CN109345038A - A kind of shipyard always organizes place intelligent distribution optimization algorithm - Google Patents
A kind of shipyard always organizes place intelligent distribution optimization algorithm Download PDFInfo
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- G—PHYSICS
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- 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
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
Abstract
The present invention relates to a kind of shipyards always to organize place intelligent optimization placement algorithm.It specifically includes；Basic data model needed for total group place management and running: network, crane data and lifting time, total group plan, are carried at ship type data in dock place；It establishes using place rule, logistics logic and process flow as the constraint condition of principal element；Total group plan balance optimizing algorithm is established with basic data model and corresponding constraint condition, forms total group place layout optimization scheme and crane planning optimization scheme.Place scheduled digital management system is always organized for independent research, realizes that the subsection scheduling simulation optimization in total group place provides theoretical and algorithm basis, for the fine-grained management for realizing the total group scheduling of shipyard, improves production efficiency, realizes that the continuous shipbuilding of equilibrium is laid a good foundation.
Description
Technical field
The present invention relates to shipbuilding technical field, in particular to a kind of shipyard always organizes place intelligent distribution optimization algorithm.
Background technique
Core resource of the dock as shipyard, the service efficiency of dock directly determine the economic benefit of shipyard.And total group
The person of directly feeding of resource is carried in place as dock, and management performance directly decides the carrying efficiency of dock.Total group place
It is numerous to manage the factor being related to, and there is many to influence each other between them, traditional means obviously can not be effectively treated
Such a large amount of information, can only "retain the large, release the small" be managed in more extensive mode；
Majority shipyard specially sets up the scheduling group that more people form by authorities or equipped section at present, formulates by batch total
The two dimensional topology scheduling scheme in group place and gantry crane application plan, it is this that list is faced in a manner of the Passive Management of resource driving
When the Discontinuous manufacture of batch, this total group of traditional site management mode issue is not protruded, and can not only accomplish comprehensive effect
Rate highest.However, when facing multiple batches of continuous production, the problem of traditional total group of site management mode, is just completely exposed, main
It embodies as follows:
1) extensive management, passive, can not push the raising of comprehensive production efficiency.Due to always organizing the number of fragments in place too
More, the mobility of plan is larger, and visualization and information sharing means lack, and leads to place layout and crane resources scheduling (lifting
Sequentially, the collaboration of more cranes) randomness, blindness it is larger, therefore place is always organized to multi-product only according to the experience of manager
Subsection scheduling management can not adapt to the lean shipbuildings mode such as Modern Shipbuilding energy-saving and emission-reduction, cost efficiency；
2) Ability of emergency management is insufficient.Total group of traditional site management mode for multiple batches of continuous production, if
Some part there is a problem, cause current place distribution not execute actually, it is subsequent that result often will always organize place
Plan of distribution is all upset, and problem constantly accumulates, and orderly management gradually becomes unordered management, ultimately becomes extensive random pipe
Reason, not can guarantee the efficient utilization of resource.
With the development of digital shipbuilding technology, computer software is applied to ship by most domestically leading shipyards
In oceangoing ship production management process, still, for always organize place use almost be at it is no quantization, without fining and without comparisonization
State causes the efficiency for carrying operation that can not improve.Thus, it is necessary to study dock/building berth emulation platform system modelling pass
Key technology and support have dock/building berth building course Optimization of Production Dispatching emulation technology of interaction characteristic.It is produced in real ship
Carried out before building the simulation of effective digitalized artificial can find in advance and correct there are the problem of, while in the actual production process
The reasonability of production plan is verified, optimization place layout improves production efficiency, balances gantry crane resource load, to realize equal
It weighs continuous shipbuilding, this is also the main direction of development of digital shipbuilding from now on.
Summary of the invention
The present invention provides a kind of shipyard and always organizes place intelligent distribution optimization method, substitutes traditional plan manually and compiles
Row's mode gets rid of tradition in such a way that resource driving, passive type always organize plan, realizes that shipyard always organizes the fine of place resource
Change, digital management, promote the Ability of emergency management in total group place, improve the reasonability of production plan, optimization place layout mentions
High efficiency, to realize balanced continuous shipbuilding.
In order to solve the above-mentioned technical problems, the present invention provides the following technical solutions:
The present invention provides a kind of shipyard and always organizes place intelligent distribution optimization algorithm, including always organize place basis data model,
Constraint condition relationship, place layout intelligent optimization algorithm and crane plan intelligent optimization algorithm；
Total group place basis data model includes dock place model, ship type data, total group of plan, carries network, crane
Data module and lifting time module；
Constraint condition relationship is set at based on place rule, logistics logic and process flow during total group is carried
Related schedule constraints, wherein constraint condition relationship includes constraint based on sites, carries network constraint and crane optimization calculating constraint: place
Constraint includes that place network divides size, overlap span and calculates weighted value；Carrying network constraint includes that each segment cycle is maximum
Floating number of days；It includes daily job initiation time, time of having a rest, work calendar, initial crane that crane planning optimization, which calculates constraint,
Position and calculating weighted value；Main constraints are as shown in table 1:
1 conditional restriction classification of table
Based on always organizing place basis data model and constraint conditional relationship, develops the place based on population and be laid out intelligence
Energy optimization algorithm and crane plan intelligent optimization algorithm, on the basis of scheduling scheme, when with crane resources, manpower working hour, work
Between, carry/total group network is constraint, carry out crane optimization and calculate.
As a preferred technical solution of the present invention, total place basis data model particular content of organizing includes:
Place is carried out gridding division by dock place model, to realize that dock place digitizes, forms place layout
" painting canvas ", for total group place intelligent distribution optimization algorithm input space data.It is as shown in table 2:
2 dock place data model of table
Ship type data are indicated as unit of being segmented, and the volume size of segmentation is indicated with the volume of the minimum bounding box of segmentation,
Constitute " pel " in the layout of place, specific data model are as follows: with the coordinate of this bounding box six end faces in hull coordinate system
Data indicate that this six data are as shown in table 3:
3 end face coordinate data type of table
| Stern end face | Bow end face | Left side | Right side | Lower end surface | Upper surface |
Total group plan is that dock always organizes the plan that block assembly and carrying are carried out on place, available segmentation occupied ground
Initial time and the end time, for total group place intelligent distribution optimization algorithm input time data.Main contents include such as table 4
It is shown:
Table 4 always organizes planning data model
| Serial number | Program content | Explanation |
| 1 | Name of vessel | Ship name |
| 2 | Total group block | Always organize total name section |
| 3 | Block | Total name section |
| 4 | Segmentation | Section name |
| 5 | Positioning plan | Plan positioning time |
| 6 | Place station | Total group place position |
| 7 | Carry plan | The time is carried in plan |
Crane data module and lifting time module, the data of crane data module essential record crane；Lift time number
Different zones segmentation is defined according to model, block carries out occupied standard lifting time, crane data and lifting when total group, carrying
The specific data of time are as shown in table 5:
The classification of 5 crane relational data model of table
As a preferred technical solution of the present invention, place is laid out intelligent optimization algorithm and crane plan intelligent optimization is calculated
Method is particle swarm optimization algorithm, the difference is that place layout intelligent optimization algorithm mainly optimizes space, and crane meter
Intelligent optimization algorithm is drawn mainly to optimize the time；The position and speed iterative formula of particle swarm optimization algorithm are as follows:
vi,d(t+1)=ω vi,d(t)+c1r1(pi,d-xi,d(t))+c2r2(pg,d-xi,d(t))
xi,d(t+1)=xi,d(t)+vi,d(t+1) (d=1,2 ..., N)
Parameter is as shown in table 6 in formula:
Table 6
| Serial number | Parameter | Explanation |
| 1 | ω | Inertia Weight |
| 2 | R1 and r2 | Equally distributed random number between 0~1 |
| 3 | C1 and c2 | Studying factors |
The value formula of ω are as follows:
Parameter is as shown in table 7 in formula:
Table 7
| Serial number | Parameter | Explanation |
| 1 | ωmax | Maximum inertia weight |
| 2 | ωmin | Minimum inertia weight |
| 3 | run | Current iteration number |
| 4 | runmax | The total degree of algorithm iteration |
As a preferred technical solution of the present invention, place is laid out intelligent optimization algorithm and uses normal distribution assignment, together
When coal addition position optimization algorithm strategy it is as shown in table 8:
8 optimization algorithm strategy of table
And the fitness function of particle swarm optimization algorithm involved in the layout intelligent optimization algorithm of the place, function
Concrete form are as follows:
Parameter is as shown in table 9 in formula:
Table 9
| Serial number | Parameter | Explanation |
| 1 | ωG | Gantry crane lifts by crane total energy consumption weight |
| 2 | ωT | Gantry crane lifts by crane time-consuming weight |
| 3 | ωX | Handling X-direction energy consumption weight |
| 4 | ωY | Handling Y-direction energy consumption weight |
| 5 | Gi | I-th of block quality |
| 6 | t | Scheme total time-consuming |
| 7 | LXi | I-th of block X-direction distance of handling |
| 8 | LYi | I-th of block Y-direction distance of handling |
As a preferred technical solution of the present invention, crane plan intelligent optimization algorithm, always to organize place placement scheme,
It is constraint with crane resources, manpower working hour, working time, carrying/total group network, balances two gantry crane operations in same dock
Load to achieve the purpose that lifting time and energy consumption comprehensive consumption are minimum under same lifting object amount, while solving " allowing for gantry crane
The step-length of position, waiting " state solves, and obtains lifting plan.
The beneficial effects obtained by the present invention are as follows being:
(1) place intelligent distribution optimization algorithm is always organized using shipyard, information relevant to total group place can be had
Effect management is advantageously implemented total group place digital management.It can by the place layout optimization scheme that intelligent optimization algorithm is formed
Precise positioning is segmented position, and crane planning optimization scheme can accurately plan crane cycle times, to realize that fine-grained management is established
Basis.
(2) place intelligent distribution optimization algorithm is always organized using shipyard, effective number can be carried out before real ship is produced and built
Change emulation, while verifying the reasonability of production plan in the actual production process, balancing equipment resource load improves production effect
Rate, to realize balanced continuous shipbuilding；
(3) place intelligent distribution optimization method is always organized using shipyard, segmentation, block can be preferentially occupied to this point of distance
The nearest region of section, block dock loading position, segmentation, the block arrangement for carrying region close to dock are close, reduce gantry crane
Travel distance, to reduce energy consumption and improve utilization of area rate.
(4) it is lower always to organize factors, the priority such as place intelligent distribution optimization method consideration global optimum, turnover rate for shipyard
Block can be turnover rate, the higher block of priority by position；
(5) shipyard is always organized place intelligent distribution optimization method and can be made effective use of in total group place apart from dock carrying
The nearest region in position, each block are drawn close to loading position as far as possible, and the block that occupied area is larger and the occupied ground period is long is as far as possible
It is placed on away from loading position compared with far region.
Detailed description of the invention
Attached drawing is used to provide further understanding of the present invention, and constitutes part of specification, with reality of the invention
It applies example to be used to explain the present invention together, not be construed as limiting the invention.
In the accompanying drawings:
Fig. 1 is the flow chart of intelligent distribution optimization method of the present invention.
Fig. 2 is the two-dimensional visualization model that certain shipyard always organizes place data in the embodiment of the present invention.
Fig. 3 is that shipyard always organizes the particle swarm optimization algorithm in the intelligent distribution optimization algorithm of place in the embodiment of the present invention.
Fig. 4 is that the total group of place obtained in the embodiment of the present invention optimizes placement scheme.
Fig. 5 is crane scheme in the embodiment of the present invention.
Fig. 6 is craneage quantity statistics in the embodiment of the present invention.
Specific embodiment
Hereinafter, preferred embodiments of the present invention will be described with reference to the accompanying drawings, it should be understood that preferred reality described herein
Apply example only for the purpose of illustrating and explaining the present invention and is not intended to limit the present invention.
Embodiment: as shown in figures 1 to 6, the present invention provides a kind of shipyard and always organizes place intelligent distribution optimization algorithm, always organizes field
Ground foundation data model, constraint condition relationship, place layout intelligent optimization algorithm and crane plan intelligent optimization algorithm.
The technical solution is now told about by taking certain shipyard as an example:
Constraint condition relationship is set at based on place rule, logistics logic and process flow during total group is carried
Related schedule constraints, wherein optimization calculates constraint as shown in table 10:
The optimization of table 10 calculates constraint
Based on basic data model and the constraint relationship, the primary condition that setting population calculates is as shown in table 11, Fig. 3
For the detailed process of particle swarm algorithm, the total group of placement scheme that certain shipyard always organizes certain batch in place is finally obtained.
The primary condition that 11 population of table calculates
| Serial number | Parameter | Initial value |
| 1 | The number of iterations | 50 |
| 2 | Population | 30 |
| 3 | Speed maximum times | 100 |
| 4 | Initialize speed | 8 |
Intelligent optimization algorithm is laid out in conjunction with place and crane plan intelligent optimization algorithm is particle swarm optimization algorithm, it is different
Be place layout intelligent optimization algorithm be optimization to space, and crane plan intelligent optimization algorithm is the optimization to the time.
The position and speed iterative formula of particle swarm optimization algorithm are as follows:
vi,d(t+1)=ω vi,d(t)+c1r1(pi,d-xi,d(t))+c2r2(pg,d-xi,d(t))
xi,d(t+1)=xi,d(t)+vi,d(t+1) (d=1,2 ..., N)
Parameter is as shown in table 12 in formula:
Table 12
| Serial number | Parameter | Explanation |
| 1 | ω | Inertia Weight |
| 2 | R1 and r2 | Equally distributed random number between 0~1 |
| 3 | C1 and c2 | Studying factors |
The value formula of ω are as follows:
Parameter is as shown in table 13 in formula:
Table 13
| Serial number | Parameter | Explanation |
| 1 | ωmax | Maximum inertia weight |
| 2 | ωmin | Minimum inertia weight |
| 3 | run | Current iteration number |
| 4 | runmax | The total degree of algorithm iteration |
Place is laid out intelligent optimization algorithm and uses normal distribution assignment, while coal addition position optimization algorithm strategy such as 14 institute of table
Show:
14 optimization algorithm strategy of table
And the fitness function of particle swarm optimization algorithm involved in the layout intelligent optimization algorithm of the place, function
Concrete form are as follows:
Parameter is as shown in Table 15 in formula:
Table 15
| Serial number | Parameter | Explanation |
| 1 | ωG | Gantry crane lifts by crane total energy consumption weight |
| 2 | ωT | Gantry crane lifts by crane time-consuming weight |
| 3 | ωX | Handling X-direction energy consumption weight |
| 4 | ωY | Handling Y-direction energy consumption weight |
| 5 | Gi | I-th of block quality |
| 6 | t | Scheme total time-consuming |
| 7 | LXi | I-th of block X-direction distance of handling |
| 8 | LYi | I-th of block Y-direction distance of handling |
Always to organize place placement scheme, and combine table 16- table 19, with crane resources, manpower working hour, the working time, carrying/
Total group network is constraint, balances two gantry crane working loads in same dock, lifts the time under same lifting object amount to reach
The minimum purpose with energy consumption comprehensive consumption, while the step-length for solving " step down, wait " state of gantry crane solves, and obtains synthesis and disappears
Consume minimum lifting plan.
16 ship type data of table
| Section name * | Stern end face (mm) * | Bow end face (mm) * | Right side (mm) * | Left side (mm) * | Lower end surface (mm) * | Upper surface (mm) * |
| A021 | 0 | 14700 | -2750 | 2750 | 10160 | 14210 |
| A020 | 2778 | 6670 | -540 | 540 | 6002 | 11960 |
| A221 | 0 | 14700 | 2150 | 14165 | 8655 | 14210 |
| A321 | 0 | 14700 | -14165 | -2150 | 8655 | 14210 |
| A231 | 0 | 14700 | 0 | 14160 | 13792 | 18592 |
| A331 | 0 | 14700 | -14160 | 0 | 13792 | c |
17 crane data of table
The 18 crane time of table
The always group plan of table 19
Finally, it should be noted that these are only the preferred embodiment of the present invention, it is not intended to restrict the invention, although
Present invention has been described in detail with reference to the aforementioned embodiments, for those skilled in the art, still can be right
Technical solution documented by foregoing embodiments is modified or equivalent replacement of some of the technical features.It is all
Within the spirit and principles in the present invention, any modification, equivalent replacement, improvement and so on should be included in protection of the invention
Within the scope of.
Claims (5)
1. a kind of shipyard always organizes place intelligent distribution optimization algorithm, which is characterized in that including always organizing place basis data model, about
Beam conditional relationship, place layout intelligent optimization algorithm and crane plan intelligent optimization algorithm；
Total group place basis data model includes dock place model, ship type data, total group of plan, carries network, crane data
Module and lifting time module；
Constraint condition relationship is set at related based on place rule, logistics logic and process flow during total group is carried
Schedule constraints, wherein constraint condition relationship includes constraint based on sites, carries network constraint and crane optimization calculating constraint: constraint based on sites
Size, overlap span are divided including place network and calculate weighted value；Carrying network constraint includes each segment cycle maximum float
Number of days；Crane planning optimization calculate constraint include the daily job initiation time, the time of having a rest, work calendar, initial crane position,
And calculate weighted value；
Based on always organizing place basis data model and constraint conditional relationship, it is excellent to develop the place layout intelligence based on population
Change algorithm and crane plan intelligent optimization algorithm with crane resources, manpower working hour, the working time, is taken on the basis of scheduling scheme
Carrying/always organize network is constraint, carries out crane and optimizes calculating.
2. a kind of shipyard according to required by right 1 always organizes place intelligent distribution optimization algorithm, which is characterized in that always organize Slag
Plinth data model particular content includes:
Place is carried out gridding division by dock place model, to realize that dock place digitizes, forms " drawing for place layout
Cloth ", for total group place intelligent distribution optimization algorithm input space data；Dock place model includes two kinds of pels of line segment and rectangle
Type, line segment is place referring to line segment, and provides horizontal and vertical mark；Rectangular element indicates total group place, channel, barrier
Etc. different zones type, different area types different color is provided and is distinguished；
Ship type data are indicated as unit of being segmented, and the volume size of segmentation is indicated with the volume of the minimum bounding box of segmentation, are constituted
" pel " in the layout of place, specific data model are as follows: with the coordinate data of this bounding box six end faces in hull coordinate system
It indicates；This six data are denoted as stern end face, bow end face, left side, right side, lower end surface, upper surface respectively；
Total group plan is that dock always organizes the plan that block assembly and carrying are carried out on place, and available segmentation occupied ground rises
Begin time and end time, for total group place intelligent distribution optimization algorithm input time data, total planning data model of organizing includes
Name of vessel always organizes block, block, segmentation, positioning plan, place station, carries plan；
Crane data module and lifting time module, the data of crane data module essential record crane are corresponding always to organize in place
Crane specific data include loaded-up condition, crane capacity, span, deck-molding, cardinal distance, overlap distance, the distance that is staggered, power supply,
Power supply mode, rail model, stroke；Lift the segmentation of time data model definitions different zones, block carries out total group, carries when institute
The standard of occupancy lifts the time, and specific data type includes: sessions, sectional area, segment encoding, weight coefficient, hook
Time, anti-body time, positioning time, uncoupling time, surplus cutting, resetting time, total ascent time.
3. a kind of required shipyard always organizes place intelligent distribution optimization algorithm according to claim 1, which is characterized in that place cloth
Office's intelligent optimization algorithm and crane plan intelligent optimization algorithm are particle swarm optimization algorithm, the difference is that place layout intelligence is excellent
Change algorithm mainly to optimize space, and crane plan intelligent optimization algorithm mainly optimizes the time；Particle group optimizing
The position and speed iterative formula of algorithm are as follows:
vi,d(t+1)=ω vi,d(t)+c1r1(pi,d-xi,d(t))+c2r2(pg,d-xi,d(t))
xi,d(t+1)=xi,d(t)+vi,d(t+1) (d=1,2 ..., N)
In formula, ω is Inertia Weight；r1And r2It is equally distributed random number, c between 0~11And c2It is Studying factors；ω's takes
It is worth formula are as follows:
In formula, ωmaxFor maximum inertia weight；ωminFor minimum inertia weight；Run is current iteration number；runmaxFor algorithm
The total degree of iteration.
4. a kind of shipyard according to claim 1 always organizes place intelligent distribution optimization algorithm, which is characterized in that place layout
Intelligent optimization algorithm uses normal distribution assignment, while coal addition position optimization algorithm strategy: Y axis coordinate moves positive optimization algorithm, Y-axis
Coordinate faces the dynamic optimization algorithm of displacement, X axis coordinate moves positive optimization algorithm, X axis coordinate faces displacement dynamic optimization algorithm, X axis coordinate normotopia
Search for mobile optimization algorithm；And the fitness evaluation letter of particle swarm optimization algorithm involved in the layout intelligent optimization algorithm of the place
Number, the concrete form of function are as follows:
In formula, ωGTotal energy consumption weight is lifted by crane for gantry crane；ωTTime-consuming weight is lifted by crane for gantry crane；ωXFor handling X-direction energy consumption
Weight；ωYFor handling Y-direction energy consumption weight；GiFor i-th of block quality；T is scheme total time-consuming；LXiFor i-th of block of handling
X-direction distance；LYiFor i-th of block Y-direction distance of handling.
5. a kind of shipyard according to claim 1 always organizes place intelligent optimization placement algorithm, which is characterized in that crane plan
Intelligent optimization algorithm is with crane resources, manpower working hour, working time, carrying/total group network always to organize place placement scheme
Constraint, balances two gantry crane working loads in same dock, and time and energy consumption synthesis are lifted under same lifting object amount to reach
Minimum purpose is consumed, while the step-length for solving " step down, wait " state of gantry crane solves, and obtains lifting plan.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116167143A (en) * | 2023-04-20 | 2023-05-26 | 江西少科智能建造科技有限公司 | Station arrangement method, system, storage medium and equipment |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106951621A (en) * | 2017-03-13 | 2017-07-14 | 江苏科技大学 | A kind of simulation optimization method that Plan rescheduling is carried for ship |
| US20180253085A1 (en) * | 2017-03-06 | 2018-09-06 | Korea Institute Of Ocean Science & Technology | Integrated solution for simulation-based production of ships and offshore plants |
-
2018
- 2018-11-08 CN CN201811326023.6A patent/CN109345038A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180253085A1 (en) * | 2017-03-06 | 2018-09-06 | Korea Institute Of Ocean Science & Technology | Integrated solution for simulation-based production of ships and offshore plants |
| CN106951621A (en) * | 2017-03-13 | 2017-07-14 | 江苏科技大学 | A kind of simulation optimization method that Plan rescheduling is carried for ship |
Non-Patent Citations (1)
| Title |
|---|
| 李翼;吕建军;甄希金;: "面向船坞总组场地的分段调度管理技术研究", 船舶工程 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116167143A (en) * | 2023-04-20 | 2023-05-26 | 江西少科智能建造科技有限公司 | Station arrangement method, system, storage medium and equipment |
| CN116167143B (en) * | 2023-04-20 | 2023-08-15 | 江西少科智能建造科技有限公司 | Station arrangement method, system, storage medium and equipment |
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