CN114925895A - Maintenance equipment prediction method, terminal and storage medium - Google Patents

Maintenance equipment prediction method, terminal and storage medium Download PDF

Info

Publication number
CN114925895A
CN114925895A CN202210508203.6A CN202210508203A CN114925895A CN 114925895 A CN114925895 A CN 114925895A CN 202210508203 A CN202210508203 A CN 202210508203A CN 114925895 A CN114925895 A CN 114925895A
Authority
CN
China
Prior art keywords
consumption
maintenance equipment
sequence
formula
equipment
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202210508203.6A
Other languages
Chinese (zh)
Inventor
李万领
孙江生
王正军
吕艳梅
张连武
连光耀
王韶光
王宁
李会杰
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
32181 Troops of PLA
Original Assignee
32181 Troops of PLA
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 32181 Troops of PLA filed Critical 32181 Troops of PLA
Priority to CN202210508203.6A priority Critical patent/CN114925895A/en
Publication of CN114925895A publication Critical patent/CN114925895A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • G06F17/12—Simultaneous equations, e.g. systems of linear equations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/067—Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/80—Management or planning

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Physics & Mathematics (AREA)
  • Strategic Management (AREA)
  • General Physics & Mathematics (AREA)
  • Economics (AREA)
  • Theoretical Computer Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Operations Research (AREA)
  • General Business, Economics & Management (AREA)
  • Mathematical Physics (AREA)
  • Tourism & Hospitality (AREA)
  • Marketing (AREA)
  • Development Economics (AREA)
  • Quality & Reliability (AREA)
  • Pure & Applied Mathematics (AREA)
  • Game Theory and Decision Science (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Educational Administration (AREA)
  • Data Mining & Analysis (AREA)
  • Finance (AREA)
  • Algebra (AREA)
  • Accounting & Taxation (AREA)
  • Databases & Information Systems (AREA)
  • Software Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention relates to the technical field of maintenance equipment inventory prediction, in particular to a maintenance equipment prediction method, a terminal and a storage medium. In the embodiment of the invention, different prediction models are selected according to the existing consumption sequence, so that the accuracy of prediction can be ensured, and the problem of repeated dispatching of maintenance equipment caused by unreasonable prediction is solved. According to the maintenance equipment prediction method, when the demand interval characteristic is common maintenance equipment, the demand is predicted through the grey prediction model, and after prediction is finished, the prediction precision is verified through precision inspection, so that the prediction accuracy of the method provided by the embodiment of the invention can be ensured.

Description

Maintenance equipment prediction method, terminal and storage medium
Technical Field
The invention relates to the technical field of maintenance equipment inventory prediction, in particular to a maintenance equipment prediction method, a terminal and a storage medium.
Background
The major exercise drill is a drill of a command and action conducted under the guidance of a planned situation, is a comprehensive training similar to actual combat, and is implemented after theoretical learning and basic training are completed, and is a high-level stage of the major exercise.
The organization and implementation of exercises embody a high level of art. Firstly, the organization is very complicated. Major exercise exercises are combined exercises of multiple kinds of work, and the workload of organization coordination and mutual cooperation is quite heavy. Requiring the commander to be clear-headed and react with the disease. Secondly, accidental injury accidents are easy to happen. Accidental injury of personnel in the performance of major activities is difficult to avoid. In order to prevent accidents, careful plans and sufficient preparation are required; a strict discipline is required; the synergy is repeatedly organized; various security measures are to be enacted. Only in this way can the exercise effect be guaranteed, and accidents can be prevented.
Proper control of the inventory quantity of maintenance equipment is an important part of the preparation work for major event exercises. Accurate demand forecasting is a prerequisite for scientific and reasonable maintenance of equipment inventory. Based on this, it is necessary to develop and design a repair equipment prediction method.
Disclosure of Invention
The embodiment of the invention provides a method, a terminal and a storage medium for predicting maintenance equipment, which are used for solving the problem that the requirement of the maintenance equipment is unreasonable in prediction so that the maintenance equipment needs to be repeatedly dispatched and transported in the prior art.
In a first aspect, an embodiment of the present invention provides a repair equipment prediction method, including:
acquiring a consumption sequence of the maintenance equipment, wherein the consumption sequence is used for representing the characteristic that the demand of the maintenance equipment changes along with time;
determining a demand interval characteristic according to the consumption sequence;
selecting a maintenance equipment consumption prediction model according to the demand interval characteristic;
and inputting the consumption sequence into the equipment consumption prediction model to obtain the predicted demand of the maintenance equipment.
In one possible implementation manner, the determining the demand interval characteristic according to the consumption sequence includes:
calculating and obtaining an average demand interval and a variation coefficient of a non-zero demand value according to the consumption sequence, wherein the variation coefficient of the non-zero demand value is determined by a first formula, and the first formula is as follows:
Figure BDA0003636915440000021
in the formula, CV () is a variation coefficient of a non-zero requirement value, x is a consumption sequence of maintenance equipment, mu is an average value of average requirement intervals of the maintenance equipment, and sigma is a standard deviation of the requirement intervals of the maintenance equipment;
and determining the requirement interval characteristic of the maintenance equipment according to the average requirement interval and the variation coefficient of the non-zero requirement value.
In one possible implementation, the demand interval characteristic includes: the method comprises the following steps of selecting a maintenance equipment consumption prediction model according to the demand interval characteristic, wherein the maintenance equipment consumption prediction model comprises the following steps:
selecting a gray prediction model for common maintenance equipment, wherein the construction step of the gray prediction model comprises the following steps:
performing 1-AGO conversion on the consumption sequence of the maintenance equipment to obtain a first-order accumulated maintenance equipment consumption data sequence;
obtaining a parameter list according to the consumption sequence of the maintenance equipment and the first-order accumulated maintenance equipment consumption data sequence;
and substituting the parameter list into a whitening equation to obtain a maintenance equipment consumption prediction model.
In one possible implementation manner, the performing 1-AGO transformation on the consumption sequence of the maintenance equipment to obtain a first-order accumulated consumption data sequence of the maintenance equipment includes:
establishing a maintenance equipment consumption original data sequence according to the consumption sequence of the maintenance equipment, wherein the maintenance equipment consumption original data sequence is as follows:
X 0 (t)＝{x 0 (1)，x 0 (2)，x 0 (3)，...，x 0 (t)}
in the formula, X 0 () Consuming the original data sequence for maintenance of the equipment, t being the time, x 0 (t) maintenance equipment consumption at time t;
calculating to obtain the first-order accumulated maintenance equipment consumption data sequence according to the maintenance equipment consumption original data sequence, wherein the first-order accumulated maintenance equipment consumption data sequence is as follows:
X 1 (t)＝{x 1 (1)，x 1 (2)，x 1 (3)，...，x 1 (t)}
in the formula, X 1 () For a first order accumulation of maintenance equipment consumption data sequences, x 1 () And calculating according to a second formula, wherein the second formula is as follows:
Figure BDA0003636915440000031
in one possible implementation manner, after the calculating and obtaining the first-order accumulated maintenance equipment consumption data sequence according to the maintenance equipment consumption original data sequence, the method further includes a step of checking applicability, where the step of applicability includes:
calculating to obtain a grade ratio according to the first-order accumulated maintenance equipment consumption data sequence and a third formula, wherein the third formula is as follows:
Figure BDA0003636915440000032
where ρ () is the step ratio;
determining a fourth formula according to the level ratio, and determining whether the maintenance equipment consumption original data sequence meets the fourth formula, wherein the fourth formula is as follows:
Figure BDA0003636915440000033
and if the original data sequence of the consumption of the maintenance equipment does not meet the fourth formula, reselecting the consumption prediction model of the maintenance equipment.
In one possible implementation, the obtaining a parameter list according to the sequence of consumption of the maintenance equipment and the sequence of first order accumulated maintenance equipment consumption data includes:
obtaining a parameter list according to the original data sequence of the maintenance equipment consumption, the first-order accumulated maintenance equipment consumption data sequence and a fifth formula, wherein the fifth formula is as follows:
Figure BDA0003636915440000041
Figure BDA0003636915440000042
Y N ＝[x 0 (2)，x 0 (3)，...，x 0 (n)] T
in the formula (I), the compound is shown in the specification,
Figure BDA0003636915440000043
is a parameter column, a is a first parameter, b is a second parameter。
In one possible implementation, the substituting the parameter column into a whitening equation to obtain a maintenance equipment consumption prediction model includes:
the maintenance equipment consumption prediction model is as follows:
Figure BDA0003636915440000044
Figure BDA0003636915440000045
in the formula (I), the
Figure BDA0003636915440000046
Demand is predicted for servicing the equipment.
In one possible implementation, after the inputting the consumption sequence into the equipment consumption prediction model to obtain the predicted demand of the maintenance equipment, the method includes:
a model precision inspection step, wherein the model precision inspection step comprises the following steps:
obtaining a prediction model precision variance ratio and a small error probability according to the consumption sequence, the maintenance equipment consumption prediction model and a sixth formula, wherein the sixth formula is as follows:
Figure BDA0003636915440000047
Figure BDA0003636915440000048
Figure BDA0003636915440000049
Figure BDA00036369154400000410
Figure BDA00036369154400000411
Figure BDA0003636915440000051
in the formula, S 1 Is the mean square error of the original data sequence, S 2 Is the mean square error of the residual error, C is the prediction model precision variance ratio, and the user is the small error probability,
Figure BDA0003636915440000052
predicting demand of the maintenance equipment at the time t obtained by inputting the consumption sequence into a maintenance equipment consumption prediction model;
and determining the precision grade of the maintenance equipment consumption prediction model according to the precision variance ratio of the prediction model, the small error probability and a threshold value.
In a second aspect, an embodiment of the present invention provides a terminal, including a memory and a processor, where the memory stores a computer program operable on the processor, and the processor executes the computer program to implement the steps of the method according to the first aspect or any possible implementation manner of the first aspect.
In a third aspect, the present invention provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the steps of the method according to the first aspect or any one of the possible implementation manners of the first aspect.
Compared with the prior art, the implementation mode of the invention has the following beneficial effects:
the embodiment of the invention discloses a maintenance equipment prediction method, which comprises the steps of firstly obtaining a consumption sequence of maintenance equipment, then determining the required interval characteristic of the maintenance equipment according to the consumption sequence, then selecting a prediction model according to the interval characteristic, and obtaining the consumption prediction of the maintenance equipment according to the prediction model. In the embodiment of the invention, different prediction models are selected according to the existing consumption sequence, so that the accuracy of prediction can be ensured, and the problem of repeated dispatching of maintenance equipment caused by unreasonable prediction is solved.
According to the maintenance equipment prediction method, when the demand interval characteristic is common maintenance equipment, the demand is predicted through the grey prediction model, and after prediction is finished, the prediction precision is verified through precision inspection, so that the prediction accuracy of the method provided by the embodiment of the invention can be ensured.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.
FIG. 1 is a flow chart of a method for predicting repair equipment provided by an embodiment of the present invention;
FIG. 2 is a schematic diagram of a classification of interval characteristics of maintenance equipment according to an embodiment of the present invention
FIG. 3 is a functional block diagram of a service equipment prediction device provided by an embodiment of the present invention;
fig. 4 is a functional block diagram of a terminal according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the following description is made with reference to the accompanying drawings.
The following is a detailed description of the embodiments of the present invention, which is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Fig. 1 is a flowchart of a repair equipment prediction method according to an embodiment of the present invention.
As shown in fig. 1, it shows a flowchart of an implementation of a repair equipment prediction method provided in an embodiment of the present invention, which is detailed as follows:
in step 101, a consumption sequence of the maintenance equipment is obtained, wherein the consumption sequence is used for representing the characteristic of the change of the demand of the maintenance equipment along with the time.
Illustratively, the consumption sequence of the maintenance equipment is a list of the consumption amount listed for a specific maintenance equipment according to the sequence of time in an application scenario.
In step 102, a demand interval characteristic is determined from the consumption sequence.
In some embodiments, the step 102 comprises:
calculating the coefficient of variation of the obtained average demand interval and the non-zero demand value according to the consumption sequence, wherein the coefficient of variation of the non-zero demand value is determined by a first formula, and the first formula is as follows:
Figure BDA0003636915440000071
in the formula, CV () is a variation coefficient of a non-zero requirement value, x is a consumption sequence of maintenance equipment, mu is an average value of average requirement intervals of the maintenance equipment, and sigma is a standard deviation of the requirement intervals of the maintenance equipment;
and determining the requirement interval characteristic of the maintenance equipment according to the average requirement interval and the variation coefficient of the non-zero requirement value.
The interval characteristic is a characteristic reflecting the frequency of consumption of the maintenance equipment, and in one classification method, it is classified into a common maintenance equipment and an uncommon maintenance equipment. For example, the time interval between the common maintenance equipments is short and the interval is stable, while the time interval between the uncommon maintenance equipments is long and the interval is unstable.
In a method of determining a demand interval characteristic, which classifies the demand interval characteristic using an average demand interval and a coefficient of variation of a non-zero demand value, the coefficient of variation of the non-zero demand value is determined according to the following equation:
Figure BDA0003636915440000072
in the formula, CV () is a coefficient of variation of a non-zero requirement value, x is a consumption sequence of the maintenance equipment, μ is a mean value of an average requirement interval of the maintenance equipment, and σ is a standard deviation of the requirement interval of the maintenance equipment.
After the variation coefficients of the average demand interval and the non-zero demand value are obtained, the demand interval characteristics can be classified according to a threshold, and the classification is performed according to a classification chart shown in fig. 2 in an application scene:
in the figure, ADI (average demand interval) is an average demand interval, and CV (coefficient of variation) is a coefficient of variation of a non-zero demand value, reflecting the intensity of variation of demand. As ADI increases, the discontinuity in demand tends to be significant; dependent CV 2 (x) The demand tends to be unstable. When ADI>1.32, the demand has a significant discontinuity when>At 0.49, the demand has significant instability. In the figure, part A is the unstable requirement, part B is the smooth requirement, part C is the block requirement, and part D is the discontinuous requirement. And after the analysis of the requirement interval characteristics, the maintenance equipment classified into the part B is common maintenance equipment, and the maintenance equipment classified into the part D is not common maintenance equipment.
In addition, a small amount of maintenance equipment has stable annual demand and obvious variation trend, the spare parts can be independently classified into one type, and a more accurate prediction result can be obtained by applying a simple exponential smoothing method. Among them, according to the results of the previous studies, when ADI is less than 1.25, the demand is considered to be relatively stable.
In step 103, a maintenance equipment consumption prediction model is selected according to the demand interval characteristic.
In some embodiments, the demand interval characteristics include: the method comprises the following steps of selecting a maintenance equipment consumption prediction model according to the demand interval characteristic, wherein the maintenance equipment consumption prediction model comprises the following steps:
selecting a gray prediction model for common maintenance equipment, wherein the construction step of the gray prediction model comprises the following steps:
carrying out 1-AGO conversion on the consumption sequence of the maintenance equipment to obtain a first-order accumulated maintenance equipment consumption data sequence;
obtaining a parameter list according to the consumption sequence of the maintenance equipment and the first-order accumulated maintenance equipment consumption data sequence;
and substituting the parameter list into a whitening equation to obtain a maintenance equipment consumption prediction model.
In some embodiments, said performing a 1-AGO transformation on the consumption sequence of the service equipment to obtain a first order cumulative service equipment consumption data sequence comprises:
establishing a maintenance equipment consumption original data sequence according to the consumption sequence of the maintenance equipment, wherein the maintenance equipment consumption original data sequence is as follows:
X 0 (t)＝{x 0 (1)，x 0 (2)，x 0 (3)，...，x 0 (t)}
in the formula, X 0 () Consuming the original data sequence for maintenance of the equipment, t being the time, x 0 (t) maintenance equipment consumption at time t;
calculating to obtain the first-order accumulated maintenance equipment consumption data sequence according to the maintenance equipment consumption original data sequence, wherein the first-order accumulated maintenance equipment consumption data sequence is as follows:
X 1 (t)＝{x 1 (1)，x 1 (2)，x 1 (3)，...，x 1 (t)}
in the formula, X 1 () For first order accumulation of maintenance equipment consumption data sequences, x 1 () And calculating according to a second formula, wherein the second formula is as follows:
Figure BDA0003636915440000081
in some embodiments, after said calculating from said service equipment consumption raw data sequence to obtain said first order accumulated service equipment consumption data sequence, further comprises checking for suitability, said step of suitability comprising:
calculating to obtain a grade ratio according to the first-order accumulated maintenance equipment consumption data sequence and a third formula, wherein the third formula is as follows:
Figure BDA0003636915440000091
where ρ () is the step ratio;
determining a fourth formula according to the level ratio, and determining whether the maintenance equipment consumption original data sequence meets the fourth formula, wherein the fourth formula is as follows:
Figure BDA0003636915440000092
and if the original data sequence of the consumption of the maintenance equipment does not meet the fourth formula, reselecting the consumption prediction model of the maintenance equipment.
In some embodiments, obtaining a parameter list based on the sequence of service equipment consumptions and the first order cumulative service equipment consumption data sequence comprises:
obtaining a parameter list according to the original data sequence of the maintenance equipment consumption, the first-order accumulated maintenance equipment consumption data sequence and a fifth formula, wherein the fifth formula is as follows:
Figure BDA0003636915440000093
Figure BDA0003636915440000094
Y N ＝[x 0 (2)，x 0 (3)，...，x 0 (n)] T
in the formula (I), the compound is shown in the specification,
Figure BDA0003636915440000095
is a parameter sequence, a is a first parameter, and b is a second parameter.
In some embodiments, substituting the parameter list into a whitening equation to obtain a maintenance equipment consumption prediction model comprises:
the maintenance equipment consumption prediction model is as follows:
Figure BDA0003636915440000101
Figure BDA0003636915440000102
in the formula (I), the
Figure BDA0003636915440000103
Demand is predicted for servicing the equipment.
In some embodiments, after said inputting said consumption sequence into said equipment consumption prediction model to obtain a predicted demand for maintenance equipment, comprises:
a model precision inspection step, wherein the model precision inspection step comprises the following steps:
obtaining a prediction model precision variance ratio and a small error probability according to the consumption sequence, the maintenance equipment consumption prediction model and a sixth formula, wherein the sixth formula is as follows:
Figure BDA0003636915440000104
Figure BDA0003636915440000105
Figure BDA0003636915440000106
Figure BDA0003636915440000107
Figure BDA0003636915440000108
Figure BDA0003636915440000109
in the formula, S 1 Is the mean square error of the original data sequence, S 2 Is the mean square error of the residual error, C is the prediction model precision variance ratio, and the user is the small error probability,
Figure BDA00036369154400001010
predicting demand of the maintenance equipment at the time t obtained by inputting the consumption sequence into a maintenance equipment consumption prediction model;
and determining the precision grade of the maintenance equipment consumption prediction model according to the precision variance ratio of the prediction model, the small error probability and a threshold value.
For various reasons, the statistics of the consumption data of the maintenance equipment in the major exercise also has the problems of small relative sample data amount and low information accuracy, and the randomness and the fuzziness of equipment faults are added, so that the equipment maintenance equipment guarantee system has obvious gray characteristics. For the common maintenance equipment among them, the grey theory has considerable adaptability in its demand prediction.
The grey system theory is a method for researching uncertainty problems of few data and poor information. The gray system is a system in which information is partially clear and partially ambiguous, and a gray prediction is formed by applying a gray system theory to a prediction. At present, due to various reasons, the problems of small relative sample data amount and low information accuracy exist in the consumption data statistics of the maintenance equipment in major exercises, and due to randomness and fuzziness of equipment faults, the equipment maintenance equipment guarantee system has obvious gray characteristics, so that the gray theory has considerable adaptability in the demand prediction of the maintenance equipment.
The grey prediction is to establish a quantitative relation between the current time axis and the future, predict the development of things through the quantitative relation, and establish a GM model extended from the past to the future according to the known or uncertain information so as to determine the change trend of the future development of the system and provide a basis for planning decision. Through the processing of original data and the establishment of a gray model, the development rule of the system is discovered and mastered, and scientific quantitative prediction is made for the future of the system.
For the consumption sequence of the maintenance equipment, the consumption sequence should be arranged in sequence according to the time when the consumption is generated, and an original data sequence of the consumption of the maintenance equipment is established, wherein the expression is as follows:
x 0 (t)＝{x 0 (1)，x 0 (2)，x 0 (3)，...，x 0 (t)}
in the formula, X 0 () Consuming the original data sequence for maintenance of the equipment, t being the time, x 0 (t) is the maintenance equipment consumption at time t.
Then, carrying out 1-AGO conversion on the original data sequence consumed by the maintenance equipment to obtain a first-order accumulated maintenance equipment consumed data sequence:
X 1 (t)＝{x 1 (1)，x 1 (2)，x 1 (3)，...，x 1 (n)}
wherein the content of the first and second substances,
Figure BDA0003636915440000111
after obtaining the first order accumulated maintenance equipment consumption data sequence, it is necessary to check the applicability of the gray model, one applicability algorithm is to detect X 0 () Whether it is a smooth sequence, it first obtains the step ratio:
Figure BDA0003636915440000112
then, it is determined whether it is a smooth sequence according to the following formula:
Figure BDA0003636915440000113
Figure BDA0003636915440000121
if the original data sequence consumed by the maintenance equipment does not satisfy the above formula, indicating that it is not applicable to the gray model, the prediction model should be reselected. It can be shown that if X 0 And (t) the quasi-smooth sequence, wherein the sequence generated by the first-order accumulation has an exponential law.
For modeling the consumption data sequence of the maintenance equipment, a parameter column is firstly determined, wherein the parameter column is a necessary step for modeling, and the expression of the parameter column is as follows:
Figure BDA0003636915440000122
wherein the content of the first and second substances,
Figure BDA0003636915440000123
Y N ＝[x 0 (2)，x 0 (3)，...，x 0 (n)] T
from the obtained parameter list, the GM (1, 1) model can be built:
Figure BDA0003636915440000124
substituting differential operation for differentiation, substituting the parameter vector into a GM (1, 1) model, solving time response, and obtaining the prediction of first-order accumulated maintenance equipment consumption data:
Figure BDA0003636915440000125
and then, reducing through a reduction model to obtain the predicted demand of the maintenance equipment:
Figure BDA0003636915440000126
in some application scenarios, the method further comprises a model precision checking step, after the predicted value is solved, the precision of the prediction model is checked, the precision of the prediction model is controlled by a variance ratio C and a small error probability P, and the calculation formula is as follows:
Figure BDA0003636915440000127
Figure BDA0003636915440000128
Figure BDA0003636915440000129
Figure BDA0003636915440000131
Figure BDA0003636915440000132
Figure BDA0003636915440000133
in the formula, S 1 Is the mean square error of the original data sequence, S 2 Is the mean square error of the residual, C is the prediction model precision variance ratio, P is the small error probability,
Figure BDA0003636915440000134
the demand is predicted for the maintenance equipment at time t obtained by inputting the consumption sequence into the maintenance equipment consumption prediction model.
In some application scenarios, the model accuracy is determined from the variance ratio C and the small error probability P in combination with table 1:
TABLE 1 model accuracy determination Table
Grade P C
I >0.95 ＜0.35
II >0.80 ＜0.45
III >0.70 ＜0.50
IV ≤0.70 ≥0.65
In step 104, the consumption sequence is input into the equipment consumption prediction model to obtain the predicted demand for maintenance of the equipment.
For example, after the model is obtained through the above steps, the consumption sequence of the maintenance equipment can be input into the model, and the predicted demand of the maintenance equipment can be obtained.
The following description is directed to a specific application.
The consumption situation of the major exercise sudden repair equipment is selected as a research object of experimental analysis, relevant information is shown in table 2, consumption data of the repair equipment in 8 major exercises from 2005 to 2014 is used as a training sample, consumption data of the first 7 exercises is used as a testing sample, and consumption data of the 8 th exercise is used as a testing sample.
TABLE 2 exercise maintenance equipment consumption data
Figure BDA0003636915440000135
Figure BDA0003636915440000141
Figure BDA0003636915440000151
Firstly, ADI and CV are calculated according to the data of the first seven times of exercises 2 On the basis, the method is comprehensively applied, and the prediction result of the consumption of the exercise maintenance equipment in 2014 is obtained.
To illustrate the necessity of using different prediction methods for different types of maintenance equipment, table 3 shows the results obtained by applying the comprehensive prediction scheme and using one prediction method for each of all maintenance equipment. It can be seen from the table that the prediction accuracy of the comprehensive prediction scheme (based on analysis interval characteristics and then selecting the grey prediction model) is significantly better than that of each independent prediction method.
TABLE 3 comprehensive prediction scheme and corresponding results obtained by applying one prediction method to all maintenance equipment respectively
Figure BDA0003636915440000152
Figure BDA0003636915440000161
The embodiment of the maintenance equipment prediction method comprises the steps of firstly obtaining a consumption sequence of maintenance equipment, then determining the required interval characteristic of the maintenance equipment according to the consumption sequence, then selecting a prediction model according to the interval characteristic, and obtaining the consumption prediction of the maintenance equipment according to the prediction model. In the embodiment of the invention, different prediction models are selected according to the existing consumption sequence, so that the accuracy of prediction can be ensured, and the problem of repeated dispatching of maintenance equipment caused by unreasonable prediction is solved.
According to the maintenance equipment prediction method, when the requirement interval characteristic is common maintenance equipment, the demand is predicted through the grey prediction model, and after prediction is finished, the prediction precision is verified through precision inspection, so that the prediction accuracy of the method provided by the embodiment of the invention can be ensured.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply any order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
The following are embodiments of the apparatus of the invention, and for details not described in detail therein, reference may be made to the corresponding method embodiments described above.
Fig. 3 is a functional block diagram of a service equipment prediction device according to an embodiment of the present invention, and referring to fig. 3, the service equipment prediction device 3 includes: a consumption sequence acquisition module 301, an interval characteristic determination module 302, a model selection module 303, and a prediction determination module 304.
A consumption sequence acquiring module 301, configured to acquire a consumption sequence of the maintenance equipment, where the consumption sequence is used to characterize a change characteristic of a demand of the maintenance equipment over time;
an interval characteristic determining module 302, configured to determine a required interval characteristic according to the consumption sequence;
the model selection module 303 is used for selecting a maintenance equipment consumption prediction model according to the demand interval characteristic;
and the number of the first and second groups,
and the prediction determining module 304 is used for inputting the consumption sequence into the equipment consumption prediction model to obtain the predicted demand of the maintenance equipment.
Fig. 4 is a functional block diagram of a terminal according to an embodiment of the present invention. As shown in fig. 4, the terminal 4 of this embodiment includes: a processor 400 and a memory 401, said memory 401 having stored therein a computer program 402 executable on said processor 400. The processor 400, when executing the computer program 402, implements the various service equipment prediction methods and embodiments described above, such as steps 101-104 shown in fig. 1.
Illustratively, the computer program 402 may be partitioned into one or more modules/units, which are stored in the memory 401 and executed by the processor 400 to implement the present invention.
The terminal 4 may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal 4 may include, but is not limited to, a processor 400, a memory 401. Those skilled in the art will appreciate that fig. 4 is only an example of a terminal 4 and does not constitute a limitation of terminal 4 and may include more or less components than those shown, or some components in combination, or different components, for example, the terminal may also include input output devices, network access devices, buses, etc.
The Processor 400 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 401 may be an internal storage unit of the terminal 4, such as a hard disk or a memory of the terminal 4. The memory 401 may also be an external storage device of the terminal 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal 4. Further, the memory 401 may also include both an internal storage unit and an external storage device of the terminal 4. The memory 401 is used for storing the computer programs and other programs and data required by the terminal. The memory 401 may also be used to temporarily store data that has been output or is to be output.
It should be clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional units and modules is only used for illustration, and in practical applications, the above function distribution may be performed by different functional units and modules as needed, that is, the internal structure of the apparatus may be divided into different functional units or modules to perform all or part of the above described functions. Each functional unit and module in the embodiments may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit, and the integrated unit may be implemented in a form of hardware, or may be implemented in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. For the specific working processes of the units and modules in the system, reference may be made to the corresponding processes in the foregoing method embodiments, which are not described herein again.
In the above embodiments, the description of each embodiment is focused on, and for parts that are not described or illustrated in detail in a certain embodiment, reference may be made to the description of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the technical solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal and method may be implemented in other manners. For example, the above-described apparatus/terminal embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions in actual implementation, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated module/unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method according to the above embodiment may be realized by a computer program, which may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the steps of the embodiments of the service equipment prediction method and the service equipment prediction apparatus may be realized. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media which may not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
The above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may be modified or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.

Claims (10)

1. A method for predicting a repair facility, comprising:
acquiring a consumption sequence of the maintenance equipment, wherein the consumption sequence is used for representing the characteristic that the demand of the maintenance equipment changes along with time;
determining a demand interval characteristic according to the consumption sequence;
selecting a maintenance equipment consumption prediction model according to the demand interval characteristic;
and inputting the consumption sequence into the equipment consumption prediction model to obtain the predicted demand of the maintenance equipment.
2. The method of predicting repair equipment as claimed in claim 1, wherein said determining a demand interval characteristic from said consumption sequence comprises:
calculating and obtaining an average demand interval and a variation coefficient of a non-zero demand value according to the consumption sequence, wherein the variation coefficient of the non-zero demand value is determined by a first formula, and the first formula is as follows:
Figure FDA0003636915430000011
in the formula, CV () is a variation coefficient of a non-zero requirement value, x is a consumption sequence of maintenance equipment, mu is an average value of average requirement intervals of the maintenance equipment, and sigma is a standard deviation of the requirement intervals of the maintenance equipment;
and determining the requirement interval characteristic of the maintenance equipment according to the average requirement interval and the variation coefficient of the non-zero requirement value.
3. The method of service equipment prediction of claim 1, wherein the demand interval characteristic comprises: the method comprises the following steps of selecting a maintenance equipment consumption prediction model according to the demand interval characteristic, wherein the maintenance equipment consumption prediction model comprises the following steps:
selecting a gray prediction model for common maintenance equipment, wherein the gray prediction model is constructed by the following steps:
carrying out 1-AGO conversion on the consumption sequence of the maintenance equipment to obtain a first-order accumulated maintenance equipment consumption data sequence;
obtaining a parameter list according to the consumption sequence of the maintenance equipment and the first-order accumulated maintenance equipment consumption data sequence;
and substituting the parameter list into a whitening equation to obtain a maintenance equipment consumption prediction model.
4. The method of claim 3, wherein said performing a 1-AGO transformation on the consumption sequence of the service equipment to obtain a first order cumulative service equipment consumption data sequence comprises:
establishing a maintenance equipment consumption original data sequence according to the consumption sequence of the maintenance equipment, wherein the maintenance equipment consumption original data sequence is as follows:
X 0 (t)＝{x 0 (1),x 0 (2),x 0 (3),…,x 0 (t)}
in the formula, X 0 () Consuming the original data sequence for maintenance of the equipment, t being the time, x 0 (t) maintenance equipment consumption at time t;
calculating to obtain the first-order accumulated maintenance equipment consumption data sequence according to the maintenance equipment consumption original data sequence, wherein the first-order accumulated maintenance equipment consumption data sequence is as follows:
X 1 (t)＝{x 1 (1),x 1 (2),x 1 (3),…,x 1 (t)}
in the formula, X 1 () For first order accumulation of maintenance equipment consumption data sequences, x 1 () And calculating according to a second formula, wherein the second formula is as follows:
Figure FDA0003636915430000021
5. the method of claim 4, further comprising a step of checking suitability after said calculating said first order cumulative service equipment consumption data sequence from said service equipment consumption raw data sequence, said step of suitability comprising:
calculating to obtain a grade ratio according to the first-order accumulated maintenance equipment consumption data sequence and a third formula, wherein the third formula is as follows:
Figure FDA0003636915430000022
where ρ () is the step ratio;
determining a fourth formula according to the level ratio, and determining whether the maintenance equipment consumption original data sequence meets the fourth formula, wherein the fourth formula is as follows:
Figure FDA0003636915430000023
ρ(t)∈(0,0.5)；
and if the original data sequence of the consumption of the maintenance equipment does not meet the fourth formula, reselecting the consumption prediction model of the maintenance equipment.
6. The method of claim 4, wherein obtaining a parameter list based on the sequence of service equipment consumptions and the first order cumulative service equipment consumption data sequence comprises:
obtaining a parameter list according to the original data sequence of the maintenance equipment consumption, the first-order accumulated maintenance equipment consumption data sequence and a fifth formula, wherein the fifth formula is as follows:
Figure FDA0003636915430000031
Figure FDA0003636915430000032
Y N ＝[x 0 (2),x 0 (3),…,x 0 (n)] T
in the formula (I), the compound is shown in the specification,
Figure FDA0003636915430000033
is a parameter sequence, a is a first parameter, and b is a second parameter.
7. The method of claim 6, wherein substituting the parameter columns into a whitening equation to obtain a model of service equipment consumption comprises:
the maintenance equipment consumption prediction model is as follows:
Figure FDA0003636915430000034
Figure FDA0003636915430000035
in the formula (I), the
Figure FDA0003636915430000036
Demand is predicted for servicing the equipment.
8. The method of claim 7, wherein said inputting said consumption sequence into said equipment consumption prediction model to obtain a predicted demand for a maintenance equipment comprises:
a model precision inspection step, wherein the model precision inspection step comprises the following steps:
obtaining a prediction model precision variance ratio and a small error probability according to the consumption sequence, the maintenance equipment consumption prediction model and a sixth formula, wherein the sixth formula is as follows:
Figure FDA0003636915430000041
Figure FDA0003636915430000042
Figure FDA0003636915430000043
Figure FDA0003636915430000044
Figure FDA0003636915430000045
Figure FDA0003636915430000046
in the formula, S 1 Is the mean square error of the original data sequence, S 2 Is the mean square error of the residual, C is the prediction model precision variance ratio, P is the small error probability,
Figure FDA0003636915430000047
predicting demand of the maintenance equipment at the time t obtained by inputting the consumption sequence into a maintenance equipment consumption prediction model;
and determining the precision grade of the maintenance equipment consumption prediction model according to the precision variance ratio of the prediction model, the small error probability and a threshold value.
9. A terminal comprising a memory and a processor, the memory having stored therein a computer program operable on the processor, wherein the processor when executing the computer program performs the steps of the method as claimed in any of claims 1 to 8.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 8.
CN202210508203.6A 2022-05-10 2022-05-10 Maintenance equipment prediction method, terminal and storage medium Pending CN114925895A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210508203.6A CN114925895A (en) 2022-05-10 2022-05-10 Maintenance equipment prediction method, terminal and storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210508203.6A CN114925895A (en) 2022-05-10 2022-05-10 Maintenance equipment prediction method, terminal and storage medium

Publications (1)

Publication Number Publication Date
CN114925895A true CN114925895A (en) 2022-08-19

Family

ID=82809040

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210508203.6A Pending CN114925895A (en) 2022-05-10 2022-05-10 Maintenance equipment prediction method, terminal and storage medium

Country Status (1)

Country Link
CN (1) CN114925895A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116993272A (en) * 2023-09-27 2023-11-03 罗普特科技集团股份有限公司 Logistics management method and system based on video monitoring and RFID

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116993272A (en) * 2023-09-27 2023-11-03 罗普特科技集团股份有限公司 Logistics management method and system based on video monitoring and RFID
CN116993272B (en) * 2023-09-27 2023-12-26 罗普特科技集团股份有限公司 Logistics management method and system based on video monitoring and RFID

Similar Documents

Publication Publication Date Title
Ray et al. Benchmarking for performance evaluation
CN110689355A (en) Client classification method, device, computer equipment and storage medium
Horal et al. Predicting the economic efficiency of the business model of an industrial enterprise using machine learning methods
CN114925895A (en) Maintenance equipment prediction method, terminal and storage medium
Shi et al. A Grey Model for Evaluation of Information Systems Security.
CN112257958A (en) Power saturation load prediction method and device
CN111339157B (en) Method, system and equipment for calculating and predicting daily operation efficiency of power distribution network
Rumyk Modeling the impact of economic indicators on food security
CN112686470A (en) Power grid saturation load prediction method and device and terminal equipment
CN112488851A (en) Method and device for automatically generating salesman grade and electronic equipment
CN115577983B (en) Enterprise task matching method based on block chain, server and storage medium
CN114707733A (en) Risk indicator prediction method and device, electronic equipment and storage medium
CN113779391A (en) Intelligent lock unlocking recommendation method, system and device based on modeling and storage medium
CN114331355A (en) Remote multi-stage approval method and system for flexible employment platform
CN113792961A (en) Economic responsibility auditing decision method and equipment based on big audit data
Niknya et al. Financial distress prediction of Tehran Stock Exchange companies using support vector machine
Nurmatov Econometric Analysis of the Efficiency of Using Tax Incentives
CN112598228A (en) Enterprise competitiveness analysis method, device, equipment and storage medium
Lum et al. The effects of data mining techniques on software cost estimation
Ruiz Difference Between the Real and Estimated Size of a Company: A Potential Cause of Tax Evasion
CN113869588A (en) Energy consumption benchmarking value prediction method, device, equipment and storage medium
CN114841817A (en) Group risk seed granularity odds rate prediction method and related equipment
CN115907217A (en) Data processing method, device, equipment and computer storage medium
CN112835951A (en) Data prediction method, data prediction device, computer equipment and storage medium
CN116703487A (en) Data analysis method, device, equipment and storage medium based on artificial intelligence

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination