CN114676572A - Parameter determination method and device and computer readable storage medium - Google Patents
Parameter determination method and device and computer readable storage medium Download PDFInfo
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Abstract
The invention provides a parameter determination method and device and a computer readable storage medium, which are used for determining universal quality characteristic parameters of equipment, firstly, the universal quality characteristic parameters of the equipment are utilized to construct a relation matrix A, and two universal quality characteristic parameters of the equipment with the maximum correlation value among the parameters in the matrix A are determined; then, establishing an Euler network simulation model and determining the running state of the equipment; then, a system efficiency model is constructed, two equipment general quality characteristic parameters with the maximum correlation value are used as the input of the system efficiency model, and the equipment running state is used as a boundary condition to carry out system efficiency analysis; and finally, taking a group of parameters of the optimal probability scheme in all schemes meeting the design requirements of the equipment general quality characteristic as a final scheme for determining the equipment general quality characteristic parameters. The invention reduces the mutual influence caused by parameter adjustment to the maximum extent, and can quickly select the optimal target scheme.
Description
Technical Field
The invention belongs to the technical field of equipment quality reliability, and particularly relates to a parameter determination method and device and a computer readable storage medium.
Background
The universal quality characteristics of the equipment are important characteristic compositions of the equipment, are provided relative to special quality characteristics, and specifically comprise reliability, maintainability, testability, supportability, safety, environmental adaptability and the like. The calculation of the universal quality characteristic parameters is provided in the demonstration stage of equipment development and production and is the basic requirement for standardizing the equipment development and production. The calculation and optimization of the universal quality characteristics of the equipment are started from the last 60 th century, only appear on one aspect of reliability at first, and gradually develop to the aspects of maintenance guarantee and the like at later time, and modeling and simulation technologies are increasingly adopted. The demonstration of the general quality characteristic parameters of the existing equipment is basically realized by adopting a method for constructing a simulation model, and the method comprises the following steps: a function model, a task model, a maintenance model, a guarantee model, etc. The main purpose is to realize the calculation of the universal quality characteristics through the models, but at present, the models can not consider the association relation between the universal quality characteristic parameters. Although scholars and engineers at home and abroad carry out a great deal of research on the universal quality characteristic parameters of equipment, the method is only limited to the construction of a parameter system, and the mutual influence relationship among the parameters is not clear. When engineering technicians design general quality characteristics, the correlation among parameters is often ignored, and the parameters are difficult to be effectively identified and demonstrated, so that the demonstration difficulty is increased. The general quality characteristic of the equipment has many relevant factors in the design process, so that the repetition often occurs in the parameter selection process, and an effective method is not provided at present to completely avoid the problem.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a parameter determination method, a parameter determination device and a computer readable storage medium, which can enhance the effectiveness and operability of general quality characteristic parameter demonstration and reduce the difficulty of parameter demonstration.
The present invention achieves the above-described object by the following technical means.
A parameter determination method for determining a universal quality characteristic parameter of an apparatus, the method comprising:
acquiring a plurality of universal quality characteristic parameters of the equipment, and constructing a relation matrix A of the universal quality characteristic parameters of all the equipment;
determining two equipment general quality characteristic parameters with the maximum correlation value in the correlation matrix A; performing simulation calculation on the tasks of the equipment by using a simulation model, and determining the running state of the equipment;
taking two equipment general quality characteristic parameters with the maximum correlation values as the input of a system efficiency model, taking an equipment running state as a boundary condition, carrying out system efficiency analysis, and determining the task success rate of the equipment in a normal state, the task success rate of a fault state, the task failure rate of the normal state and the task failure rate of the fault state;
scheme A composed of parameters of general quality characteristics when equipment is equippedtParameter C ofjSatisfaction degree SijSatisfies the following conditions: sij≤ErJudging that the use efficiency of the equipment meets the requirement of universal quality characteristic design of the equipment; wherein ErFor system performance analysis results, parameter CjIs composed of i CijComposition C ofijThe method comprises the following steps of (1) providing universal quality characteristic parameters for any equipment, wherein i is a parameter ordinal number, i is more than 1, and j is the number of parameters; t is the number of schemes, AtThe equipment general quality characteristic parameters in (1) belong to a relation matrix A;
taking a group of parameters of the optimal probability scheme in all schemes meeting the design requirement of the universal quality characteristic of the equipment as a final scheme for determining the universal quality characteristic parameters of the equipment; wherein the optimal probability is the probability that a solution is better than all other solutions in the entire solution space S.
Further, obtaining a plurality of generic quality characteristic parameters of the equipment comprises:
determining the type of equipment, the use condition of the equipment and the environmental condition of the equipment;
determining equipment general quality characteristic parameters according to equipment types, equipment use conditions and equipment environment conditions
Further, the equipment universal quality characteristic parameters comprise average fault interval time, average repair time, average logistic delay time, fault rate and service life.
Furthermore, the equipment universal quality characteristic parameters are used as input conditions, and a correlation coefficient function is utilized to construct a relation matrix A of all the equipment universal quality characteristic parameters.
Further, the relationship matrix is:
wherein:
C(i-1)1+k1(j)＝Ci1
C(i-1)j+kj(j)＝Cij(ii) a K isj(j) Satisfies the following conditions:
wherein: c'ijAnd C1jRespectively the upper and lower limits of the jth parameter.
Further, when i is equal to 1, the correlation value is determined according to the following:
wherein:
is XjAnd X1Coefficient of correlation of (c), Cov (X)1,Xj) Is XjAnd X1The covariance of (a) of (b),
is X1The standard deviation of (a) is determined,
is XjStandard deviation of (A), and
further, the simulation model is an euler network simulation model, and the euler network simulation model is obtained by modeling the use or task working condition through the euler network.
Further, the system performance model is:
wherein: alpha is alpha1Representing the probability of the equipment being in a normal state, alpha2Representing probability of equipment failure state, c1Indicating the inherent capability of the equipment in its normal state, c2Indicating the capability of the equipment to complete the task in the fault state, d11Task success rate indicating equipment Normal status, d12Task failure rate, d, indicating the normal state of the equipment21Indicating success of a task of a fault state of the equipment, d22The task indicating the equipment failure state is unsuccessful.
Further, the optimal probability of any scheme satisfies:
Pobi＝P(J(Ap)＞max{J(Aq)})Aq∈S，p≠q
wherein J is a comprehensive evaluation function of the scheme.
Further, still include: sensitivity passing of single parameter in optimal probability scheme
Is calculated to obtain wherein
αji、βjiIs a parameter CjThe Beta of (1) is fitted to the distribution parameters, and J is the comprehensive evaluation function of the scheme.
A parameter determination apparatus, comprising:
the parameter correlation determination module is used for determining two equipment general quality characteristic parameters with the maximum correlation values in the correlation matrix;
the equipment running state determining module is used for determining the running state of the equipment;
the efficiency analysis module is used for carrying out system efficiency analysis by taking the result of the parameter correlation determination module as the input of a system efficiency model and the running state of equipment as boundary conditions;
and the final scheme determining module is used for judging a scheme meeting the design requirement of the universal quality characteristic of the equipment according to the system efficiency analysis result, and taking a group of parameters of the optimal probability scheme as a final scheme for determining the universal quality characteristic parameters of the equipment.
A computer-readable storage medium, in which a computer program is stored which, when executed by a processor, causes the processor to carry out the above-mentioned parameter determination method.
The invention has the beneficial effects that:
(1) the method utilizes the correlation coefficient function to perform correlation selection on the equipment general quality characteristic parameters, takes the equipment general quality characteristic parameters with strong correlation as a means for evaluating the system efficiency, and takes possibly influenced parameters as variables for analyzing the system efficiency, thereby reducing the mutual influence caused by parameter adjustment to the maximum extent;
(2) in the invention, one group of parameters of the optimal probability scheme in all parameters meeting the design requirements of the universal quality characteristics of the equipment is used as a final scheme for determining the universal quality characteristic parameters of the equipment, and the traditional comparison of system efficiency is changed into the possibility (namely the probability) that the target scheme is superior to all other schemes on the premise of meeting the design requirements, so that the optimal target scheme can be easily selected;
(3) according to the equipment type, the using working condition of the equipment and the environmental condition of the equipment, the equipment general quality characteristic parameters which accord with the research object are screened out, and the correlation analysis is facilitated;
(4) the equipment general quality characteristic parameters comprise average fault interval time, average repair time, average logistic delay time, fault rate and service life, and are favorable for determining the normal state or fault state of the equipment;
(5) the simulation model of the invention adopts an Euler network simulation model, which visually displays the correlation relationship of equipment tasks and is convenient for simulation calculation;
(6) the system efficiency model comprises the inherent capacity of the equipment in a normal state and the capacity of completing tasks in a fault state of the equipment, and can comprehensively reflect the running state of the equipment;
(7) the invention calculates the sensitivity of a single parameter in the optimal probability scheme, determines the equipment general quality characteristic parameter with the highest sensitivity, and ensures that the optimal probability of the scheme is higher, the satisfaction is improved and the task success rate is higher by adjusting the characteristic parameter.
Drawings
FIG. 1 is a flow chart of a method for determining a universal quality characteristic parameter of equipment based on an Euler network according to the present invention;
FIG. 2(a) is a diagram of a first model of an equipment task process;
FIG. 2(b) is a diagram of a second model of an equipment task process;
FIG. 2(c) is a diagram of a third model of an environmental task process;
FIG. 3 is a graph of a parameter fit for inventive protocol 1;
FIG. 4 is a graph of a parameter fit for scheme 2 of the present invention;
FIG. 5 is a graph of a parameter fit for inventive protocol 3.
Detailed Description
The invention will be further described with reference to the following figures and specific examples, but the scope of the invention is not limited thereto.
The invention relates to a method for determining equipment universal quality characteristic parameters based on an Euler network, which specifically comprises the following steps:
s1, determining the type of the equipment, including: rockets (missiles), aeronautical equipment (fixed-wing aircraft, helicopters, etc.), vehicles (wheels)Formula, caterpillar), ship, etc. The use (mission) conditions include: speed n, height H, speed v, etc. The environmental conditions include: temperature t, humidity w, air pressure ApAnd the like. Depending on the type of equipment, the operating (mission) conditions and environmental conditions are different. Through the determination of the parameters, basic conditions of equipment operation are obtained, and therefore the long-term working state or task state of the equipment is clarified.
Then, relevant characteristic parameters of the equipment general quality characteristics are determined, such as: the general quality characteristics include mean time between failures MTBF, mean time to repair MTTR, mean logistical delay time MLFT, failure rate, service life, and the like. The equipment has a plurality of universal quality characteristic parameters, and can be initially selected according to the equipment characteristics, the use (task) working conditions and the like, so as to provide analysis input conditions for the subsequent parameter correlation analysis.
S2, the correlation between parameters is complicated, and a parameter may be correlated with a plurality of parameters, so that it has a greater influence on which parameter, and a quantitative calculation is necessary. And taking the equipment general quality characteristic parameters as input conditions, and constructing a relation matrix A of all the equipment general quality characteristic parameters by means of a correlation coefficient function. The construction process is as follows:
let CijThe method is characterized in that the method is a general quality characteristic parameter of any equipment, wherein i is a parameter ordinal number, and j is a parameter number. Order:
wherein, C'ijAnd C1jRespectively the upper and lower limits of the jth parameter.
Number series K:
K＝[k1(j) k2(j)…kj(j)]
＝[Δk1 Δk2…Δkj]
constructing a relation matrix A of all equipment general quality characteristic parameters:
wherein there are:
C(i-1)1+Δk1＝Ci1
C(i-1)j+Δkj＝Cij
s3, judging the correlation among the parameters in the relation matrix A
Order:
the correlation is determined according to the following formula:
wherein
Is XjAnd X1Coefficient of correlation of (c), Cov (X)1,Xj) Is XjAnd X1The covariance of (a) of (b),
is X1The standard deviation of (a) is determined,
is XjStandard deviation of (2). The correlation value epsilon is a number between 0 and 1, and when epsilon is 1, the two parameters are completely linearly correlated, and the closer to 1, the stronger the correlation between the two parameters.
Constructing a correlation coefficient array for a single parameter or a correlation coefficient matrix for a plurality of parameters; as shown in the following formula:
and determining and outputting a group of parameters with the strongest correlation relationship through the correlation coefficient array or the correlation coefficient matrix, wherein the parameters are used as judgment parameters for carrying out system performance analysis under the constraint of equipment use performance and cost (including time).
S4, modeling the use or task working condition in an Euler network mode to construct an Euler network simulation model
According to the characteristics of the Euler network system and the general rules of the life cycle of the entities, the static attribute value of each entity mark and the state variable at any time are required to be given. For example, in aeronautical equipment, three core entities are defined: the system comprises a task entity M, an equipment entity Q and an environment entity E, wherein the task entity M is a temporary entity, and the equipment entity Q and the environment entity E are permanent entities.
It is assumed that the time to complete a task follows a normal distribution N (μ, σ)2) The equipment-to-life follows an exponential distribution e (λ).
The following description will be made by taking two series tasks, and the simulation model of euler's net for performing tasks under two environments by a certain aviation equipment is shown in fig. 2(a), (b), and (c). Wherein a is1、a2Respectively showing a first executive task and a second executive task, a3Indicating completion of the task; s1、s2Respectively representing equipment on task one and task two, s3Indicating outside the system, s4Indicating equipment waiting; s is5、s6Respectively, the task waiting caused by the environment.
The simulation time T is set to 0, and a is set as an active state, which represents a work (task) process. If a occurs current activity, for either condition
(wherein s includes s1-s6When the task is changed, the number of s is changed;
representing a certain active state), the tag set Δ s of s in a certain specific state is { δ | ρ (δ) ═ s }, then s for the input tag set of a is:
where ρ istIs the phase at a certain time;
the result of the occurrence of the active state a is that the tag set Δ x of the element x under the identity M of the euler net is changed to a new tag set Δ' x and:
the identity M of the euler network thus becomes:
M′(x)＝|Δ′x|
under the new label M' of the euler network, the phase of the label δ is:
and performing simulation calculation to obtain an evaluation result of the equipment operation state (represented by the mark set delta' x). For the activity where a is the current end, the same as the input condition, which is not described herein again, if T ≧ T*(stop time) the run ends.
Based on the Euler network modeling method, the equipment, environment and the like are materialized, and the activity cycle of the entity is described graphically in a network diagram mode. Entity behaviors are described through equipment use (task) process analysis in S1, and the use (task) working conditions are modeled in an Euler network mode corresponding to different entity behaviors under different equipment use conditions. The Euler network simulation model realizes the description and evaluation of the equipment running state and provides boundary conditions for further system efficiency analysis.
And S5, the system efficiency analysis can effectively determine the system efficiency, and realize comprehensive balance and optimization of the general quality characteristic parameters. The equipment has more judgment parameters under the constraints of operational performance and cost (including time), wherein the aviation equipment is taken as an example, and the main judgment parameters are defined from the perspective of system efficiency as follows:
1) availability of systemsot: representing the probability of the aeronautical equipment being in an intact state;
2) inherent availability of the system Rct: representing the probability of preparing specified intact aerial equipment within a specified time;
3) task reliability Rmt: indicating the ability of the aerospace vehicle to perform a specified function within a specified mission profile.
Therefore, an aviation equipment general quality characteristic parameter demonstration model based on system efficiency is established:
Er＝AsotRctRmt
that is, the aviation equipment must satisfy the requirements of three aspects of the availability of the system, the inherent availability of the system and the task reliability at the same time, so as to consider the system capability to be achieved.
The judgment parameter and the general quality characteristic parameter demonstration model are also suitable for other equipment types.
Constructing a system efficiency model, wherein the system efficiency is expressed by a vector as:
wherein: alpha is alpha1Representing the probability of the equipment being in a normal state, alpha2Representing probability of equipment failure state, c1Representing the intrinsic capability of the equipment in the normal state of the equipment (considering tactical indexes, equipment repairability, guarantee conditions, personnel quality and the like), c2Indicating the capability of the equipment to complete the task in the fault state, d11、d12、d21、d22Respectively showing the success rate of the task in the normal state, the failure rate of the task in the normal state, the success rate of the task in the fault state and the failure rate of the task in the fault state of the equipment.
And (4) taking a group of parameters with the strongest correlation obtained in the step (S3) as the input of a system efficiency model, taking the equipment running state obtained in the step (S4) as a boundary condition, carrying out system efficiency analysis, and determining the task success rate and the task failure rate of the equipment in the normal state and the fault state.
Then, the system efficiency result is judged, and since the parameters of the general quality characteristics are used as variables, the judgment result can be regarded as a random variable, and then the scheme (a group C) is carried outijCorresponding to a solution), the solution selection process may be translated into the concept of calculating the best solution probability value, i.e., the best probability, for a solution. Optimal probability refers to the likelihood that a solution is better than all other solutions in the overall solution space S. The determination process is as follows:
Pobi＝P(J(Ap)＞max{J(Aq)})Aq∈S，p≠q
where J is the overall evaluation function of the scheme, for example: an entropy value method; the comprehensive evaluation function in the present invention may also be used in other ways, and is not limited to the entropy method.
According to scheme AtParameter C ofj(by i CijComposition C ofijFor arbitrary equipment universal quality characteristic parameters) of the distribution BijGenerating a random number r, and taking r as a scheme AiParameter C ofjDegree of satisfaction SijAnd when: sij≤ErAnd judging that the use efficiency of the equipment meets the requirement of the universal quality characteristic design of the equipment. And taking a set of parameters of a scheme with the highest probability (optimal probability) among all schemes meeting the design requirement of the equipment universal quality characteristic as a final scheme of the universal quality characteristic. t is the number of schemes, constituting scheme AtThe number of the equipment universal quality characteristic parameters is less than or equal to the number of the equipment universal quality characteristic parameters in the relation matrix A, namely the composition scheme AtThe equipment generic quality characteristic parameter of (2) belongs to a relation matrix A.
Variations in the recipe ordering represent the uncertainty effect of the simulation data, and if a control variable is made for a certain parameter, a sensitivity calculation for the individual parameter can be formed.
Wherein
αji、βjiIs a parameter CjBeta of (d) is fitted to the distribution parameters. Considering the characteristics of aviation equipment, the comprehensive satisfaction degree is as follows: availability of systemsotInherent system availability RctAnd task reliability Rmt。
The method has the advantages that the general quality characteristic parameter of a certain device with the highest sensitivity is determined, the optimal probability of the scheme is higher by adjusting the characteristic parameter, the satisfaction degree is improved, and the task success rate is higher.
This example determines the type of equipment to be aircraft equipment, using (mission) operating conditions of 7500r/min for speed n, 9500m for altitude H and 850Km/H for speed v, and ambient conditions of-30 ℃ for temperature t, 20% for humidity w and air pressure apAs a common working condition of the equipment, 30Kpa, and so on, the following conditions are obtained: working conditions or task states under different conditions such as limit working conditions, standby and the like. Due to the particularities of the aeronautical equipment, parameters such as: availability of use (A)O) Mean time between failure flight hours (TFBF), Mean Time Between Failure (MTBF), mission reliability (R)M) Air parking rate (IFSR), early interchange rate (UERR), service life (TLSE), Mean Time To Repair (MTTR), fault detection rate (R)FD) Accident Rate (RPA), etc.
And executing the steps S2-S5, and obtaining a parameter fitting graph under different schemes by fitting various parameter simulation result data influencing the comprehensive satisfaction degree through simulation calculation, as shown in the figures 3-5.
Setting the weight of the parameter by the calculation method of the optimal probability, and assuming that the system uses the weight W of the availabilitysotWeight W of system inherent availability 0.3ctWeight W of task reliability 0.3mt0.4, the calculation results are shown in table 1Shown in the figure.
TABLE 1 optimal probability calculation results
From the calculation result, the number of times that the scheme 3 becomes the optimal scheme is the largest, the scheme 1 is the largest, and the scheme 2 is the smallest, and the scheme 3 can be determined to be the optimal scheme of the three schemes according to the calculation result value.
According to an embodiment of the present invention, there is provided a parameter determination apparatus including:
the parameter correlation determination module is used for determining two equipment general quality characteristic parameters with the maximum correlation values in the correlation matrix;
the equipment running state determining module is used for determining the running state of the equipment;
the efficiency analysis module is used for carrying out system efficiency analysis by taking the result of the parameter correlation determination module as the input of a system efficiency model and taking the running state of equipment as a boundary condition;
and the final scheme determining module is used for judging a scheme meeting the design requirement of the universal quality characteristic of the equipment according to the system efficiency analysis result, and taking a group of parameters of the optimal probability scheme as a final scheme for determining the universal quality characteristic parameters of the equipment.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk, an optical disk, or the like.
The present invention is not limited to the above-described embodiments, and any obvious improvements, substitutions or modifications can be made by those skilled in the art without departing from the spirit of the present invention.
Claims (12)
1. A method of parameter determination, characterized by: for determining a universal quality characteristic parameter of an apparatus, the method comprising:
acquiring a plurality of universal quality characteristic parameters of the equipment, and constructing a relation matrix A of the universal quality characteristic parameters of all the equipment;
determining two equipment general quality characteristic parameters with the maximum correlation value in the correlation matrix A; performing simulation calculation on the tasks of the equipment by using a simulation model, and determining the running state of the equipment;
taking two equipment general quality characteristic parameters with the maximum correlation values as the input of a system efficiency model, taking an equipment running state as a boundary condition, carrying out system efficiency analysis, and determining the task success rate of the equipment in a normal state, the task success rate of a fault state, the task failure rate of the normal state and the task failure rate of the fault state;
scheme A composed of universal quality characteristic parameters when equipment is equippedtParameter C ofjSatisfaction degree SijSatisfies the following conditions: sij≤ErJudging that the use efficiency of the equipment meets the requirement of universal quality characteristic design of the equipment; wherein ErFor system performance analysis results, parameter CjIs composed of i CijComposition C ofijThe method comprises the following steps of (1) providing universal quality characteristic parameters for any equipment, wherein i is a parameter ordinal number, i is more than 1, and j is the number of parameters; t is the number of schemes, AtThe equipment general quality characteristic parameters in (1) belong to a relation matrix A;
taking a group of parameters of the optimal probability scheme in all schemes meeting the design requirement of the universal quality characteristic of the equipment as a final scheme for determining the universal quality characteristic parameters of the equipment; wherein the optimal probability is the probability that a solution is better than all other solutions in the entire solution space S.
2. The parameter determination method of claim 1, wherein obtaining a plurality of generic quality characteristic parameters of the equipment comprises:
determining the type of equipment, the use condition of the equipment and the environmental condition of the equipment;
and determining the universal quality characteristic parameters of the equipment according to the type of the equipment, the using condition of the equipment and the environmental condition of the equipment.
3. The parameter determination method according to claim 1 or 2, characterized in that the equipment common quality characteristic parameters include average fault interval time, average repair time, average logistics delay time, fault rate and service life.
4. The parameter determination method according to claim 3, wherein the equipment universal quality characteristic parameters are used as input conditions, and a correlation coefficient function is used to construct a relation matrix A of all the equipment universal quality characteristic parameters.
5. The parameter determination method of claim 4, wherein the relationship matrix is:
wherein:
k is the same asj(j) Satisfies the following conditions:
wherein: c'ijAnd C1jRespectively the upper and lower limits of the jth parameter.
6. The method according to claim 4, wherein when i is 1, the correlation value is determined according to:
wherein:
is XjAnd X1Coefficient of correlation of (c), Cov (X)1,Xj) Is XjAnd X1The covariance of (a) of (b),
is X1The standard deviation of (a) is determined,
is XjStandard deviation of (A), and
7. the parameter determination method according to claim 1, wherein the simulation model is an euler network simulation model, and the euler network simulation model is obtained by modeling a use or task condition through an euler network.
8. The parameter determination method of claim 1, wherein the system performance model is:
wherein: alpha is alpha1Representing the probability of a normal state of the equipment, alpha2Representing probability of equipment failure state, c1Indicating the inherent capability of the equipment in its normal state, c2Indicating the capability of the equipment to complete the task in the fault state, d11Task success rate indicating equipment Normal status, d12Task failure rate indicating the normal state of the equipment, d21Indicating success of a task of a fault state of the equipment, d22To representThe task of arming the fault condition is unsuccessful.
9. The parameter determination method according to claim 1, wherein the optimal probability of any scheme satisfies:
Pobi＝P(J(Ap)＞max{J(Aq)})Aq∈S，p≠q
wherein J is a comprehensive evaluation function of the scheme.
10. The parameter determination method of claim 1, further comprising: sensitivity passing of single parameter in optimal probability scheme
Is calculated to obtain wherein
αji、βjiIs a parameter CjThe Beta of (1) is fitted with the distribution parameters, and J is a comprehensive evaluation function of the scheme.
11. A parameter determination apparatus, comprising:
the parameter correlation determination module is used for determining two equipment general quality characteristic parameters with the maximum correlation values in the correlation matrix;
the equipment running state determining module is used for determining the running state of the equipment;
the efficiency analysis module is used for carrying out system efficiency analysis by taking the result of the parameter correlation determination module as the input of a system efficiency model and taking the running state of equipment as a boundary condition;
and the final scheme determining module is used for judging a scheme meeting the design requirement of the universal quality characteristic of the equipment according to the system efficiency analysis result, and taking a group of parameters of the optimal probability scheme as a final scheme for determining the universal quality characteristic parameters of the equipment.
12. A computer-readable storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, causes the processor to carry out the parameter determination method according to any one of claims 1-10.
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