CN110503240A - A kind of maintenance of the vessel resource requirement prediction technique and device - Google Patents
A kind of maintenance of the vessel resource requirement prediction technique and device Download PDFInfo
- Publication number
- CN110503240A CN110503240A CN201910656780.8A CN201910656780A CN110503240A CN 110503240 A CN110503240 A CN 110503240A CN 201910656780 A CN201910656780 A CN 201910656780A CN 110503240 A CN110503240 A CN 110503240A
- Authority
- CN
- China
- Prior art keywords
- ship
- dock repair
- repair
- dock
- rank
- 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.)
- Granted
Links
- 238000012423 maintenance Methods 0.000 title claims abstract description 55
- 238000000034 method Methods 0.000 title claims abstract description 38
- 230000008439 repair process Effects 0.000 claims abstract description 114
- 230000000994 depressogenic effect Effects 0.000 claims abstract description 9
- 230000008569 process Effects 0.000 claims abstract description 9
- 238000004458 analytical method Methods 0.000 claims abstract description 7
- 239000011248 coating agent Substances 0.000 claims description 4
- 238000000576 coating method Methods 0.000 claims description 4
- 238000004870 electrical engineering Methods 0.000 claims description 4
- 230000005662 electromechanics Effects 0.000 claims description 4
- 238000007621 cluster analysis Methods 0.000 claims description 3
- 230000004927 fusion Effects 0.000 abstract description 8
- 238000011156 evaluation Methods 0.000 abstract description 4
- 238000011160 research Methods 0.000 description 7
- 238000010276 construction Methods 0.000 description 2
- 238000013210 evaluation model Methods 0.000 description 2
- 238000013468 resource allocation Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
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
- 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
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] 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
- G06Q10/20—Administration of product repair or maintenance
-
- 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
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/40—Business processes related to the transportation industry
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Game Theory and Decision Science (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention relates to a kind of maintenance of the vessel resource requirement prediction technique and devices, this method carries out clustering for history dock repair data, obtain every engineering each dock repair rank and its in requisition for dock repair resource occupation time, while obtaining the corresponding ship status index of each dock repair rank using Reverse Analysis Way of Trouble；Then the dock repair rank of ship to be repaired is obtained in combination with the judge of operator according to ship current operating conditions index；Finally according to the dock repair resources requirement of dock repair level prediction ship to be repaired.The present invention carries out the requirement forecasting for repairing the occupancy type resource such as required personnel, facility, equipment in depressed place maintenance process suitable for ship, using fusion historical data and the method for real time information, establish the statistical weight prediction model based on fuzzy evaluation, complete requirement forecasting modeling technique, by carrying out data fusion to history, real time data with certainty and uncertainty, the requirement forecasting that maintenance of the vessel occupies class resource is provided.
Description
Technical field
The present invention relates to maintenance of the vessel resources technical fields, are specifically related to a kind of maintenance of the vessel resource requirement prediction
Method and device.
Background technique
Maintenance of the vessel resource requirement prediction mainly provides basis to carry out the configuration of ship dock repair Maintenance Resource configuration optimization
Data, and the maintenance project for further working out apparel for business department provides scientific theoretical foundation and feasible reference side
Case, the plan for enabling it to work out preferably meet ship use and maintenance requirements.It is big under the premise of ensureing ship availability
Width reduces life cycle cost, generates more significant economic benefit.Currently, in terms of maintenance of the vessel resource, studying more is
Maintenance Resource type and quantity during shipbuilding determine method, and in the mathematical model established, spininess repairs consumption-type
Distributing rationally for resource conducts a research, such as standby redundancy demand model, and to manpower, facility needed for maintenance of the vessel, equipment etc.
Occupancy type the Study on Resources is less.
Summary of the invention
The present invention for the technical problems in the prior art, provide a kind of maintenance of the vessel resource requirement prediction technique and
Device.
The technical scheme to solve the above technical problems is that
On the one hand, the present invention provides a kind of maintenance of the vessel resource requirement prediction technique, comprising the following steps:
For history dock repair data carry out clustering, obtain every engineering each dock repair rank and its in requisition for depressed place
Resource occupation time is repaired, while obtaining the corresponding ship status index of each dock repair rank using Reverse Analysis Way of Trouble；
According to ship current operating conditions index in combination with the judge of operator, the dock repair grade of ship to be repaired is obtained
Not；
According to the dock repair resources requirement of dock repair level prediction ship to be repaired.
Further, the history dock repair data include: the profession, rank and quantity of technical staff；Operating personnel's is special
Industry, rank and quantity；Ensure type, model, scale and the quantity of equipment；Type, model, scale and the quantity of support facility.
Further, the history dock repair data that are directed to carry out clustering, obtain each dock repair rank of every engineering
And its in requisition for dock repair resource occupation time, comprising:
Ship total dock repair time is divided into three ranks: it is general, more serious, serious, calculate statistical averages at different levels
Time corresponds to overall dock repair dock repair resource occupation times at different levels with this；Similarly the dock repair of Ship ' items engineering is at different levels
Other dock repair resource occupation time.
Further, the clustering is specific as follows:
Wherein, xijIndicate holding time when the i-th class resource jth maintenance of the vessel；xiIndicate the i-th class resource occupation time
Average value；N indicates that the history collected repairs ships quantity；σiIndicate the mean square deviation of the i-th class resource occupation time；
Then work as xij-xi< σiWhen, xijBelong to general rank；
Work as σi≤xij-xi2 σ of <iWhen, xijBelong to more serious rank；
As 2 σi≤xij-xiWhen, xijBelong to severity level.
Further, it is described according to ship current operating conditions index in combination with the judge of operator, obtain wait tie up
The dock repair rank of shiprepair oceangoing ship, comprising:
Establish following assessment models:
ZHIBIAO2i=0.4ZHIBIAO1+0.6JZPGi
Wherein:
T0, T1, S0 are constant, indicate maintenance of the vessel after to repair next time theoretical hours underway, interval time, boat
Row mileage；
WXJB0 indicates the dock repair rank of warship totality；
WXJB1iIndicate the dock repair rank of i-th engineering of the warship；
HSJ indicates hours underway after the maintenance of warship last time；
HLZ indicates shipping kilometre after the maintenance of warship last time；
CWXSJ indicates the time after the maintenance of warship last time；
JZPGiIndicate warship captain for the assessment in the warship use process.
Further, every engineering includes at least hull, ship electromechanics, electrical engineering, piping engineering, occupies dress and apply
Fill one of engineering or a variety of.
Further, the dock repair resources requirement according to dock repair level prediction ship to be repaired, comprising:
The statistical weight prediction model based on fuzzy message is established, according to the dock repair rank and each dock repair of ship to be repaired
Rank in requisition for dock repair resource occupation time, treat maintenance ship dock repair resources requirement predicted.
Further, the statistical weight prediction model based on fuzzy message, such as following formula:
ZYSLi=α JIBIE1SLi+βJIBIE2SLi+γJIBIE3SLi
In formula, ZYSLiIndicate the quantity required of the i-th class resource in maintenance process；
JIBIE1SLi、JIBIE2SLi、JIBIE3SLiIt then respectively indicates the i-th class resource and corresponds to dock repair rank generally, relatively sternly
Weight, serious historical statistics average value；
α, β, γ are weighting coefficient, according to historical experience value.
Preferably, α, β, γ value are as follows in the statistical weight prediction model:
On the other hand, the present invention also provides a kind of maintenance of the vessel resource requirement prediction meanss, comprising:
Cluster Analysis module obtains each dock repair grade of every engineering for carrying out clustering for history dock repair data
Not and its in requisition for dock repair resource occupation time, while obtaining the corresponding ship shape of each dock repair rank using Reverse Analysis Way of Trouble
State index；
Dock repair level assessment module is obtained for the judge according to ship current operating conditions index in combination with operator
To the dock repair rank of ship to be repaired；
Requirement forecasting module, for the dock repair resources requirement according to dock repair level prediction ship to be repaired.
The beneficial effects of the present invention are: the situation that the present invention is insufficient for current occupancy type resource requirement research, carries out
Occupancy type resource requirement research, proposes the method in conjunction with historical data and ship real time data to be repaired, passes through data fusion
Mode carry out requirement forecasting.For the empirical data of history, same type data are mainly divided into three by the way of cluster
A rank calculates assembly averages at different levels, and the statistical value of each rank of dock repair (general, more serious, serious) is corresponded to this；It is right
Data are assessed in the present condition for ships to be repaired in real time, are provided using fuzzy synthetic evaluation model.
The present invention carries out suitable for ship repairs the occupancy type such as required personnel, facility, equipment money maintenance process in depressed place
The requirement forecasting in source establishes the statistical weight prediction based on fuzzy evaluation using fusion historical data and the method for real time information
Model completes requirement forecasting modeling technique, by carrying out data to history, real time data with certainty and uncertainty
Fusion provides the requirement forecasting that maintenance of the vessel occupies class resource.Research achievement is required each when can not only instruct maintenance of the vessel
Class resource allocation proposal, additionally it is possible to instruct the resource constructions such as personnel, facility, the equipment of maintenance of the vessel producer.
Detailed description of the invention
Fig. 1 is the method for the present invention flow chart；
Fig. 2 is apparatus of the present invention structure chart.
Specific embodiment
The principle and features of the present invention will be described below with reference to the accompanying drawings, and the given examples are served only to explain the present invention, and
It is non-to be used to limit the scope of the invention.
As shown in Figure 1, the present invention provides a kind of maintenance of the vessel resource requirement prediction technique, comprising the following steps:
For history dock repair data carry out clustering, obtain every engineering each dock repair rank and its in requisition for depressed place
Resource occupation time is repaired, while obtaining the corresponding ship status index of each dock repair rank using Reverse Analysis Way of Trouble；
According to ship current operating conditions index in combination with the judge of operator, the dock repair grade of ship to be repaired is obtained
Not；
According to the dock repair resources requirement of dock repair level prediction ship to be repaired.
Specifically, firstly, consider requirement of the Maintenance Resource requirement forecasting to empirical data, historical data acquisition and pre- is proposed
Processing method, acquisition data type include: the profession, rank and quantity of technical staff；Profession, rank and the number of operating personnel
Amount；Ensure type, model (scale) and the quantity of equipment；Type, model (scale) and the quantity of support facility.For data
Ship total dock repair time (excluding the waiting time) is divided into three ranks mainly by the way of cluster by pretreatment, is calculated each
The statistical average time of rank corresponds to the dock repair time of each rank of overall dock repair (general, more serious, serious) with this；Similarly may be used
To obtain each rank of dock repair of ship items engineering (hull, electrical engineering, piping engineering, occupies dress and coating project at ship electromechanics)
The dock repair time of (general, more serious, serious).
For example, to certain ship dock repair input data carry out rough estimates, as input depressed place in engineering structure, bow sternpost, container,
Suction box overhauling project needs hull profession 1 people of intermediate engineer, and 13 days, then statistics is engineering structure, bow sternpost, cabin in depressed place
It was 13 (man days) that cabinet, suction box overhauling project, which need the intermediate engineer's quantity required of hull profession,.
Further, the clustering is specific as follows:
Wherein, xijIndicate holding time when the i-th class resource jth maintenance of the vessel；xiIndicate the i-th class resource occupation time
Average value；N indicates that the history collected repairs ships quantity；σiIndicate the mean square deviation of the i-th class resource occupation time；
Then work as xij-xi< σiWhen, xijBelong to general rank；
Work as σi≤xij-xi2 σ of <iWhen, xijBelong to more serious rank；
As 2 σi≤xij-xiWhen, xijBelong to severity level.
Secondly, considering that the requirement of maintenance ship real time data is treated in Maintenance Resource requirement forecasting, ship current state is proposed
Appraisal procedure establishes current state assessment models.Treat maintenance ship carry out entry evaluation, nothing but exactly two aspect ask
Topic: first is in the conventional operation i.e. some parameters such as runing time, hours underway, navigation of last time maintenance so far of the ship
Journey etc.；Second is the preliminary judgement for judging i.e. captain of operator as whether certain systems are operating abnormally or all run just
Often etc..Accordingly, we establish following assessment models:
ZHIBIAO2i=0.4ZHIBIAO1+0.6JZPGi
Wherein:
T0, T1, S0 are constant, indicate maintenance of the vessel after to repair next time theoretical hours underway, interval time, boat
Row mileage；
WXJB0 indicates the dock repair rank of warship totality；
WXJB1iIndicate i-th engineering of the warship (hull, electrical engineering, piping engineering, occupies dress and coating project at ship electromechanics)
Dock repair rank；
HSJ indicates hours underway (hour) after the maintenance of warship last time；
HLZ indicates shipping kilometre (in the sea) after the maintenance of warship last time；
CWXSJ indicates the time (day) after the maintenance of warship last time；
JZPGiWarship captain is indicated for the assessment in the warship use process, the corresponding hull of value 1,2,3, ship are electromechanical, electric
Gas engineering, piping engineering, residence fills and the situation of coating project is general, more serious, serious.
Finally, establish the statistical weight prediction model based on fuzzy message, according to the dock repair rank of ship to be repaired and
Each dock repair rank in requisition for dock repair resource occupation time, treat maintenance ship dock repair resources requirement predicted.
The statistical weight prediction model based on fuzzy message, such as following formula:
ZYSLi=α JIBIE1SLi+βJIBIE2SLi+γJIBIE3SLi
In formula, ZYSLiIndicate the quantity required of the i-th class resource in maintenance process；
JIBIE1SLi、JIBIE2SLi、JIBIE3SLiIt then respectively indicates the i-th class resource and corresponds to dock repair rank generally, relatively sternly
Weight, serious historical statistics average value；
α, β, γ are weighting coefficient, according to historical experience value.
Preferably, α, β, γ value are as follows in the statistical weight prediction model:
The present invention carries out suitable for ship repairs the occupancy type such as required personnel, facility, equipment money maintenance process in depressed place
The requirement forecasting in source establishes the statistical weight prediction based on fuzzy evaluation using fusion historical data and the method for real time information
Model completes requirement forecasting modeling technique, by carrying out data to history, real time data with certainty and uncertainty
Fusion provides the requirement forecasting that maintenance of the vessel occupies class resource.Research achievement is required each when can not only instruct maintenance of the vessel
Class resource allocation proposal, additionally it is possible to instruct the resource constructions such as personnel, facility, the equipment of maintenance of the vessel producer.
On the other hand, the present invention also provides a kind of maintenance of the vessel resource requirement prediction meanss, as shown in Figure 2, comprising:
Cluster Analysis module obtains each dock repair grade of every engineering for carrying out clustering for history dock repair data
Not and its in requisition for dock repair resource occupation time, while obtaining the corresponding ship shape of each dock repair rank using Reverse Analysis Way of Trouble
State index；
Dock repair level assessment module is obtained for the judge according to ship current operating conditions index in combination with operator
To the dock repair rank of ship to be repaired；
Requirement forecasting module, for the dock repair resources requirement according to dock repair level prediction ship to be repaired.
The situation that the present invention is insufficient for current occupancy type resource requirement research, has carried out occupancy type resource requirement research,
The method in conjunction with historical data and ship real time data to be repaired is proposed, requirement forecasting is carried out by way of data fusion.
For the empirical data of history, same type data are mainly divided into three ranks by the way of cluster, are calculated at different levels
Assembly average corresponds to the statistical value of each rank of dock repair (general, more serious, serious) with this；For real-time ship to be repaired
As-Is Assessment data, are provided using fuzzy synthetic evaluation model.
The foregoing is merely presently preferred embodiments of the present invention, is not intended to limit the invention, it is all in spirit of the invention and
Within principle, any modification, equivalent replacement, improvement and so on be should all be included in the protection scope of the present invention.
Claims (10)
1. a kind of maintenance of the vessel resource requirement prediction technique, which comprises the following steps:
For history dock repair data carry out clustering, obtain every engineering each dock repair rank and its in requisition for dock repair provide
Source holding time, while the corresponding ship status index of each dock repair rank is obtained using Reverse Analysis Way of Trouble；
According to ship current operating conditions index in combination with the judge of operator, the dock repair rank of ship to be repaired is obtained；
According to the dock repair resources requirement of dock repair level prediction ship to be repaired.
2. the method according to claim 1, wherein the history dock repair data include: technical staff profession,
Rank and quantity；Profession, rank and the quantity of operating personnel；Ensure type, model, scale and the quantity of equipment；Support facility
Type, model, scale and quantity.
3. the method according to claim 1, wherein described carry out clustering for history dock repair data,
Obtain every engineering each dock repair rank and its in requisition for dock repair resource occupation time, comprising:
Ship total dock repair time is divided into three ranks: it is general, more serious, serious, the statistical average times at different levels are calculated,
Overall dock repair dock repair resource occupation times at different levels are corresponded to this；The similarly dock repair of Ship ' items engineering depressed places at different levels
Repair resource occupation time.
4. according to the method described in claim 3, it is characterized in that, the clustering is specific as follows:
Wherein, xijIndicate holding time when the i-th class resource jth maintenance of the vessel；xiIndicate that the i-th class resource occupation time is average
Value；N indicates that the history collected repairs ships quantity；σiIndicate the mean square deviation of the i-th class resource occupation time；
Then work as xij-xi< σiWhen, xijBelong to general rank；
Work as σi≤xij-xi2 σ of <iWhen, xijBelong to more serious rank；
As 2 σi≤xij-xiWhen, xijBelong to severity level.
5. the method according to claim 1, wherein described tie simultaneously according to ship current operating conditions index
The judge of closing operation person obtains the dock repair rank of ship to be repaired, comprising:
Establish following assessment models:
ZHIBIAO2i=0.4ZHIBIAO1+0.6JZPGi
Wherein:
T0, T1, S0 are constant, indicate after maintenance of the vessel to repair next time theoretical hours underway, interval time, in navigation
Journey；
WXJB0 indicates the dock repair rank of warship totality；
WXJB1iIndicate the dock repair rank of i-th engineering of the warship；
HSJ indicates hours underway after the maintenance of warship last time；
HLZ indicates shipping kilometre after the maintenance of warship last time；
CWXSJ indicates the time after the maintenance of warship last time；
JZPGiIndicate warship captain for the assessment in the warship use process.
6. method according to claim 1-5, which is characterized in that every engineering include at least hull,
One of ship electromechanics, electrical engineering, piping engineering, residence dress and coating project are a variety of.
7. the method according to claim 1, wherein the depressed place according to dock repair level prediction ship to be repaired
Repair resources requirement, comprising:
The statistical weight prediction model based on fuzzy message is established, according to the dock repair rank of ship to be repaired and each dock repair rank
To in requisition for dock repair resource occupation time, treat maintenance ship dock repair resources requirement predicted.
8. the method according to the description of claim 7 is characterized in that the statistical weight based on fuzzy message predicts mould
Type, such as following formula:
ZYSLi=α JIBIE1SLi+βJIBIE2SLi+γJIBIE3SLi
In formula, ZYSLiIndicate the quantity required of the i-th class resource in maintenance process；
JIBIE1SLi、JIBIE2SLi、JIBIE3SLiThen respectively indicating the i-th class resource, to correspond to dock repair rank general, more serious, tight
The historical statistics average value of weight；
α, β, γ are weighting coefficient, according to historical experience value.
9. according to the method described in claim 8, it is characterized in that, α, β, γ value is such as in the statistical weight prediction model
Under:
。
10. a kind of maintenance of the vessel resource requirement prediction meanss characterized by comprising
Cluster Analysis module, for for history dock repair data carry out clustering, obtain every engineering each dock repair rank and
Its in requisition for dock repair resource occupation time, while obtaining the corresponding ship status of each dock repair rank using Reverse Analysis Way of Trouble and referring to
Mark；
Dock repair level assessment module, for the judge according to ship current operating conditions index in combination with operator, obtain to
Repair the dock repair rank of ship；
Requirement forecasting module, for the dock repair resources requirement according to dock repair level prediction ship to be repaired.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910656780.8A CN110503240B (en) | 2019-07-19 | 2019-07-19 | Ship maintenance resource demand prediction method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910656780.8A CN110503240B (en) | 2019-07-19 | 2019-07-19 | Ship maintenance resource demand prediction method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110503240A true CN110503240A (en) | 2019-11-26 |
| CN110503240B CN110503240B (en) | 2022-04-05 |
Family
ID=68586721
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910656780.8A Active CN110503240B (en) | 2019-07-19 | 2019-07-19 | Ship maintenance resource demand prediction method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110503240B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111695744A (en) * | 2020-06-28 | 2020-09-22 | 曾晨 | Maintenance equipment demand prediction analysis system based on big data |
| CN111723255A (en) * | 2020-05-08 | 2020-09-29 | 中国人民解放军海军特色医学中心 | Construction method of ship control room operation task completion time database |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102136032A (en) * | 2011-03-16 | 2011-07-27 | 北京航空航天大学 | Process-based method for establishing equipment reliability maintenance safety (RMS) requirement model |
| CN103164757A (en) * | 2011-12-12 | 2013-06-19 | 中国人民解放军第二炮兵工程学院 | Gear maintaining task assigning method |
| CN104268411A (en) * | 2014-09-29 | 2015-01-07 | 中国人民解放军国防科学技术大学 | Method for maintainability assessment and weak link identification of production design stage |
| US20150039764A1 (en) * | 2013-07-31 | 2015-02-05 | Anton Beloglazov | System, Method and Computer Program Product for Energy-Efficient and Service Level Agreement (SLA)-Based Management of Data Centers for Cloud Computing |
| CN104637021A (en) * | 2013-11-08 | 2015-05-20 | 广州市地下铁道总公司 | Condition-maintenance-mode city rail vehicle auxiliary maintenance system |
| CN204425404U (en) * | 2015-02-06 | 2015-06-24 | 合肥戎科信息技术开发有限公司 | Based on the mobile repair and maintenance system of Internet of Things |
| CN105574592A (en) * | 2015-12-18 | 2016-05-11 | 中国人民解放军第五七一九工厂 | Aero-engine maintenance engineering management tool |
| CN107437116A (en) * | 2017-08-02 | 2017-12-05 | 中国航空工业集团公司西安飞机设计研究所 | It is a kind of towards using and maintenance task Equipment Indemnificatory analysis method |
| CN107918799A (en) * | 2017-11-08 | 2018-04-17 | 中国舰船研究设计中心 | A kind of ship integrated logistics support management system based on B/S framework |
| CN108053036A (en) * | 2017-10-17 | 2018-05-18 | 中国人民解放军海军工程大学 | A kind of Large Complex Equipment Maintenance Support Capability Assessment method |
-
2019
- 2019-07-19 CN CN201910656780.8A patent/CN110503240B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102136032A (en) * | 2011-03-16 | 2011-07-27 | 北京航空航天大学 | Process-based method for establishing equipment reliability maintenance safety (RMS) requirement model |
| CN103164757A (en) * | 2011-12-12 | 2013-06-19 | 中国人民解放军第二炮兵工程学院 | Gear maintaining task assigning method |
| US20150039764A1 (en) * | 2013-07-31 | 2015-02-05 | Anton Beloglazov | System, Method and Computer Program Product for Energy-Efficient and Service Level Agreement (SLA)-Based Management of Data Centers for Cloud Computing |
| CN104637021A (en) * | 2013-11-08 | 2015-05-20 | 广州市地下铁道总公司 | Condition-maintenance-mode city rail vehicle auxiliary maintenance system |
| CN104268411A (en) * | 2014-09-29 | 2015-01-07 | 中国人民解放军国防科学技术大学 | Method for maintainability assessment and weak link identification of production design stage |
| CN204425404U (en) * | 2015-02-06 | 2015-06-24 | 合肥戎科信息技术开发有限公司 | Based on the mobile repair and maintenance system of Internet of Things |
| CN105574592A (en) * | 2015-12-18 | 2016-05-11 | 中国人民解放军第五七一九工厂 | Aero-engine maintenance engineering management tool |
| CN107437116A (en) * | 2017-08-02 | 2017-12-05 | 中国航空工业集团公司西安飞机设计研究所 | It is a kind of towards using and maintenance task Equipment Indemnificatory analysis method |
| CN108053036A (en) * | 2017-10-17 | 2018-05-18 | 中国人民解放军海军工程大学 | A kind of Large Complex Equipment Maintenance Support Capability Assessment method |
| CN107918799A (en) * | 2017-11-08 | 2018-04-17 | 中国舰船研究设计中心 | A kind of ship integrated logistics support management system based on B/S framework |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111723255A (en) * | 2020-05-08 | 2020-09-29 | 中国人民解放军海军特色医学中心 | Construction method of ship control room operation task completion time database |
| CN111695744A (en) * | 2020-06-28 | 2020-09-22 | 曾晨 | Maintenance equipment demand prediction analysis system based on big data |
| CN111695744B (en) * | 2020-06-28 | 2023-04-18 | 中国舰船研究设计中心 | Maintenance equipment demand prediction analysis system based on big data |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110503240B (en) | 2022-04-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105096053B (en) | A kind of health control decision-making technique suitable for complicated technology system | |
| Dalgic et al. | Investigation of optimum crew transfer vessel fleet for offshore wind farm maintenance operations | |
| CN103069254A (en) | Method and arrangement for controllin energy consumption in a marne vessel | |
| CN110503240A (en) | A kind of maintenance of the vessel resource requirement prediction technique and device | |
| KR20090091277A (en) | Information recording medium on which a computer-readable program for ship's sailing order optimization system | |
| CN110516821A (en) | A kind of maintenance of the vessel method for optimizing resource allocation and device | |
| CN109978184A (en) | A kind of maintenance of the vessel maintenance platform | |
| CN107480830A (en) | Anchor position number optimization method based on the emulation of harbor approach anchorage hybrid system | |
| CN104376375B (en) | Marine ships oil spill accident emergency resources concocting method based on multi objective fuzzy planning | |
| CN105404940B (en) | Maintenance resource prediction method for ship use stage | |
| Jeon et al. | Ship safety policy recommendations for Korea: application of system dynamics | |
| Oke et al. | An approach for evaluating preventive maintenance scheduling cost | |
| Dawid et al. | Development of an O&M tool for short term decision making applied to offshore wind farms | |
| Lazakis | Establishing an innovative and integrated reliability and criticality based maintenance strategy for the maritime industry | |
| KR20210060171A (en) | Method for predicting demand of requirement man power and material according to new ship contract | |
| Garbatov et al. | Investment cost estimate accounting for shipbuilding constraints | |
| KR20090051150A (en) | Information recording medium on which a computer-readable program for ship's energy consumption optimization system | |
| Evers et al. | Centralized versus distributed feeder ship service: the case of the Maasvlakte harbour area of Rotterdam | |
| CN113806915A (en) | Marine wind power plant operation and maintenance simulation method based on analog platform | |
| Lin et al. | Subjective and objective analysis of schedule delaying factors for container shipping lines | |
| Manea et al. | Using regression theory to solve some problems regarding ship repairing and maintenance activities in shipyards | |
| Manea et al. | The Influence of the Deadweight in the Projection of the Duration of the Maritime Ships Mentenancy Works | |
| CN113570237B (en) | Automatic queuing method for ship based on planar two-dimensional coordinates | |
| Bondarenko et al. | Effectiveness harbour tug fleet: problem formulation and methodology of its solution | |
| Bao et al. | Research on Intelligent Regulation of Ship-to-Ship Crude Oil Transfer at Sea Based on Internet of Things |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |