CN113988918A - System and method for estimating daily rent of ship - Google Patents

System and method for estimating daily rent of ship Download PDF

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CN113988918A
CN113988918A CN202111234479.1A CN202111234479A CN113988918A CN 113988918 A CN113988918 A CN 113988918A CN 202111234479 A CN202111234479 A CN 202111234479A CN 113988918 A CN113988918 A CN 113988918A
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王群
陆旭昇
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Cosco Shipping Technology Co Ltd
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Cosco Shipping Technology Co Ltd
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Abstract

The invention provides a system and a method for estimating daily rent of a ship, the system comprises an information acquisition module, a data support module, a daily rent estimation module and a man-machine interaction module, the man-machine interaction module is respectively connected with the information acquisition module, the data support module and the daily rent estimation module, the information acquisition module and the data support module are both connected with the daily rent estimation module, the information acquisition module acquires freight data and carrying data selected by a user, the data support module provides historical data as a data base and a support for daily rent estimation and man-machine interaction, the rent estimation module receives the data of the information acquisition module and the data support module, an algorithm model is established based on the continuity principle of a time series algorithm, the daily rent of the ship is estimated and calculated through the algorithm model, and the estimated daily rent of the ship is displayed to the user through the man-machine interaction module, the method provides income prediction of rent of the next day of single-ship voyage for the user and provides decision assistance for subsequent ship and goods transaction.

Description

System and method for estimating daily rent of ship
Technical Field
The invention relates to the technical field of ship transportation, in particular to a system and a method for estimating daily rent of a ship.
Background
With the development of economy, the transport business of massive commodities in the coast is busy, and under the popularization and deep fusion of internet technology, the commercial change of the current internet era increasingly permeates into various fields of the traditional industry, so that the existing business mode and rule system are changed or even overturned. The shipping industry is a traditional industry spanning hundreds of years, and inevitably faces the problems of rail contact with the development of times, deep electric shock and the like.
With the development of big data related technology, the intelligent calculation of daily rent of a ship for a single voyage becomes possible, and the daily rent of the ship is combined with the big data technology, so that the shipowner can predict freight benefits in advance, and decision assistance is provided for the follow-up transaction of the ship and goods.
At present, daily rent of a ship is mainly calculated by data such as ship freight volume, freight rate, loading and unloading port distance, loading and unloading port use fee, fuel oil cost, total loading and unloading stop time, ship renting cost, ship loading and unloading port positions, port sequence, navigational speed, navigational oil consumption and the like in combination with algorithms such as time sequence, fitting and the like, the calculation method cannot efficiently provide income prediction of rent of the ship on the next day of single ship voyage for users, and the time for the users to screen pallets and ships is very long.
Disclosure of Invention
In order to solve various problems in the existing estimation of daily rent of ships, the invention provides a system for estimating the daily rent of the ships, which is used for providing income prediction of daily rent of a single ship for a user with the demand of transporting bulk goods on the coast, providing decision assistance for subsequent ship transaction, reducing the time for the user to screen pallets and ships and improving the working efficiency. The invention also relates to a ship daily rent estimation method.
The technical scheme of the invention is as follows:
a ship daily rent estimation system is characterized by comprising an information acquisition module, a data support module, a daily rent estimation module and a human-computer interaction module, wherein the human-computer interaction module is respectively connected with the information acquisition module, the data support module and the daily rent estimation module, and the information acquisition module and the data support module are both connected with the daily rent estimation module;
the information acquisition module acquires freight data and carrying data selected by a user in the human-computer interaction module;
the data support module provides freight data, carrying data, freight data and industry information data which are expected to be used as a data base and support for daily rent estimation and man-machine interaction;
the daily rent estimation module receives the freight data and the carrying data which are acquired by the information acquisition module and selected by a user, establishes an algorithm model comprising a service level and a cost level based on the continuity principle of a time series algorithm by combining the data provided by the data support module, obtains port distance data, ship speed data and fuel cost data through estimation and calculation of the algorithm model, generates a ship daily rent through estimation and calculation, and feeds back the estimated ship daily rent result to the man-machine interaction module;
and the man-machine interaction module is provided with a pallet for the freight requirement selected by the user, a shipper and a selection confirmation instruction, and displays the estimation result of the algorithm model of the daily rent estimation module.
Preferably, the freight data includes origin port, destination port, port spacing, loading port usage, unloading port usage, days of loading and unloading, cargo type, freight volume and intention price, and the carrying data includes ship parameters, air port, freight rate, exchange rate, fuel cost and carrying date.
Preferably, the ship parameters include ship name, ship full-load speed, ship ballast speed, ship full-load voyage consumption, ship ballast voyage consumption, ship berth consumption, and ship cargo quota.
Preferably, the data support module further combines data cleansing technology to convert and cleanse data.
Preferably, the daily rent estimation module generates expense level data and service level data through estimation and calculation, the expense level data further comprises voyage net profit and marginal profit besides the daily rent of the ship, the service level data comprises total voyage days, total berthing days and total voyage period, the service level data respectively presents the prediction benefits of the service level data and the expense level data, and assistance is provided for user decision making.
A ship daily rent estimation method is characterized by comprising the following steps:
an information acquisition step: collecting shipping data and carrier data selected by a user;
a data supporting step: providing expected used freight data, carrying data, freight data and industry information data as a data base and support for daily rent estimation and human-computer interaction;
day rent estimation step: receiving freight data and carrying data in the information acquisition step, establishing an algorithm model comprising a service level and a cost level based on the continuity principle of a time series algorithm by combining the data provided in the data support step, obtaining port distance data, ship speed data and fuel cost data through estimation and calculation of the algorithm model, generating ship daily rent through estimation and calculation, and feeding back an estimated ship daily rent result to the man-machine interaction step;
a man-machine interaction step: pallets with shipping requirements for selection by the user, shipments of ships, and selection confirmation instructions, and presenting the estimation results of the algorithm model of the daily rental estimation step.
Preferably, the freight data in the information collecting step includes an origin port, a destination port, a port distance, a freight port usage fee, an unloading port usage fee, a loading and unloading number of days, a cargo type, a freight volume, and an intention price, and the carrying data includes ship parameters, an empty port, a freight rate, an exchange rate, a fuel cost, and a carrying date.
Preferably, the ship parameters include ship name, ship full-load speed, ship ballast speed, ship full-load voyage consumption, ship ballast voyage consumption, ship berth consumption, and ship cargo quota.
Preferably, the data in the data support step is further combined with a data cleansing technique to convert and cleanse the data.
Preferably, the daily rent estimation step generates expense level and service level data through estimation and calculation, the expense level data further comprises voyage net profit and marginal profit besides the daily rent of the ship, and the service level data comprises total voyage days, total berthing days and total voyage period, respectively presents the prediction benefits of the service level and the expense level data, and provides assistance for the decision of the user.
The invention has the beneficial effects that:
the invention provides a daily rent estimation system for ships, which comprises an information acquisition module, a data support module, a daily rent estimation module and a man-machine interaction module, wherein the information acquisition module acquires freight data and carrying data selected by a user in the man-machine interaction module; the data support module provides freight data, carrying data, freight data and industry information data which are expected to be used as a data base and support for daily rent estimation and man-machine interaction; the daily rent estimation module receives the freight data and the carrying data which are collected by the information collection module and selected by a user, combines the data provided by the data support module, establishes an algorithm model comprising a service level and a cost level based on the continuity principle of a time series algorithm, obtains port distance data, ship speed data and fuel cost data through the estimation and calculation of the algorithm model, further generates ship daily rent through estimation and calculation, and feeds back the estimated ship daily rent result to the man-machine interaction module, the man-machine interaction module is provided with a pallet, a carrying ship plate and a selection confirmation instruction which are used for the freight requirement selected by the user, displays the estimation result of the algorithm model of the daily rent estimation module, establishes the algorithm model for estimation, namely the core function of the ship daily rent estimation system, the input parameters of the algorithm relate to user information collection and data support, and after the function is screened and processed, the system has the advantages that the service user is fed back with the daily rent of a single ship, the benefit prediction data of the service level and the financial level are respectively presented, assistance is provided for user decision making, the benefit prediction of the daily rent of the single ship is provided for the user conveniently and efficiently, the system is very convenient and efficient to use from the user experience level, the user (such as a shipowner and a shipowner) only inputs retrieval conditions, the pallet or ship information highly meeting relevant conditions can be retrieved and selected by clicking a button in the human-computer interaction module, the time for the user to select the pallet and the ship is greatly reduced, the working efficiency is improved, and the convenience and the high efficiency of the system are fully exerted. In terms of basic data support, the system makes full use of the collected freight data, carrying data, freight data, industry information data and the like as data support during estimation, and can further combine with a data cleaning technology to convert and clean the data, so that the working stability and estimation accuracy of the system are greatly improved. The system provides income prediction of daily rent of a single ship for a user with the demand of transporting bulk goods on the coast, and provides decision assistance for subsequent ship goods transaction.
The invention also relates to a method for estimating the daily rent of a ship, which corresponds to the system for estimating the daily rent of the ship and can be understood as the implementation method based on the system for estimating the daily rent of the ship, the method comprises an information acquisition step, a data support step, a daily rent estimation step and a man-machine interaction step, firstly, freight data and carrying data selected by a user are acquired, and freight data, carrying data, freight charge data and industry information data which are expected to be used are provided as data bases and supports for daily rent estimation and man-machine interaction, then, an algorithm model comprising a service level and a cost level is established based on the continuity principle of a time series algorithm according to the acquired freight demand data and the collected support data of the user, and port distance data, ship speed data and fuel cost data are obtained through estimation and calculation of the algorithm model, and then, the daily rent of the ship is generated through pre-estimation calculation, the estimated daily rent result of the ship is fed back to the user, a pallet, a ship plate and a selection confirmation instruction for the user to select the freight requirement are provided, the estimation result of the algorithm model of the daily rent estimation is displayed, the time for the user to screen the pallet and the ship is shortened, and the working efficiency is improved. The method mainly predicts the benefits of marine transportation of bulk commodities on the coast of China, so that the user can predict the benefits in advance before actual transaction and provide decision assistance for subsequent transaction.
Drawings
Fig. 1 is a schematic view of the overall structure of the daily rental evaluation system for ships according to the present invention.
Fig. 2 is a schematic diagram of the operation of the daily rental evaluation system for ships according to the present invention.
Fig. 3 is a schematic flow chart of the daily rent estimation method for ships according to the invention.
FIG. 4 is a schematic diagram of the selection of the man-machine interaction module boat pallet.
FIG. 5 is a schematic view showing details of measurement and calculation of the human-computer interaction module interface according to the present invention.
FIG. 6 is a schematic diagram of single-voyage daily rent and remaining days for arrival at a loading port of the human-computer interaction module of the present invention.
FIG. 7 is a schematic diagram of single-voyage daily rent, voyage period, and voyage income of the human-computer interaction module of the present invention.
Detailed Description
The present invention will be described with reference to the accompanying drawings.
The invention relates to a ship daily rent estimation system, which comprises an information acquisition module, a data support module, a daily rent estimation module and a human-computer interaction module, wherein the human-computer interaction module is respectively connected with the information acquisition module, the data support module and the daily rent estimation module; the data support module provides freight data, carrying data, freight data and industry information data which are expected to be used as a data base and support for daily rent estimation and man-machine interaction; the daily rent estimation module receives the freight data and the carrying data which are collected by the information collection module and selected by a user, establishes an algorithm model comprising a service level and a cost level based on the continuity principle of a time series algorithm by combining the data provided by the data support module, obtains intermediate data comprising port distance data, ship speed data, fuel cost data and the like through the estimation and calculation of the algorithm model, generates a ship daily rent through estimation and calculation, and feeds back the estimated ship daily rent result to the man-machine interaction module; the man-machine interaction module is provided with a pallet for the freight demand selected by a user, a shipper carrying plate and a selection confirmation instruction, and displays the estimation result of an algorithm model of the daily rent estimation module, the system is essentially a single-voyage daily rent estimation system of a large commodity transport ship in China based on the algorithm model, and by acquiring the freight transport demand of large bulk cargoes and combining with the automatic analysis and processing of a background algorithm mode, the system provides the user with measurement and calculation data of a business level and a cost level.
The present invention will be further described in detail with reference to the accompanying drawings.
Fig. 1 is a schematic diagram of the overall structure of a daily rent estimation system for a ship according to the present invention, which includes an information acquisition module, a data support module, a daily rent estimation module, and a human-computer interaction module, wherein the information acquisition module is configured to acquire shipping data and carrying data selected by a user in the human-computer interaction module, and specific ship information, and recommend a ship or cargo to be matched to a pallet or ship selected by the user; preferably, as shown in the schematic diagram of fig. 2, the freight data includes data of origin port, destination port, port distance, usage charge of loading port, usage charge of unloading port, loading and unloading days, cargo type, freight volume and intention price, and the carrying data includes data of ship parameters, empty port, freight rate, lag/speed rate, exchange rate, total stop time of port, fuel cost and carrying date. Further preferably, the ship parameters include ship name, daily rental of the ship, full-load speed of the ship, no-load speed of the ship, full-load consumption of the ship, ballast consumption of the ship, berthing consumption of the ship, and cargo rating of the ship.
Wherein, the goods are bulk goods types needing to be collected and used as option; the freight volume is the transport tonnage of the goods required to be collected, is an important basis for daily rent of ships and is taken as a necessary item; freight rate is the cost related to the freight rate of the pallet, and comprises two data of yuan/ton and yuan/ton/day as mandatory items; fuel cost is the cost-variant component of the estimation method for the FO (fuel) and DO (diesel) prices that need to be collected as a mandatory item; the loading and unloading port and the corresponding port use fee, the algorithm model combines the data item to provide the distance mileage of the voyage section, and the corresponding voyage day and ton day profits are calculated according to the data item, and as a result, the important component of the daily rent of the ship is used as a necessary item.
Important measurement results such as ton day margin profit and equivalent foreign trade rent relate to port spacing data in a port spacing library, and the data are mainly obtained from an external network. The port charge of loading and unloading goods and port use charge are important components of daily rent estimation of a single ship, the data are obtained from media such as an industry website through an interface, and a man-machine interaction interface is provided for a user to directly carry out operations such as addition, modification and deletion.
The data support module is used for providing collected historical data of expected use, wherein the collected historical data comprises freight data, carrying data, freight data, goods source data, fuel cost data and historical transaction data, data such as coastal bulk cargo transportation freight rates, market conditions, industry news, industry information and industry freight rate indexes are obtained through web crawlers on the internet, and data such as coastal port intervals and coastal port use fees obtained based on a data fitting algorithm are used as data bases and supports for single-ship daily rent estimation and man-machine interaction, and the data support module is combined with a data cleaning technology to convert and clean the data, delete repeated information, correct errors and provide data consistency.
The daily rent estimation module receives the freight data and the carrying data which are acquired by the information acquisition module and selected by a user, establishes an algorithm model comprising a service level and a cost level based on the continuity principle of a time series algorithm by combining the data provided by the data support module, obtains port distance data, ship speed data, fuel cost data and the like through estimation and calculation of the algorithm model, generates a ship daily rent through estimation and calculation, and feeds back the estimated ship daily rent result to the man-machine interaction module; specifically, the daily rent of the ship is calculated according to freight rate, cargo allocation quantity of the ship, port use charge for loading and unloading, fuel cost, sailing days, port distance, sailing oil consumption, port mileage, ship speed and other data to generate total voyage, total voyage cost and single voyage period, and then the daily rent of the ship is calculated by combining the calculated total voyage, total voyage cost and single voyage period with the exchange rate.
Preferably, the daily rent estimation module generates expense level data and service level data through estimation and calculation, the expense level data further comprises voyage net profit and marginal profit besides the daily rent of the ship, the service level data comprises total voyage days, total berthing days and total voyage period, the service level data respectively presents the prediction benefits of the service level data and the expense level data, and assistance is provided for the decision of the user.
The human-computer interaction module is provided with a pallet of freight requirements for selection by a user, a shipper and a selection confirmation instruction, such as human-computer interaction interface diagrams shown in fig. 4-7, showing the estimation result of the algorithm model of the daily rent estimation module. FIG. 4 is a schematic diagram of a human machine interaction module pallet selection showing pallets for user selection and a pallet pre-assembly of the present invention; FIG. 5 shows the details of the calculations; FIG. 6 shows a single-voyage daily rental and the remaining days to port; FIG. 7 shows single-voyage daily rent, voyage period, and voyage revenue.
The invention has also related to a ship day rent estimation method, this method corresponds to above-mentioned ship day rent estimation system, can understand as the realization method of the above-mentioned ship day rent estimation system, this method includes information acquisition step, data support step, day rent estimation step and man-machine interaction step, as the flow shown in fig. 3, first of all is the information acquisition step, gather the data of freight transportation and carrying the data selected by the user; then, a data support step, namely providing expected used historical data including freight data, carrying data, freight data and industry information data as a data base and support for daily rent estimation and man-machine interaction, and converting and cleaning the data by combining a data cleaning technology; and a daily rent estimation step, namely receiving the freight data and the carrying data selected by the user and collected in the information collection step, combining the data provided in the data support step, establishing an algorithm model comprising a service level and a cost level based on the continuity principle of a time series algorithm, obtaining port distance data, ship speed data, fuel cost data and the like through estimation and calculation of the algorithm model, further generating a ship daily rent through estimation and calculation, feeding the estimated daily rent back to the human and interaction step, and finally, carrying out a man-machine interaction step, wherein the pallet, a carrying ship plate and a selection confirmation instruction of the freight requirement selected by the user are provided, and the estimation result is displayed to the user algorithm model.
Preferably, the daily rent estimation step generates expense level and service level data through estimation and calculation, the expense level data further comprises voyage net profit and marginal profit besides the daily rent of the ship, the service level data comprises total voyage days, total berthing days and total voyage period, the service level data respectively presents the prediction benefits of the service level and the expense level data, and assistance is provided for user decision making.
Preferably, the freight data in the information collecting step includes an origin port, a destination port, a port distance, a freight port usage fee, an unloading port usage fee, a loading and unloading number of days, a kind of cargo, a freight volume, and an intention price, and the carrying data includes ship parameters, an empty port, a freight rate, an exchange rate, a fuel cost, and a carrying date.
Preferably, the ship parameters include ship name, ship daily rent, ship full-load speed, ship ballast speed, ship full-load voyage consumption, ship ballast voyage consumption, ship berth consumption, and ship cargo quota.
Example (b):
the specific operation flow of the system for estimating the daily rent of a single ship is as follows:
firstly, a user selects transportation demand information (namely freight transportation and carrying demand information) of a certain cargo on a front-end interface (a man-machine interaction interface belonging to a man-machine interaction module) of the system, wherein the freight volume, freight rate, lag time/fast dispatch fee, total stop time of a port, loading and unloading are indispensable items in the transportation demand information, other data items are optional items, and after the information is selected, a matching mechanism is triggered, and the information is fed back to a background as an invitation by the front end of the system.
And secondly, after the background receives the information parameters, the data support module is combined to respectively calculate the data such as the distance between the freight loading and unloading ports, the exchange rate of the RMB to the dollar, the fuel price rate, the daily rent level from the ship empty port to the loading port and then to the unloading port and the like based on a data fitting algorithm.
And then, combining the demand information and the calculated data, inputting the demand information serving as basic data parameters of a rent estimation module, performing estimation calculation to generate daily rent of the ship after screening and processing, and gradually performing estimation calculation to obtain cost level and service level data such as voyage net profit, marginal profit, operating day marginal profit, ton day marginal equivalent foreign trade rent, total voyage day, total berthing day, total voyage period and the like, wherein the system feeds the data back to the front end of the user after obtaining the data.
Finally, after receiving the data parameters, the system front end feeds back the daily rent results of the ships to a user front end interface, and a user can provide decision assistance for the transaction of the ships and goods according to estimated daily rent statistics and other fed-back parameters.
The invention provides an objective and scientific system and method for estimating daily rent of ships, which innovatively apply big data correlation technology to coastal bulk commodity transportation business, provide income prediction of daily rent of a single ship for users with coastal bulk cargo transportation demands, provide decision assistance for subsequent ship transaction, greatly reduce the time for users to screen pallets and ships, and improve the working efficiency.
It should be noted that the above-mentioned embodiments enable a person skilled in the art to more fully understand the invention, without restricting it in any way. Therefore, although the present invention has been described in detail with reference to the drawings and examples, it will be understood by those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention.

Claims (10)

1. A ship daily rent estimation system is characterized by comprising an information acquisition module, a data support module, a daily rent estimation module and a human-computer interaction module, wherein the human-computer interaction module is respectively connected with the information acquisition module, the data support module and the daily rent estimation module, and the information acquisition module and the data support module are both connected with the daily rent estimation module;
the information acquisition module acquires freight data and carrying data selected by a user in the human-computer interaction module;
the data support module provides freight data, carrying data, freight data and industry information data which are expected to be used as a data base and support for daily rent estimation and man-machine interaction;
the daily rent estimation module receives the freight data and the carrying data which are acquired by the information acquisition module and selected by a user, establishes an algorithm model comprising a service level and a cost level based on the continuity principle of a time series algorithm by combining the data provided by the data support module, obtains port distance data, ship speed data and fuel cost data through estimation and calculation of the algorithm model, generates a ship daily rent through estimation and calculation, and feeds back the estimated ship daily rent result to the man-machine interaction module;
and the man-machine interaction module is provided with a pallet for the freight requirement selected by the user, a shipper and a selection confirmation instruction, and displays the estimation result of the algorithm model of the daily rent estimation module.
2. The system according to claim 1, wherein the shipping data includes origin port, destination port, port spacing, port usage charges, days of loading and unloading, cargo type, volume and intent price, and the shipping data includes ship parameters, port availability, freight rate, exchange rate, fuel cost and date of delivery.
3. The system of claim 2, wherein the vessel parameters include a vessel name, a vessel full-load voyage speed, a vessel ballast voyage speed, vessel full-load voyage consumption, vessel ballast voyage consumption, vessel berth consumption, and vessel cargo rating.
4. The system according to claim 1, wherein the data support module further incorporates data cleansing techniques to convert and cleanse the data.
5. The system for estimating daily rent of a ship according to claim 1, wherein the daily rent estimation module generates fee level and business level data through estimation and calculation, the fee level data further comprises net voyage profit and marginal profit besides the daily rent of the ship, and the business level data comprises total voyage days, total berthing days and total voyage period, respectively presents the prediction benefits of the business level and the fee level data, and provides assistance for user decision making.
6. A ship daily rent estimation method is characterized by comprising the following steps:
an information acquisition step: collecting shipping data and carrier data selected by a user;
a data supporting step: providing expected used freight data, carrying data, freight data and industry information data as a data base and support for daily rent estimation and human-computer interaction;
day rent estimation step: receiving freight data and carrying data in the information acquisition step, establishing an algorithm model comprising a service level and a cost level based on the continuity principle of a time series algorithm by combining the data provided in the data support step, obtaining port distance data, ship speed data and fuel cost data through estimation and calculation of the algorithm model, generating ship daily rent through estimation and calculation, and feeding back an estimated ship daily rent result to the man-machine interaction step;
a man-machine interaction step: pallets with shipping requirements for selection by the user, shipments of ships, and selection confirmation instructions, and presenting the estimation results of the algorithm model of the daily rental estimation step.
7. The method of claim 6, wherein the shipping data in the step of collecting information includes origin port, destination port, port distance, port usage charge, number of days of loading and unloading, type of cargo, volume of cargo and intention price, and the shipping data includes shipping parameters, port empty, rate of freight, exchange rate, fuel cost and date of delivery.
8. The method of claim 7, wherein the ship parameters include ship name, ship full load speed, ship ballast speed, ship full load consumption, ship ballast consumption, ship berth consumption, and ship cargo quota.
9. The method of claim 6, wherein the data in the step of supporting the data is further combined with a data washing technique to convert and wash the data.
10. The method for estimating the daily rent of the ship according to claim 6, wherein the daily rent estimation step generates expense level data and business level data through estimation and calculation, the expense level data further comprises net voyage profit and marginal profit besides the daily rent of the ship, and the business level data comprises total voyage days, total berthing days and total voyage period, respectively presents the prediction benefits of the business level data and the expense level data, and provides assistance for user decision making.
CN202111234479.1A 2021-10-22 2021-10-22 System and method for estimating daily rent of ship Pending CN113988918A (en)

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