1 of the technical scheme, the initial efficiency of the naval vessel at the decision starting time point comprises a command control capacity value, a mobility capacity value, a defense capacity value, an attack capacity value and a guarantee capacity value, wherein the capacity values represent the sizes of the subentry capacities and are obtained by comprehensively evaluating the technical state of equipment; the vessel repair data parameters comprise a vessel number, a repair level preliminarily drawn according to the current vessel level repair rule, a budget index, a repair period and recovery efficiency, the time length from the last level repair or the end of the service guarantee to the decision-making starting point, and a use efficiency lower limit determined according to the use requirement.
The specific method of the step 2 of the technical scheme is that firstly, a single naval vessel efficiency evaluation model is established according to the contribution degree of each subentry capacity in the initial efficiency to the overall efficiency;
the efficiency of the single naval vessel is divided into a command control ability value, a maneuvering ability value, a defense ability value, an attack ability value and a guarantee ability value, and the contribution degree of each subentry ability to the overall efficiency is considered to obtain the single naval vessel SiThe overall efficiency of (A) is:
Pi＝wc1Xi+wc2Mi+wc3Fi+wc4Ji+wc5Li
in the formula, PiIndicating vessels SiOverall efficiency of Xi、Mi、Fi、Ji、LiRespectively representing the command control capability value, the maneuvering capability value, the defense capability value, the attack capability value and the guarantee capability value of the naval vessel, wc1～wc5Representing fractional capability versus overall performance separatelyThe contribution weight is determined comprehensively by a planning builder according to the mission task of the naval vessel, relevant regulation instructions and basic-level army opinions;
then, aiming at the characteristic that the efficiency of the naval vessel attenuates along with time in the actual use process, a reverse logistic function is adopted to establish an attenuation model of the naval vessel efficiency;
Figure BDA0002734636340000051
in the formula, Pi(t) denotes a vessel SiPerformance function of PioRepresenting the initial performance of the vessel, i.e. the performance at the decision start time point, kiExpressing the efficiency decay rate, relating to the characteristics and use intensity of the naval vessel, t represents the time, aiExpressing the efficiency decay length, reflecting the time length of slow efficiency decay of the naval vessel at the initial use stage, relating to the inherent characteristics and use strength of the naval vessel, biThe time length from the completion of the last grade repair or the service guarantee of the naval vessel to the decision starting time point is represented, and e is a natural constant;
further based on the attenuation model of the efficiency of the naval vessel, the efficiency is recovered by considering the grade repair, and the naval vessel SiThe efficiency change conditions are as follows:
Figure BDA0002734636340000061
wherein, [0, T]Represents the start and end time, T, of the periodi sAnd Ti eRespectively represents the starting and stopping time point of repair, P'ioIndicating the efficacy, Δ P, of the vessel before repairiThe efficiency of the recovery after the repair is expressed, and the recovery is determined according to the budget index and the repair period by specifically referring to the historical repair situation;
establishing a ship formation system efficiency evaluation model, and obtaining the single capability value of the ship formation according to a ship efficiency attenuation model:
Figure BDA0002734636340000062
in the formula, CXRepresenting a certain individual capability value, w, of a fleet of vesselsiIndicating vessels SiThe weight in the formation of the ships is determined according to the importance degree of the ships and the troops and mission tasks of the ships in the formation, XiIndicating vessels SiThe initial singles ability value of (a);
further obtaining the efficiency of a naval vessel formation system:
C(t)＝wc1CX(t)+wc2CM(t)+wc3CF(t)+wc4CJ(t)+wc5CL(t)
wherein C (t) represents the efficiency of the ship formation system, CX(t)、CM(t)、CF(t)、CJ(t)、CL(t) command control ability value, maneuvering ability value, defense ability value, attack ability value and guarantee ability value of naval vessel formation, wc1～wc5Representing the contribution degree weight of the item dividing capacity to the overall efficiency;
establishing a ship formation expected system efficiency evaluation model, considering the expectation of each subentry capacity value when the ship formation fulfills mission tasks and the waste caused by unbalanced recovery of single capacity, and establishing a ship formation system efficiency value function by using a prospect theory;
Figure BDA0002734636340000063
in the formula of UX(t) a cost function representing the ability of a vessel to form a single item, CX(t) actual value representing the ability of the vessel to form the single item, EXExpected value, λ, representing the ability of a vessel to form the single itemiAnd beta i0 representing the risk aversion coefficients for actual values above and below expectations, respectively<λi<1<βi；
Further obtaining a naval vessel formation expected system efficiency evaluation model:
U(t)＝wc1UX(t)+wc2UM(t)+wc3UF(t)+wc4UJ(t)+wc5UL(t)
wherein U (t) represents the expected system efficiency of the ship formation, wc1～wc5Represents the weight of contribution of the subentry ability to the overall efficiency, UX(t)、UM(t)、UF(t)、UJ(t)、ULAnd (t) the value function of command control capability, maneuvering capability, defense capability, attack capability and guarantee capability of the ship formation is represented.
The specific method in step 3 of the above technical solution is that the optimization objective of the optimization model is to maximize the efficiency of the expected system of the naval vessel formation in a certain period T, and is expressed as:
Figure BDA0002734636340000071
wherein U (t) represents the expected system efficiency of the ship formation at the time t, and fUIndicating that the vessel formation is in time [0, T ]]Desired system efficacy;
the decision variables and the value ranges of the optimization models are as follows: for naval vessels SiWhether or not to repair xi，xi1 denotes repair, x i0 means no repair; and the starting time T of the repairi s，Ti s∈[0,T]；
The constraint conditions of the optimization model are as follows:
for repair time period constraints, according to current repair work regulations, for vessels SiComprises the following steps:
Ti e-Ti s＝ti
in the formula, Ti eIndicating the end of repair time, Ti sIndicating the repair start time, tiIndicating a well-defined repair period according to the regulations;
for the midspan repair cost constraint, when the vessel has a span of [0, T]Time period of repair, current period [0, T]The repair cost is the repair budget index andperiod [0, T]Product of repair time fraction, naval vessel SiThe interim repair expenses are:
Figure BDA0002734636340000072
in the formula, ciIndicates the current period [0, T]Cost of repair, CiRepresenting a repair budget indicator, t'iIndicates the current period [0, T]Repair time;
for the total cost constraint of repair, the total cost of the warship to be repaired is not more than the total cost budget index, which is specifically expressed as:
Figure BDA0002734636340000081
in the formula, xiWhen 0, it represents the vessel SiNo repair is needed; x is the number ofiWhen 1, it represents a vessel SiNeed to be repaired; c. CiIndicating vessels SiCurrent stage [0, T]Cost of repair, CzRepresenting a total budget expenditure indicator;
for the performance constraint, without modification, repairs result in only limited recovery of performance, and do not achieve the initial performance at the end of the last level repair or commissioning, when:
P′io+ΔPi≤Pio
in the formula, PioRepresenting the initial efficiency, P 'of the vessel'ioIndicating the efficacy, Δ P, of the vessel before repairiThe efficiency of the ship which can be recovered after the repair is shown;
for the enroute rate constraint, it is specifically expressed as:
Figure BDA0002734636340000082
wherein N represents the total number of vessels, NrIndicating the number of vessels in repair, NuRepresenting the number of vessels in a state of overhaul and r representing the specified minimum airspeed requirement.
In step 4 of the above technical scheme, the concrete steps of solving the ship formation level repair plan optimization model by using the hybrid coding particle swarm algorithm are as follows:
firstly, coding, considering that decision variables of a ship formation grade repair plan optimization model are whether ships are repaired or not and the repair starting time, wherein the decision variables have discrete variables and continuous variables, and a mode of sectionally and mixedly coding the two variables is adopted, specifically
Figure BDA0002734636340000083
Wherein, [ x ]1,x2,…,xn]Adopting binary coding (the former part of decision variable, representing whether the naval vessel is repaired or not), and taking 0 or 1, x1,x2,…,xnIndicating whether the 1 st to the nth vessels are repaired or not;
Figure BDA0002734636340000084
real number coding (the latter part of decision variable, which represents the initial time of ship repair) is adopted to represent the initial time of the repair of the 1 st to the nth ships, and the value range is [0, T]；
At this time, for a population consisting of N particles (a particle is a specific expression in the particle swarm algorithm, and the position of a particle represents a solution given according to a predetermined encoding rule), the current position of a particle m (m is 1,2, …, N) is represented as
Figure BDA0002734636340000091
xm1,xm2,...,xmnRepresents the decision variable [ x ] corresponding to the mth particle1,x2,…,xn]A set of solutions representing whether the vessel is repaired;
Figure BDA0002734636340000092
representing decision variables corresponding to the m-th particle
Figure BDA0002734636340000093
Represents the starting time of the vessel repair, the current flight speed (flight speed represents the grain)The trend of the change of the sub-position in the next step) is expressed as
Figure BDA0002734636340000094
Particle position is a multidimensional variable, the flight velocity is not the same for each dimension, and therefore the flight velocity is also multidimensional, and therefore
Figure BDA0002734636340000095
Respectively representing the corresponding particle positions
Figure BDA0002734636340000096
Flight speeds of different dimensions, PmThe position with the best adaptation value experienced by the particle m is represented, called the individual optimum position; pgThe position representing the optimal fitness value searched so far by all the particles in the whole particle swarm is called as a global optimal position;
initialising the particle population, i.e. randomly setting the initial position X of the particles mm(1) And an initial velocity Vm(1)；
Then, an objective function f in the ship formation grade repair plan optimization modelUDirectly serving as a fitness function in a particle swarm algorithm, and calculating the fitness value of each particle;
for each particle, its fitness value is compared with the individual optimum position PmIf better, update PmFor each particle, each iteration can obtain a new position (i.e. a set of solutions of the model), where "updating" the individual optimal position means that if a solution with higher fitness is encountered during the iteration, the corresponding position of the particle at that time is recorded as the optimal solution in the previous iterations so far; for each particle, its fitness value is compared with the global optimum position PgIf better, update PgHere, the optimal positions in the past iterations of all particles are recorded, not just the historical optimal positions for only a single particle;
the flight speed and position of each particle are updated synchronously:
the flight speed update, specifically expressed as:
Vm(k+1)＝w(k)*Vm(k)+c1*rand()*(Pm-Xm(k))+c2*rand()*(Pg-Xm(k))
wherein, Vm(k +1) represents the velocity of the (k +1) th generation particle, Vm(k) Represents the velocity of the kth generation of particles, c1、c2For a preset relative to different optimal positions (individual optimal positions P)mAnd a global optimum position Pg) The learning factor of (1); rand () is at [0, 1 ]]Random numbers within the range, w (k), are inertial weights used to control the search range.
For the inertia weight w (k), a linear decreasing weight strategy is adopted:
Figure BDA0002734636340000101
wherein, wmaxRepresenting the maximum inertial weight, wminRepresents the minimum inertial weight, k represents the current iteration number, TmaxRepresenting the maximum number of iterations;
for preventing the particles from jumping out of the optimized interval directly due to excessive speed, the speed of each dimension of the particles is limited to [ V ]min,Vmax]And when the particle exceeds the speed range, the speed of the particle is initialized again.
Particle position update (i.e. a set of solutions for the model), for discrete encoding, is specifically represented as:
Figure BDA0002734636340000102
for continuous coding, it is specifically expressed as:
Xm(k+1)＝Xm(k)+Vm(k+1)
in the formula, Xm(k +1) represents the position of the (k +1) th generation particle, Xm(k) The position of the kth generation particle is shown, and s represents a particle flight speed conversion function;
iterative optimization, which is to iterate according to the steps, record and update the optimal fitness value and the corresponding particle position of the particle group in the past iteration, synchronously update the flight speed and the position of the particles, and when the preset maximum iteration time T is reachedmaxAnd then obtaining the particle position with the optimal fitness value, wherein the particle position corresponds to the optimal decision variable in the ship formation grade repair plan model, namely whether the ship is repaired or not and the repair starting time, so that the establishment of the ship formation grade repair plan is realized.
In this embodiment, an annual-level repair plan is compiled for a formation of 7 vessels.
According to step 1, the initial performance and the itemized performance of the vessel at the decision starting time point are shown in fig. 2, and the repair data parameters are shown in fig. 3.
According to step 2, the contribution degree weight of the individual ship itemized capacity to the overall efficiency is comprehensively determined by a planning builder according to strategic objectives, mission tasks, relevant regulation instructions and basic unit opinions, (w)c1,wc2,wc3,wc4,wc5) (0.2,0.15,0.25,0.25, 0.15); the ship efficiency attenuation function is obtained by curve fitting according to the evaluation data of various capabilities of the ship at different time points, and is shown in figure 4; the weight of a single ship in formation is determined according to the importance degree of the ship and the army and mission task of the ship in formation, (w)1,w2,w3,w4,w5,w6,w7) (0.17,0.17,0.14,0.14,0.14,0.12, 0.12); the expected value of the formation ability of the naval vessel is determined according to strategic targets, mission tasks, superior instructions and basic unit opinions, (E)X,EM,EF,EJ,EL) (25,40,25,25, 30); risk aversion coefficient lambdaiAnd betaiAnd 0.6 and 1.2 respectively according to the preference of a decision maker for risks.
According to the step 3, the budget index of the ship repair expenditure is 7000 ten thousand yuan, and the lowest rate of flight is comprehensively determined to be r 60% according to the army type and the current regulations;
according to step 4, particle swarm calculationThe method parameters are set as follows: n is 100, Tmax＝200，c1＝1.5，c2＝1.5，wmax＝0.9，wmin＝0.4，Vmax＝2，Vmin＝-2。
Through optimization and solution, the ship formation grade repair plan is obtained and is shown in fig. 5, the value function change situation of the ship formation itemizing capacity is shown in fig. 6, and the available state of the ship is shown in fig. 7.
For comparative analysis, a traditional repair plan compiling method based on calendar time is adopted, a vessel grade repair plan is shown in a table 8, a value function change condition of vessel formation itemizing capacity is shown in a table 9, and a vessel available state is shown in a table 10.
Compared with a repair plan compiled by a traditional method, the ship formation system has higher efficiency and shows higher fighting capacity level; the ship formation itemizing capacity can reach the expected value of the itemizing capacity all year round and can meet the use requirement at any time; the ships are more uniformly distributed in the repair quantity, and the coordination relationship between the use and repair of the ships and between different ships is better processed.
The invention provides a ship formation grade repair plan compiling method based on expected system efficiency, which can be used for scientifically simulating a ship formation grade repair plan under the existing repair mode framework by fully considering the system efficiency of ship formation, the efficiency attenuation characteristic of ships and the expectation and preference of a decision maker, so that the ship formation can effectively meet the system operation requirement.
Details not described in this specification are within the skill of the art that are well known to those skilled in the art.

Claims (5)

1. A ship formation grade repair plan compiling method based on expected system efficiency is characterized by comprising the following steps:
step 1: establishing a ship formation grade repair plan basic database, wherein the ship formation grade repair plan basic database comprises the initial efficiency of a ship at a decision starting time point and ship repair data parameters;
step 2: establishing a ship formation expectation system performance evaluation model according to the contribution degree of each subentry capacity in the initial performance to the overall performance, the attenuation characteristic of the ship performance over time, the expectation of each subentry capacity value of the ship formation mission task and the preference degree of a decision maker to risks;
and step 3: establishing a ship formation grade repair plan optimization model by utilizing a ship formation expected system efficiency evaluation model, aiming at maximizing the expected system efficiency of the ship formation in a certain period, taking whether the ship is repaired and the repair starting time as decision variables, and considering the repair period constraint, the over-period repair cost constraint, the repair total cost constraint, the efficiency constraint and the aircraft rate constraint;
and 4, step 4: and solving the optimization model of the ship formation grade repair plan by using a hybrid coding particle swarm algorithm to obtain whether the ships are repaired or not and the repair starting time, and further determining the ship formation grade repair plan.
2. The method for developing a vessel fleet-level repair plan based on a desired system performance as claimed in claim 1, wherein: in the step 1, the initial efficiency of the naval vessel at the decision starting time point comprises a command control ability value, a maneuvering ability value, a defense ability value, an attack ability value and a guarantee ability value; the vessel repair data parameters comprise a vessel number, a repair level preliminarily drawn according to the current vessel level repair rule, a budget index, a repair period and recovery efficiency, the time length from the last level repair or the end of the service guarantee to the decision-making starting point, and a use efficiency lower limit determined according to the use requirement.
3. The method for developing a vessel fleet-level repair plan based on a desired system performance as claimed in claim 1, wherein: the specific method of the step 2 is that firstly, a single naval vessel efficiency evaluation model is established according to the contribution degree of each subentry capacity in the initial efficiency to the overall efficiency;
the efficiency of a single naval vessel is divided into a command control ability value, a maneuvering ability value, a defense ability value, an attack ability value and a guarantee ability value, and each subentry ability is considered to the totalThe contribution degree of the efficiency is obtained to obtain a single naval vessel SiThe overall efficiency of (A) is:
Pi＝wc1Xi+wc2Mi+wc3Fi+wc4Ji+wc5Li
in the formula, PiIndicating vessels SiOverall efficiency of Xi、Mi、Fi、Ji、LiRespectively representing the command control capability value, the maneuvering capability value, the defense capability value, the attack capability value and the guarantee capability value of the naval vessel, wc1～wc5Respectively representing the contribution degree weight of the subentry capacity to the overall efficiency;
then, aiming at the characteristic that the efficiency of the naval vessel attenuates along with time in the actual use process, a reverse logistic function is adopted to establish an attenuation model of the naval vessel efficiency;
Figure FDA0002734636330000021
in the formula, Pi(t) denotes a vessel SiPerformance function of PioIndicates the initial performance of the vessel, kiRepresenting the rate of decay of the potency, t representing time, aiRepresents the length of the decay in efficiency, biThe time length from the completion of the last grade repair or the service guarantee of the naval vessel to the decision starting time point is represented, and e is a natural constant;
further based on the attenuation model of the efficiency of the naval vessel, the efficiency is recovered by considering the grade repair, and the naval vessel SiThe efficiency change conditions are as follows:
Figure FDA0002734636330000022
wherein, [0, T]Represents the start and end time, T, of the periodi sAnd Ti eRespectively represents the starting and stopping time point of repair, P'ioIndicating the efficacy, Δ P, of the vessel before repairiIndicating the effectiveness of the recovery after repair；
Establishing a ship formation system efficiency evaluation model, and obtaining the single capability value of the ship formation according to a ship efficiency attenuation model:
Figure FDA0002734636330000031
in the formula, CXRepresenting a certain individual capability value, w, of a fleet of vesselsiIndicating vessels SiWeight in formation of ships, XiIndicating vessels SiThe initial singles ability value of (a);
further obtaining the efficiency of a naval vessel formation system:
C(t)＝wc1CX(t)+wc2CM(t)+wc3CF(t)+wc4CJ(t)+wc5CL(t)
wherein C (t) represents the efficiency of the ship formation system, CX(t)、CM(t)、CF(t)、CJ(t)、CL(t) command control ability value, maneuvering ability value, defense ability value, attack ability value and guarantee ability value of naval vessel formation, wc1～wc5Representing the contribution degree weight of the item dividing capacity to the overall efficiency;
establishing a ship formation expected system efficiency evaluation model, considering the expectation of each subentry capacity value when the ship formation fulfills mission tasks and the waste caused by unbalanced recovery of single capacity, and establishing a ship formation system efficiency value function by using a prospect theory;
Figure FDA0002734636330000032
in the formula of UX(t) a cost function representing the ability of a vessel to form a single item, CX(t) actual value representing the ability of the vessel to form the single item, EXExpected value, λ, representing the ability of a vessel to form the single itemiAnd betaiIndicating the risk of the actual value being higher and lower than desired, respectivelyA coefficient of malignancy of 0<λi<1<βi；
Further obtaining a naval vessel formation expected system efficiency evaluation model:
U(t)＝wc1UX(t)+wc2UM(t)+wc3UF(t)+wc4UJ(t)+wc5UL(t)
wherein U (t) represents the expected system efficiency of the ship formation, wc1～wc5Represents the weight of contribution of the subentry ability to the overall efficiency, UX(t)、UM(t)、UF(t)、UJ(t)、ULAnd (t) the value function of command control capability, maneuvering capability, defense capability, attack capability and guarantee capability of the ship formation is represented.
4. The method for developing a vessel fleet-level repair plan based on a desired system performance as claimed in claim 1, wherein: the specific method in the step 3 is that the optimization target of the optimization model is that the efficiency of an expected system of the ship formation in a certain period T is maximized, and is expressed as follows:
Figure FDA0002734636330000041
wherein U (t) represents the expected system efficiency of the ship formation at the time t, and fUIndicating that the vessel formation is in time [0, T ]]Desired system efficacy;
the decision variables and the value ranges of the optimization models are as follows: for naval vessels SiWhether or not to repair xi，xi1 denotes repair, xi0 means no repair; and the starting time T of the repairi s，Ti s∈[0,T]；
The constraint conditions of the optimization model are as follows:
for repair time period constraints, according to current repair work regulations, for vessels SiComprises the following steps:
Ti e-Ti s＝ti
in the formula, Ti eIndicating the end of repair time, Ti sIndicating the repair start time, tiIndicating a well-defined repair period according to the regulations;
for the midspan repair cost constraint, when the vessel has a span of [0, T]Time period of repair, current period [0, T]The repair cost is the repair budget index and the current period [0, T]Product of repair time fraction, naval vessel SiThe interim repair expenses are:
Figure FDA0002734636330000042
in the formula, ciIndicates the current period [0, T]Cost of repair, CiRepresenting a repair budget indicator, t'iIndicates the current period [0, T]Repair time;
for the total cost constraint of repair, the total cost of the warship to be repaired is not more than the total cost budget index, which is specifically expressed as:
Figure FDA0002734636330000043
in the formula, xiWhen 0, it represents the vessel SiNo repair is needed; x is the number ofiWhen 1, it represents a vessel SiNeed to be repaired; c. CiIndicating vessels SiCurrent stage [0, T]Cost of repair, CzRepresenting a total budget expenditure indicator;
for the performance constraint, without modification, repairs result in only limited recovery of performance, and do not achieve the initial performance at the end of the last level repair or commissioning, when:
P′io+ΔPi≤Pio
in the formula, PioRepresenting the initial efficiency, P 'of the vessel'ioIndicating the efficacy, Δ P, of the vessel before repairiThe efficiency of the ship which can be recovered after the repair is shown;
for the enroute rate constraint, it is specifically expressed as:
Figure FDA0002734636330000051
wherein N represents the total number of vessels, NrIndicating the number of vessels in repair, NuRepresenting the number of vessels in a state of overhaul and r representing the specified minimum airspeed requirement.
5. The method for developing a vessel fleet-level repair plan based on a desired system performance as claimed in claim 1, wherein: in the step 4, the concrete steps of solving the ship formation grade repair plan optimization model by using the hybrid coding particle swarm algorithm are as follows:
firstly, coding, considering that decision variables of a ship formation grade repair plan optimization model are whether ships are repaired or not and the repair starting time, wherein the decision variables have discrete variables and continuous variables, and a mode of sectionally and mixedly coding the two variables is adopted, specifically
Figure FDA0002734636330000052
Wherein, [ x ]1,x2,…,xn]Using binary coding, taking 0 or 1, x1,x2,…,xnIndicating whether the 1 st to the nth vessels are repaired or not;
Figure FDA0002734636330000053
real number coding is adopted to represent the repair starting time of the 1 st to the nth naval vessels, and the value range is [0, T]；
At this time, for a population consisting of N particles, the current position of the particle m (m ═ 1,2, …, N) is represented as
Figure FDA0002734636330000054
xm1,xm2,...,xmnRepresents the decision variable [ x ] corresponding to the mth particle1,x2,…,xn]A set of solutions representing whether the vessel is repaired;
Figure FDA0002734636330000055
representing decision variables corresponding to the m-th particle
Figure FDA0002734636330000056
Represents the starting time of the vessel repair;
the current flight speed is expressed as
Figure FDA0002734636330000057
Figure FDA0002734636330000058
Respectively representing the corresponding particle positions
Figure FDA0002734636330000061
Flight speeds of different dimensions, PmThe position with the best adaptation value experienced by the particle m is represented, called the individual optimum position; pgThe position representing the optimal fitness value searched so far by all the particles in the whole particle swarm is called as a global optimal position;
initialising the particle population, i.e. randomly setting the initial position X of the particles mm(1) And an initial velocity Vm(1)；
Then, an objective function f in the ship formation grade repair plan optimization modelUDirectly serving as a fitness function in a particle swarm algorithm, and calculating the fitness value of each particle;
for each particle, its fitness value is compared with the individual optimum position PmIf better, update PmIf a solution with higher fitness is encountered in the iteration process, recording the corresponding position of the particle at the moment as the optimal solution in the iteration until now; for each particle, its fitness value is compared with the global optimum position PgIf better, update PgHere, the optimal positions in the past iterations of all particles are recorded, not just the historical optimal positions for only a single particle;
the flight speed and position of each particle are updated synchronously:
the flight speed update, specifically expressed as:
Vm(k+1)＝w(k)*Vm(k)+c1*rand()*(Pm-Xm(k))+c2*rand()*(Pg-Xm(k))
wherein, Vm(k +1) represents the velocity of the (k +1) th generation particle, Vm(k) Represents the velocity of the kth generation of particles, c1、c2The learning factors are preset learning factors relative to different optimal positions; rand () is at [0, 1 ]]Random number within a range, w (k) is an inertial weight used to control the search range;
for the inertia weight w (k), a linear decreasing weight strategy is adopted:
Figure FDA0002734636330000062
wherein, wmaxRepresenting the maximum inertial weight, wminRepresents the minimum inertial weight, k represents the current iteration number, TmaxRepresenting the maximum number of iterations;
for preventing the particles from jumping out of the optimized interval directly due to excessive speed, the speed of each dimension of the particles is limited to [ V ]min,Vmax]If the particle exceeds the speed range, the speed of the particle is initialized again;
particle position update, for discrete encoding, is specifically expressed as:
Figure FDA0002734636330000071
for continuous coding, it is specifically expressed as:
Xm(k+1)＝Xm(k)+Vm(k+1)
in the formula, Xm(k +1) represents the position of the (k +1) th generation particle, Xm(k) The position of the kth generation particle is shown, and s represents a particle flight speed conversion function;
iterative optimization, which is to iterate according to the steps, record and update the optimal fitness value and the corresponding particle position of the particle group in the past iteration, synchronously update the flight speed and the position of the particles, and when the preset maximum iteration time T is reachedmaxAnd then obtaining the particle position with the optimal fitness value, wherein the particle position corresponds to the optimal decision variable in the ship formation grade repair plan model, namely whether the ship is repaired or not and the repair starting time, so that the establishment of the ship formation grade repair plan is realized.
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