CN116757308B - Spare part demand prediction method and system for electronic voting component - Google Patents
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Abstract
The invention provides a spare part demand prediction method and a system of an electronic voting component, belonging to the field of calculation of spare part demand of the voting component, wherein the method comprises the following steps: initializing the total number of spare parts and the initial spare part guarantee probability to be 0; when the task of the electronic voting component is finished, all spare parts are consumed, and under the condition that the first electronic component unit is in a fault state after the last spare part is consumed, the first spare part guarantee probability that the number of the electronic component units capable of working is larger than or equal to the minimum number of the working units capable of being accepted is calculated; calculating the probability that all electronic part units work normally when the task of the electronic voting part is finished; calculating the total spare part guarantee probability; when the total spare part guarantee probability is smaller than the spare part guarantee probability index, increasing the total number of spare parts, and repeating the steps; otherwise, the current total number of spare parts is taken as the spare part demand. The spare part demand prediction method of the electronic voting component provided by the invention is high in accuracy.
Description
Technical Field
The invention belongs to the field of calculation of spare part demand of voting parts, and particularly relates to a spare part demand prediction method and system of an electronic voting part.
Background
Spare parts are an important maintenance resource and are the material basis for maintenance work to be carried out. The equipment is not stopped for a long time due to insufficient spare parts, and can not be used for a long time, so that serious waste in economy and management is caused. Spare part guarantee probability is the probability of the required spare part when a fault occurs, and is commonly used for calculating the spare part demand. At present, the demand of spare parts is calculated for voting parts, and a series part method and an approximation method are commonly used. The series component method regards voting components as series components (any unit fails equally to the components fail), and the spare part demand results are larger than the actual demand; the approximation describes the overall lifetime distribution of the represented component approximately using some standard distribution type, such as an exponential, gamma, or normal distribution, etc., and the main disadvantage of this approach is that the error range due to the approximation is not easily controlled.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a spare part demand prediction method and system of an electronic voting component, and aims to solve the problem that the spare part demand of the electronic voting component estimated by the existing series component method and approximation method is larger in deviation.
In order to achieve the above purpose, the invention provides a spare part demand prediction method of an electronic voting component, which comprises the following steps:
s1: initializing the total number of spare parts to be 0, and setting the guarantee probability of the initial spare parts to be 0;
s2: when the task of the electronic voting component is finished, all spare parts are consumed, and a first electronic component unit is in a fault state after consuming the last spare part, calculating first spare part guarantee probability that the number j of the electronic component units capable of working is larger than or equal to the number k of the minimum working units capable of being accepted according to the index distribution of the service lives of the electronic component units and the correlation of the time probability of the r times of faults of the electronic component units with a gamma function; wherein the initial value of j is k;
s3: according to the index distribution of the life compliance index of the electronic part units and the correlation of the time probability of r times of faults of the electronic part units with a gamma function, calculating the probability that spare parts remain when the task of the electronic voting part is finished and all the electronic part units work normally, and taking the probability as a second spare part guarantee probability;
s4: adding the initial spare part guarantee probability, the first spare part guarantee probability and the second spare part guarantee probability, and calculating the total spare part guarantee probability;
S5: comparing the total spare part guarantee probability with the spare part guarantee probability index, and if the total spare part guarantee probability is smaller than the spare part guarantee probability index, increasing the total number of spare parts, and switching to S2; otherwise, taking the total number of the current spare parts as the spare part demand, and outputting the total spare part guarantee probability;
the electronic voting component consists of a plurality of electronic component units of the same type.
Further preferably, S2 specifically comprises the steps of:
s2.1: when the task of the electronic voting component is finished, under the condition that the number of the electronic component units capable of working is larger than or equal to the minimum number of working units capable of being accepted, the electronic component units consume all spare parts, and the first electronic component unit is in a fault state, sequentially calculating the number r of the spare parts consumed by the first electronic component unit and the probability pg of the last spare part consumed according to the index distribution of the service life of the electronic component unit and the time probability of the r times of faults of the electronic component and the gamma function 1+r Probability pf that other electronics units are in a failure state 1+r Probability pw of other electronic component units in working state 1+r The method comprises the steps of carrying out a first treatment on the surface of the Wherein r is more than or equal to 0 and less than or equal to s, s is the total number of the current spare parts, and corresponding probability arrays pg, pf and pw are obtained according to the difference of r;
S2.2: convolving the probability pf that pg, n-j-1 electronic part units are in a fault state and the probability pw that j electronic part units are in a working state for n-1 times, and taking the 1+s element in the convolution result as a probability density function with the number j of the working units after calculating and integrating all electronic consumption spare parts; wherein the initial value of j is k;
s2.3: integrating the probability density function with the number j of the workable units on the task time, and calculating the probability p corresponding to the number j of the workable electronic parts;
s2.4: multiplying the total number n of the electronic parts, the probability p corresponding to the number j of the electronic parts capable of working and the combination nj of the number j of the electronic parts capable of working to obtain a first spare part guarantee probability corresponding to the number j of the electronic parts capable of working, and adding and updating the initial spare part guarantee probability by adopting the initial spare part guarantee probability and the first spare part guarantee probability corresponding to the number j of the electronic parts capable of working;
s2.5: and j=j+1, judging whether j is smaller than or equal to the total number n of the electronic parts, if yes, turning to S2.2, otherwise, taking the initial spare part guarantee probability as a first spare part guarantee probability that the number j of the electronic part units capable of working is larger than or equal to the number k of the minimum working units capable of being accepted.
Further preferably, the method for calculating the second spare part guarantee probability in S3 includes the following steps:
s3.1: calculating a probability array of r spare parts consumed by the current g-th electronic part unit according to the index distribution of the life compliance of the electronic part unit and the correlation of the gamma function of the time probability of the r faults of the electronic part unit; wherein r is more than or equal to 0 and less than or equal to s, and the initial value of g is 1;
s3.2: convolving the calculated probability array of the g-th electronic part unit consuming r spare parts with the probability array of the g-1 electronic part unit consuming l spare parts before synthesis to obtain the probability array of the g electronic part unit consuming l spare parts before synthesis;
s3.3: updating the next electronic part unit into the current g-th electronic part unit, and if the number of g is larger than the total number of the electronic parts, taking the sum of the probabilities that the first 1+s comprehensive electronic part units consume 1+s spare parts in a normal working state as a second spare part guarantee probability; otherwise, turning to S3.1;
and when the electronic voting component task is finished, all the electronic component units are in a normal working state.
Further preferably, the first electronic component unit consumes r spare parts in number, and the probability pg of consuming the last spare part 1+r The method comprises the following steps:
probability pf of other electronics units being in failure state 1+r The method comprises the following steps:
probability pw of other electronic component units being in working state 1+r The method comprises the following steps:
wherein Γ () is a gamma function, x is a working time variable; the electronic part unit life obeys an exponential distribution E (a); t is task time; t is a time variable.
Further preferably, the probability that the current g-th electronic component unit consumes r spare parts in a normal working state is:
wherein Γ () is a gamma function,the electronic part unit life obeys an exponential distribution E (a); t is task time; t is a time variable.
In a second aspect, the present invention provides a spare part demand prediction system for an electronic class voting component, comprising:
the initialization module is used for initializing the total number of spare parts to be 0 and setting the guarantee probability of the initial spare parts to be 0;
the calculation module of the first spare part guarantee probability is used for calculating the first spare part guarantee probability that the number j of the electronic part units capable of working is larger than or equal to the minimum number k of the working units capable of being accepted according to the index distribution of the service lives of the electronic part units and the correlation of the time probability of the r times of faults of the electronic part units and the gamma function under the condition that all the spare parts are consumed when the task of the electronic voting part is finished and the first electronic part unit is in a fault state after consuming the last spare part; wherein the initial value of j is k;
The calculation module of the second spare part guarantee probability is used for calculating the probability that all the electronic part units work normally when the task of the electronic voting component ends according to the fact that the service lives of the electronic part units obey the exponential distribution and the time probability that the electronic part units have r times of faults is related to a gamma function, and taking the probability as the second spare part guarantee probability;
the total spare part guarantee probability calculating module is used for adding the first spare part guarantee probability and the second spare part guarantee probability to calculate the total spare part guarantee probability;
the data output module is used for comparing the total spare part guarantee probability with the spare part guarantee probability index, if the total spare part guarantee probability is smaller than the spare part guarantee probability index, the total number of spare parts is increased, the calculation module of the first spare part guarantee probability and the calculation module of the second spare part guarantee probability are driven to operate, and if not, the current total number of spare parts is used as the spare part demand, and the total spare part guarantee probability is output;
the electronic voting component consists of a plurality of electronic component units of the same type.
Further preferably, the calculating module of the first spare part guarantee probability includes:
the probability parameter calculation unit of each electronic component unit is used for sequentially calculating the probability pg of the first electronic component unit consuming r spare parts and consuming the last spare part according to the index distribution of the service life of the electronic component unit and the correlation of the time probability of r times of faults of the electronic component unit and the gamma function under the condition that the first electronic component unit is in a fault state and the electronic component unit is in a fault state when the number of the electronic component units capable of working is larger than or equal to the minimum number of acceptable working units when the task of the electronic class voting component is finished 1+r Probability pf that other electronics units are in a failure state 1+r Probability pw of other electronic component units in working state 1+r The method comprises the steps of carrying out a first treatment on the surface of the Wherein r is more than or equal to 0 and less than or equal to s, s is the total number of the current spare parts, and corresponding probability arrays pg, pf and pw are obtained according to the difference of r;
the probability calculation unit of the comprehensive electronic part unit is used for carrying out convolution on the probability pf of pg, n-j-1 electronic part units in a fault state and the probability pw of j electronic part units in a working state for n-1 times, and taking the 1+s element in the convolution result as a probability density function capable of enabling the number of the working units to be j after all electronic parts are consumed by the comprehensive electronic parts; wherein the initial value of j is k; integrating the probability density function with the number j of the workable units on the task time, and calculating the probability p corresponding to the number j of the workable electronic parts;
the initial spare part guarantee probability updating module is used for multiplying the total number of electronic parts n, the probability p corresponding to the number j of the electronic parts capable of working and the combination nj of the number j of the electronic parts capable of working to obtain the first spare part guarantee probability corresponding to the number j of the electronic parts capable of working, and updating the initial spare part guarantee probability by adding the initial spare part guarantee probability and the first spare part guarantee probability corresponding to the number j of the electronic parts capable of working;
The first spare part guarantee probability confirming unit is used for enabling j=j+1 to judge whether j is smaller than or equal to the total number n of the electronic parts, if yes, driving the probability calculating unit of the comprehensive electronic part units to operate, otherwise, taking the initial spare part guarantee probability as the first spare part guarantee probability that the number j of the electronic part units capable of working is larger than or equal to the minimum number k of the working units capable of being accepted.
Further preferably, the calculating module of the second spare part guarantee probability includes:
the probability calculation unit is used for calculating a probability array of the current g-th electronic part unit consuming r spare parts in a normal working state according to the fact that the service lives of the electronic part units obey the exponential distribution and the time probability of the r-time faults of the electronic part units are related to the gamma function; wherein r is more than or equal to 0 and less than or equal to s, and the initial value of g is 1;
the probability calculation unit is used for convolving the calculated probability array of the g-th electronic part unit consuming r spare parts in a normal working state with the probability array of the g-1 electronic part unit consuming l spare parts in the normal working state to obtain the probability array of the g electronic part unit consuming l spare parts in the normal working state;
The second spare part guarantee probability calculation unit is used for updating the next electronic part unit into the current g-th electronic part unit, if the number of g is larger than the total number of the electronic parts, taking the sum of probabilities of the first 1+s comprehensive electronic part units consuming spare parts in a normal working state as the second spare part guarantee probability, and otherwise, driving the probability calculation unit of each electronic part unit consuming spare parts to operate;
and when the electronic voting component task is finished, all the electronic component units are in a normal working state.
Further preferably, the first electronic component unit consumes r spare parts in number, and the probability pg of consuming the last spare part 1+r The method comprises the following steps:
probability pf of other electronics units being in failure state 1+r The method comprises the following steps:
probability pw of other electronic component units being in working state 1+r The method comprises the following steps:
wherein Γ () is a gamma function; x is a working time variable; the electronic part unit life obeys an exponential distribution E (a); t is task time; t is a time variable.
Further preferably, the probability that the current g-th electronic component unit consumes r spare parts in a normal working state is:
wherein Γ () is a gamma function, and the lifetime of the electronic element unit obeys the exponential distribution E (a); t is task time; t is a time variable.
In a third aspect, the present application provides an electronic device, comprising: at least one memory for storing a program; at least one processor for executing a memory-stored program, which when executed is adapted to carry out the method described in the first aspect or any one of the possible implementations of the first aspect.
In a fourth aspect, the present application provides a computer readable storage medium having a computer program stored thereon, which when run on a processor causes the processor to perform the method described in the first aspect or any one of the possible implementations of the first aspect.
In a fifth aspect, the present application provides a computer program product which, when run on a processor, causes the processor to perform the method described in the first aspect or any one of the possible implementations of the first aspect.
It will be appreciated that the advantages of the second to fifth aspects may be found in the relevant description of the first aspect, and are not described here again.
In general, the above technical solutions conceived by the present invention have the following beneficial effects compared with the prior art:
The invention provides a spare part demand prediction method of an electronic voting part, wherein the total spare part guarantee probability is the sum of a first spare part guarantee probability and a second spare part guarantee probability, the first spare part guarantee probability is for all spare parts consumed when an electronic voting part task is finished, but the number of electronic part units capable of working is more than or equal to the minimum number of working units capable of being accepted, the electronic voting part can still work normally, the second spare part guarantee probability is for the spare part guarantee probability when all electronic parts work normally when the electronic voting part task is finished, and the acquired spare part demand is closer to the result of a simulation method through the calculation method of the total spare part guarantee probability.
Drawings
FIG. 1 is a flow chart of a method for predicting spare part demand for an electronic class voting component provided by an embodiment of the present invention;
FIG. 2 is a flowchart of calculating a first spare part assurance probability provided by an embodiment of the present invention;
FIG. 3 is a flowchart of calculating a second spare part assurance probability provided by an embodiment of the present invention;
Fig. 4 is a graph comparing the simulation method provided by the embodiment of the present invention with the spare part demand result of the spare part demand prediction method provided by the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The term "and/or" herein is an association relationship describing an associated object, and means that there may be three relationships, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. The symbol "/" herein indicates that the associated object is or is a relationship, e.g., A/B indicates A or B.
The terms "first" and "second" and the like in the description and in the claims are used for distinguishing between different objects and not for describing a particular sequential order of objects. For example, the first response message and the second response message, etc. are used to distinguish between different response messages, and are not used to describe a particular order of response messages.
In the embodiments of the present application, words such as "exemplary" or "such as" are used to mean serving as examples, illustrations, or descriptions. Any embodiment or design described herein as "exemplary" or "for example" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion.
In the description of the embodiments of the present application, unless otherwise specified, the meaning of "a plurality of" means two or more, for example, a plurality of processing units means two or more processing units and the like; the plurality of elements means two or more elements and the like.
First, technical terms involved in the embodiments of the present application will be described.
The kN voting component consists of n units with the same type, and the kN voting component is regarded as normal working as long as the number of the units which can still work is not lower than k, otherwise, the kN voting component is regarded as faults and can not work continuously.
All n units can operate, often a popular full power operation, with only k units operating an acceptable, minimally derated use.
The life of the electronic part generally obeys an exponential distribution; common electronic components are printed circuit board packages, electronic components, resistors, capacitors, integrated circuits, and the like; random variable obeys an exponential distribution E (mu), mu is the variable mean value and the probability density function
The electronic voting component consists of a plurality of electronic component units of the same type;
when a certain unit of the electronic voting component fails, the replacement maintenance is immediately carried out, and the maintenance time is not counted; the part is maintained by replacement, so that the part is in a working state of full use as much as possible until the task is finished or no spare part can be used during the task, and the part stops working because the number of working units is less than the minimum requirement.
The task time of the known electronic part units in the electronic part voting component is T, the spare part guarantee probability index is Q, the electronic part kN voting component consists of n electronic part units of the same type, the acceptable minimum working unit number is k, the service life of the electronic part units obeys the index distribution E (a), and the electronic part units are immediately replaced and repaired after 1 electronic part unit fault occurs, so that the maintenance time is ignored.
Next, the technical solutions provided in the embodiments of the present application are described.
In one aspect, as shown in fig. 1, the invention provides a spare part demand prediction method of an electronic voting component, which comprises the following steps:
s1: initializing data, namely enabling the total number s=0 of spare parts, and marking the number of electronic part units capable of working when the task of the electronic voting component is finished as j, wherein the initial value of j is the minimum number k of electronic part working units capable of being accepted; and let spare part guarantee probability P s Is 0;
s2: when the task of the electronic voting component is finished, all spare parts are consumed, and the last spare part is used for maintaining the first electronic component unit, but the first electronic component is in a fault state, and the probability p corresponding to the number j of the electronic component units capable of working is calculated; wherein, y (x) is a probability density function with j number of workable units under the condition that all spare parts are consumed and the last spare part is consumed for the first electronic part unit;
the calculation method of y (x), as shown in fig. 2, includes the following steps:
a. when the task of the electronic voting component is finished, the electronic component unit has consumed all spare parts, wherein the first electronic component unit consumes r spare parts, and the first electronic component unit is in a fault state, calculates the r spare parts consumed by the first electronic component unit and the pg of the last spare part consumed according to the index distribution of the service life of the electronic component unit and the correlation of the time probability of r faults of the electronic component with a gamma function 1+r ，0≤r≤s；
Wherein Γ () is a gamma function,
b. when the task of the electronic voting component is finished, the electronic component unit consumes all spare parts, wherein the first unit consumes r spare parts, the first electronic component unit consumes the last spare part, and under the condition that the first electronic component is in a fault state, the probability pf that other units are in the fault state when the task of the electronic voting component is finished is calculated according to the fact that the life compliance index distribution of the electronic component unit and the time probability of r times of faults of the electronic component are related to a gamma function 1+r ，0≤r≤s；
c. When the task of the electronic voting component is finished and is in a normal working state, the electronic component unit consumes all spare parts, wherein the first unit consumes r spare parts, the first electronic component unit consumes the last spare part, and under the condition of being in a fault state, according to the life compliance index distribution of the electronic component unit and the time probability of r times of faults of the electronic component, the probability pw of other electronic components in the working state when the task of the electronic voting component is finished is calculated through correlation with a gamma function 1+r Wherein r is more than or equal to 0 and less than or equal to s, and according to the difference of r, obtaining a probability array pw of other corresponding electronic parts in a working state;
d. the method comprises the steps of carrying out convolution n-1 times on the probability pg that a first electronic part unit consumes r spare parts, the probability pf that a last spare part is consumed, the probability pf that n-j-1 units are in a fault state and the probability pw that j units are in a working state, calculating and integrating 1+s elements pj in a convolution result after all electronic parts consume all spare parts 1+s As a function of the probability density of j being able to work the number of units;
more specifically expressed as follows:
d.1: initializing the probability pj that the number of the electronic part units capable of working is j after the comprehensive electronic part consumes s spare parts as a probability array pg that the first electronic part unit consumes r spare parts and the last spare part; i.e. pj=pg, in=1; wherein in-1 is the convolution number;
d.2: update pj=pj×pw, which is a convolution calculation symbol; wherein pj is a probability array of j of the number of electronic part units capable of working under the condition that the integrated electronic part consumes s spare parts and the first electronic part unit consumes the last spare part;
d.3: updating in=in+1, and if in is less than or equal to j, turning to execute d.2; otherwise reset in=1, go to step d.4;
d.4: update pj=pj×pf, which is a convolution calculation symbol;
d.5: updating in=in+1, judging whether in is less than or equal to n-j-1, if yes, turning to d.4, otherwise outputting y (x) =pj 1+s ；
S3: multiplying the total number of electronic parts n, the probability p corresponding to the number j of the electronic parts capable of working and the combination nj of the number j of the electronic parts capable of working, and updating the spare part guarantee probability, namely: ps=ps+n×nj×p; updating j=j+1, if j is less than or equal to n, executing step S2, otherwise executing step S4;
in more detail, the probability p corresponding to the number j of electronic component units capable of operating is: when the task of the electronic voting component is finished, all spare parts are consumed, the first electronic component unit consumes the last spare part, but the first electronic component unit is in a fault state, and the other electronic components have the probability p that j electronic components are in a normal working state;
The combination nj of the number j of electronic units that can operate is: when the task of the electronic voting component is finished, all spare parts are consumed, the first electronic component unit consumes the last spare part, but the first electronic component unit is in a fault state, and the j electronic components in other electronic components are multiplied by the combination nj in a normal working state
S4: when the task of the electronic voting component is finished and is in a normal working state, calculating the probability pt that all electronic component units can work when the task is finished according to the fact that the service lives of the electronic component units obey the exponential distribution and the time probability that the electronic component fails r times is related to a gamma function; as shown in fig. 3, the method specifically comprises the following steps:
s4.1: calculating probability pd that r spare parts are consumed by the current g-th electronic part unit in normal working state 1+r ，0≤r≤s；
S4.2: calculating a probability array of r spare parts consumed by g-1 electronic part units before synthesis, wherein pj=pd and g=1; otherwise pj=pj×pd, which is a convolution calculation symbol;
s4.3: updating g=g+1, if g is less than or equal to n, turning to S4.1, otherwise, making
S5: and updating the spare part guarantee probability by adopting the probability pt that all the electronic part units can work when the calculation task is finished, enabling the total spare part guarantee probability Ps=Ps+pt, adding 1 to the total number of the spare parts if Ps < Q, and turning to the step S2, otherwise, outputting the total number of the spare parts as the spare part demand S and the corresponding spare part guarantee probability Ps.
In a second aspect, the present invention provides a spare part demand prediction system for an electronic class voting component, comprising:
the initialization module is used for initializing the total number of spare parts to be 0 and setting the guarantee probability of the initial spare parts to be 0;
the calculation module of the first spare part guarantee probability is used for calculating the first spare part guarantee probability that the number j of the electronic part units capable of working is larger than or equal to the minimum number k of the working units capable of being accepted according to the index distribution of the service lives of the electronic part units and the correlation of the time probability of the r times of faults of the electronic part units and the gamma function under the condition that all the spare parts are consumed when the task of the electronic voting part is finished and the first electronic part unit is in a fault state after consuming the last spare part; wherein the initial value of j is k;
the calculation module of the second spare part guarantee probability is used for calculating the probability that all the electronic part units work normally when the task of the electronic voting component ends according to the fact that the service lives of the electronic part units obey the exponential distribution and the time probability that the electronic part units have r times of faults is related to a gamma function, and taking the probability as the second spare part guarantee probability;
the total spare part guarantee probability calculating module is used for adding the first spare part guarantee probability and the second spare part guarantee probability to calculate the total spare part guarantee probability;
The data output module is used for comparing the total spare part guarantee probability with the spare part guarantee probability index, if the total spare part guarantee probability is smaller than the spare part guarantee probability index, the total number of spare parts is increased, the calculation module of the first spare part guarantee probability and the calculation module of the second spare part guarantee probability are driven to operate, and if not, the current total number of spare parts is used as the spare part demand, and the total spare part guarantee probability is output;
the electronic voting component consists of a plurality of electronic component units of the same type.
Further preferably, the calculating module of the first spare part guarantee probability includes:
the probability parameter calculation unit of each electronic component unit is used for sequentially calculating the probability pg of the first electronic component unit consuming r spare parts and consuming the last spare part according to the index distribution of the service life of the electronic component unit and the correlation of the time probability of r times of faults of the electronic component unit and the gamma function under the condition that the first electronic component unit is in a fault state and the electronic component unit is in a fault state when the number of the electronic component units capable of working is larger than or equal to the minimum number of acceptable working units when the task of the electronic class voting component is finished 1+r Probability pf that other electronics units are in a failure state 1+r Probability pw of other electronic component units in working state 1+r The method comprises the steps of carrying out a first treatment on the surface of the Wherein r is more than or equal to 0 and less than or equal to s, s is the total number of the current spare parts, and corresponding probability arrays pg, pf and pw are obtained according to the difference of r;
the probability calculation unit of the comprehensive electronic part unit is used for carrying out convolution on the probability pf of pg, n-j-1 electronic part units in a fault state and the probability pw of j electronic part units in a working state for n-1 times, and taking the 1+s element in the convolution result as a probability density function capable of enabling the number of the working units to be j after all electronic parts are consumed by the comprehensive electronic parts; wherein the initial value of j is k; integrating the probability density function with the number j of the workable units on the task time, and calculating the probability p corresponding to the number j of the workable electronic parts;
the initial spare part guarantee probability updating module is used for multiplying the total number of electronic parts n, the probability p corresponding to the number j of the electronic parts capable of working and the combination nj of the number j of the electronic parts capable of working to obtain the first spare part guarantee probability corresponding to the number j of the electronic parts capable of working, and updating the initial spare part guarantee probability by adding the initial spare part guarantee probability and the first spare part guarantee probability corresponding to the number j of the electronic parts capable of working;
The first spare part guarantee probability confirming unit is used for enabling j=j+1 to judge whether j is smaller than or equal to the total number n of the electronic parts, if yes, driving the probability calculating unit of the comprehensive electronic part units to operate, otherwise, taking the initial spare part guarantee probability as the first spare part guarantee probability that the number j of the electronic part units capable of working is larger than or equal to the minimum number k of the working units capable of being accepted.
Further preferably, the calculating module of the second spare part guarantee probability includes:
the probability calculation unit is used for calculating a probability array of the current g-th electronic part unit consuming r spare parts in a normal working state according to the fact that the service lives of the electronic part units obey the exponential distribution and the time probability of the r-time faults of the electronic part units are related to the gamma function; wherein r is more than or equal to 0 and less than or equal to s, and the initial value of g is 1;
the probability calculation unit is used for convolving the calculated probability array of the g-th electronic part unit consuming r spare parts in a normal working state with the probability array of the g-1 electronic part unit consuming l spare parts in the normal working state to obtain the probability array of the g electronic part unit consuming l spare parts in the normal working state;
The second spare part guarantee probability calculation unit is used for updating the next electronic part unit into the current g-th electronic part unit, if the number of g is larger than the total number of the electronic parts, taking the sum of probabilities of the first 1+s comprehensive electronic part units consuming spare parts in a normal working state as the second spare part guarantee probability, and otherwise, driving the probability calculation unit of each electronic part unit consuming spare parts to operate;
and when the electronic voting component task is finished, all the electronic component units are in a normal working state.
Further preferably, the first electronic component unit consumes r spare parts in number, and the probability pg of consuming the last spare part 1+r The method comprises the following steps:
probability pf of other electronics units being in failure state 1+r The method comprises the following steps:
probability pw of other electronic component units being in working state 1+r The method comprises the following steps:
wherein Γ () is a gamma function,x is a working time variable; the electronic part unit life obeys an exponential distribution E (a); t is task time; t is a time variable.
Further preferably, the probability that the current g-th electronic component unit consumes r spare parts in a normal working state is:
wherein Γ () is a gamma function; the electronic part unit life obeys an exponential distribution E (a); t is task time; t is a time variable.
Examples
The electronic voting component consists of 5 electronic units, the number of the least possible working units is not less than 3, the service life of the electronic units obeys the index distribution E (150), the task time is 200h, the required spare part guarantee probability is not less than 0.9, and the spare part demand at the moment is calculated; the specific implementation process of the spare part demand prediction method of the electronic voting component is as follows:
(1) Initializing the number of spare parts s=0, consuming all spare parts when the task is finished, consuming the last spare part by the first electronic part unit, and recording the number of units which can still work in the initialization as j, wherein j=3;
(2) Let spare part guarantee probability ps=0, calculate probability p that can work unit quantity j corresponds, the result is shown in table 1;
TABLE 1
| j | 3 | 4 |
| p | 0.0050 | 0.0036 |
(3) Updating spare part assurance probability ps=ps+nxnjx p= 0.1171;
(4) Calculating the probability pt=0.0013 that all units can work at the end of the task;
(5) Let the spare part guarantee probability ps=ps+pt= 0.1184, since Ps < Q, the total number of spare parts s=s+1 is updated;
(6) After executing the steps (2) to (5) for a plurality of times, when s=7, ps= 0.9053, and meets the index requirement of not less than 0.9; and stopping calculation, and outputting spare part demand s=7 and corresponding spare part guarantee probability Ps.
In the embodiment, the simulation method is adopted to simulate the spare part guarantee probability results corresponding to the number of each spare part, fig. 4 is a comparison chart of 2 spare part guarantee probability results, wherein the number of the spare parts is 0-7, obtained by adopting the simulation method and the spare part demand prediction method provided by the invention, and fig. 4 shows that the spare part demand prediction method provided by the invention is very consistent with the results of the simulation method.
It should be understood that, the foregoing apparatus is used to perform the method in the foregoing embodiment, and corresponding program modules in the apparatus implement principles and technical effects similar to those described in the foregoing method, and reference may be made to corresponding processes in the foregoing method for the operation of the apparatus, which are not repeated herein.
Based on the method in the above embodiment, an embodiment of the present application provides an electronic device. The apparatus may include: at least one memory for storing programs and at least one processor for executing the programs stored by the memory. Wherein the processor is adapted to perform the method described in the above embodiments when the program stored in the memory is executed.
Based on the method in the above embodiment, the present application provides a computer-readable storage medium storing a computer program, which when executed on a processor, causes the processor to perform the method in the above embodiment.
Based on the methods in the above embodiments, the present application provides a computer program product, which when run on a processor causes the processor to perform the methods in the above embodiments.
It is to be appreciated that the processor in embodiments of the present application may be a central processing unit (centralprocessing unit, CPU), but may also be other general purpose processors, digital signal processors (digital signalprocessor, DSP), application specific integrated circuits (application specific integrated circuit, ASIC), field programmable gate arrays (field programmable gate array, FPGA) or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. The general purpose processor may be a microprocessor, but in the alternative, it may be any conventional processor.
The method steps in the embodiments of the present application may be implemented by hardware, or may be implemented by a processor executing software instructions. The software instructions may be comprised of corresponding software modules that may be stored in random access memory (random access memory, RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present application, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted across a computer-readable storage medium. The computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, fiber optic, digital Subscriber Line (DSL)), or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device including one or more servers, data centers, etc. that can be integrated with the medium. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a Solid State Disk (SSD)), or the like.
It will be appreciated that the various numerical numbers referred to in the embodiments of the present application are merely for ease of description and are not intended to limit the scope of the embodiments of the present application.
It will be readily appreciated by those skilled in the art that the foregoing description is merely a preferred embodiment of the invention and is not intended to limit the invention, but any modifications, equivalents, improvements or alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (6)
1. The spare part demand prediction method of the electronic voting component is characterized by comprising the following steps of:
s1: initializing the total number of spare parts to be 0, and setting the guarantee probability of the initial spare parts to be 0;
s2: when the task of the electronic voting component is finished, all spare parts are consumed, and the first electronic component unit is in a fault state after the last spare part is consumed, calculating first spare part guarantee probability that the number j of the electronic component units capable of working is larger than or equal to the minimum number k of the working units capable of being accepted according to the fact that the service life of the electronic component unit obeys the exponential distribution and the time probability of r times of faults of the electronic component unit is related to a gamma function; wherein the initial value of j is k;
S3: according to the index distribution of the life compliance index of the electronic part units and the correlation of the time probability of r times of faults of the electronic part units with a gamma function, calculating the probability that all the electronic part units work normally when the task of the electronic voting component is finished, and taking the probability as a second spare part guarantee probability;
s4: adding the first spare part guarantee probability and the second spare part guarantee probability, and calculating the total spare part guarantee probability;
s5: comparing the total spare part guarantee probability with the spare part guarantee probability index, and if the total spare part guarantee probability is smaller than the spare part guarantee probability index, increasing the total number of spare parts, and switching to S2; otherwise, taking the total number of the current spare parts as the spare part demand, and outputting the total spare part guarantee probability;
the electronic voting component consists of a plurality of electronic component units of the same type;
s2 specifically comprises the following steps:
s2.1: when the task of the electronic voting component is finished, under the condition that the number of the electronic component units capable of working is larger than or equal to the minimum number of working units capable of being accepted, the electronic component units consume all spare parts, and the first electronic component unit is in a fault state, sequentially calculating the number r of the spare parts consumed by the first electronic component unit and the probability pg of the last spare part consumed according to the index distribution of the service life of the electronic component unit and the time probability of the r times of faults of the electronic component and the gamma function 1+r Probability pf that other electronics units are in a failure state 1+r Probability pw of other electronic component units in working state 1+r The method comprises the steps of carrying out a first treatment on the surface of the Wherein r is more than or equal to 0 and less than or equal to s, s is the total number of the current spare parts, and corresponding probability arrays pg, pf and pw are obtained according to the difference of r;
s2.2: carrying out convolution on the probability pf that pg, n-j-1 electronic part units are in a fault state and the probability pw that j electronic part units are in a working state for n-1 times, and taking the 1+s-th element in the convolution result as a probability density function with the number j of the working units after all electronic parts are consumed by the comprehensive electronic parts; wherein the initial value of j is k;
s2.3: integrating the probability density function with the number j of the workable units on the task time, and calculating the probability p corresponding to the number j of the workable electronic parts;
s2.4: multiplying the total number n of the electronic parts, the probability p corresponding to the number j of the electronic parts capable of working and the combination nj of the number j of the electronic parts capable of working to obtain a first spare part guarantee probability corresponding to the number j of the electronic parts capable of working, and adding and updating the initial spare part guarantee probability by adopting the initial spare part guarantee probability and the first spare part guarantee probability corresponding to the number j of the electronic parts capable of working;
S2.5: let j=j+1, judge j is less than or equal to the total number n of electronic components, if yes, go to S2.2, otherwise, regard initial spare part guarantee probability as the first spare part guarantee probability that the number j of electronic component units capable of working is greater than or equal to the number k of minimum working units capable of being accepted;
s3, a method for calculating the second spare part guarantee probability comprises the following steps:
s3.1: according to the index distribution of the life compliance index of the electronic part unit and the correlation of the time probability of r faults of the electronic part unit and the gamma function, calculating a probability array pd of the current g-th electronic part unit consuming r spare parts in a normal working state; wherein r is more than or equal to 0 and less than or equal to s, and the initial value of g is 1;
s3.2: convolving the calculated probability array of the g-th electronic part unit consuming r spare parts in a normal working state with the probability array of the g-1 electronic part unit consuming l spare parts in the normal working state before synthesis to obtain the probability array of the g electronic part unit consuming l spare parts in the normal working state before synthesis;
s3.3: updating the next electronic part unit into the current g-th electronic part unit, and taking the sum of the probabilities of consuming spare parts of the first 1+s comprehensive electronic part units in a normal working state as a second spare part guarantee probability if the number of g is larger than the total number of electronic parts; otherwise, turning to S3.1;
And when the electronic voting component task is finished, all the electronic component units are in a normal working state.
2. The spare part demand prediction method according to claim 1, wherein the first electronic part unit consumes r spare part numbers and the probability pg of consuming the last spare part 1+r The method comprises the following steps:
probability pf of other electronics units being in failure state 1+r The method comprises the following steps:
probability pw of other electronic component units being in working state 1+r The method comprises the following steps:
wherein Γ () is a gamma function; x is a working time variable; the electronic part unit life obeys an exponential distribution E (a); t is task time; t is a time variable.
3. The spare part demand prediction method according to claim 1, wherein the probability that the current g-th electronic part unit consumes r spare parts in a normal operation state is:
wherein Γ () is a gamma function, and the lifetime of the electronic element unit obeys the exponential distribution E (a); t is task time; t is a time variable.
4. A spare part demand prediction system for an electronic class voting component, comprising:
the initialization module is used for initializing the total number of spare parts to be 0 and setting the guarantee probability of the initial spare parts to be 0;
the calculation module of the first spare part guarantee probability is used for calculating the first spare part guarantee probability that the number j of the electronic part units capable of working is larger than or equal to the minimum number k of the working units capable of being accepted according to the index distribution of the service lives of the electronic part units and the correlation of the time probability of the r times of faults of the electronic part units and the gamma function under the condition that all the spare parts are consumed when the task of the electronic voting part is finished and the first electronic part unit is in a fault state after consuming the last spare part; wherein the initial value of j is k;
The calculation module of the second spare part guarantee probability is used for calculating the probability that all the electronic part units work normally when the task of the electronic voting component ends according to the fact that the service lives of the electronic part units obey the exponential distribution and the time probability that the electronic part units have r times of faults is related to a gamma function, and taking the probability as the second spare part guarantee probability;
the total spare part guarantee probability calculating module is used for adding the first spare part guarantee probability and the second spare part guarantee probability to calculate the total spare part guarantee probability;
the data output module is used for comparing the total spare part guarantee probability with the spare part guarantee probability index, if the total spare part guarantee probability is smaller than the spare part guarantee probability index, the total number of spare parts is increased, the calculation module of the first spare part guarantee probability and the calculation module of the second spare part guarantee probability are driven to operate, and if not, the current total number of spare parts is used as the spare part demand, and the total spare part guarantee probability is output;
the electronic voting component consists of a plurality of electronic component units of the same type;
the calculation module of the first spare part guarantee probability comprises:
the probability parameter calculation unit of each electronic part unit is used for calculating the probability parameter of each electronic part unit when the task of the electronic voting part is finished, and when the number of the electronic part units capable of working is more than or equal to the minimum number of acceptable working units, the electronic part units consume all spare parts, and the first electronic part unit is in a fault state, according to the life compliance index distribution of the electronic part units and the fact that the electronic part is subjected to r times of failure The time probability of the barrier is related to the gamma function, and the probability pg of the first electronic component unit consuming r spare parts and the last spare part is calculated in sequence 1+r Probability pf that other electronics units are in a failure state 1+r Probability pw of other electronic component units in working state 1+r The method comprises the steps of carrying out a first treatment on the surface of the Wherein r is more than or equal to 0 and less than or equal to s, s is the total number of the current spare parts, and corresponding probability arrays pg, pf and pw are obtained;
the probability calculation unit of the comprehensive electronic part unit is used for carrying out convolution on the probability pf of pg, n-j-1 electronic part units in a fault state and the probability pw of j electronic part units in a working state for n-1 times, and taking the 1+s element in the convolution result as a probability density function capable of enabling the number of the working units to be j after all electronic parts are consumed by the comprehensive electronic parts; wherein the initial value of j is k; integrating the probability density function with the number j of the workable units on the task time, and calculating the probability p corresponding to the number j of the workable electronic parts;
the initial spare part guarantee probability updating module is used for multiplying the total number of electronic parts n, the probability p corresponding to the number j of the electronic parts capable of working and the combination nj of the number j of the electronic parts capable of working to obtain the first spare part guarantee probability corresponding to the number j of the electronic parts capable of working, and updating the initial spare part guarantee probability by adding the initial spare part guarantee probability and the first spare part guarantee probability corresponding to the number j of the electronic parts capable of working;
The first spare part guarantee probability confirming unit is used for enabling j=j+1 to judge whether j is smaller than or equal to the total number n of the electronic parts, if yes, driving a probability calculating unit of the comprehensive electronic part units to operate, otherwise, taking the initial spare part guarantee probability as a first spare part guarantee probability that the number j of the electronic part units capable of working is larger than or equal to the minimum number k of the working units capable of being accepted;
the calculation module of the second spare part guarantee probability comprises:
the probability calculation unit is used for calculating a probability array of the current g-th electronic part unit consuming r spare parts in a normal working state according to the fact that the service lives of the electronic part units obey the exponential distribution and the time probability of the r-time faults of the electronic part units are related to the gamma function; wherein r is more than or equal to 0 and less than or equal to s, and the initial value of g is 1;
the probability calculation unit is used for convolving the calculated probability array of the g-th electronic part unit consuming r spare parts in a normal working state with the probability array of the g-1 electronic part unit consuming l spare parts in the normal working state to obtain the probability array of the g electronic part unit consuming l spare parts in the normal working state;
The second spare part guarantee probability calculation unit is used for updating the next electronic part unit into the current g-th electronic part unit, if the number of g is larger than the total number of the electronic parts, taking the sum of probabilities of the first 1+s comprehensive electronic part units consuming spare parts in a normal working state as the second spare part guarantee probability, and otherwise, driving the probability calculation unit of each electronic part unit consuming spare parts to operate;
and when the electronic voting component task is finished, all the electronic component units are in a normal working state.
5. The spare part demand prediction system according to claim 4, wherein the first electronic part unit consumes r spare part numbers and the probability pg of consuming the last spare part 1+r The method comprises the following steps:
probability pf of other electronics units being in failure state 1+r The method comprises the following steps:
probability pw of other electronic component units being in working state 1+r The method comprises the following steps:
wherein Γ () is a gamma function, x is a working time variable; the electronic part unit life obeys an exponential distribution E (a); t is task time; t is a time variable.
6. The spare part demand prediction system according to claim 4, wherein the probability that the current g-th electronic part unit consumes r spare parts in a normal operation state is:
Wherein Γ () is a gamma function, and the lifetime of the electronic element unit obeys the exponential distribution E (a); t is task time; t is a time variable.
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| CN110598363A (en) * | 2019-09-30 | 2019-12-20 | 青岛航讯网络技术服务有限公司 | Voting component spare part amount calculation method, voting component spare part amount simulation method, voting component terminal, and storage medium |
| CN115330045A (en) * | 2022-08-10 | 2022-11-11 | 北京航空航天大学 | Aircraft state-based dynamic spare part planning method and system |
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| CN106874238A (en) * | 2017-01-20 | 2017-06-20 | 中国人民解放军海军工程大学 | A kind of computational methods of Weibull type unit spare parts demand amount |
| CN110598363A (en) * | 2019-09-30 | 2019-12-20 | 青岛航讯网络技术服务有限公司 | Voting component spare part amount calculation method, voting component spare part amount simulation method, voting component terminal, and storage medium |
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