Method for generating carrier transportation data and related electronic device

Abstract

A method performed by an electronic device for generating carrier transit data is disclosed. The method includes obtaining historical data associated with one or more previous shipments involving external transit. The method includes predicting an external turn time parameter indicative of a turn time of a container handled by external transit, e.g., by applying a predictive model to the historical data. The method includes generating carrier transit data associated with a carrier transit service based on the predicted external turn time parameter. The method includes communicating the carrier transit data to a user for use in booking a shipment.

Description

[0001] This disclosure relates to the field of transportation and freight. Specifically, it relates to a method for generating carrier transportation data and related electronic devices. Background Technology

[0002] The shipment of goods involves the arrival of the goods at a terminal (e.g., a port) and / or their subsequent departure from the terminal. For example, a shipment is carried out by the carrier (such as the shipper). Transportation may involve, for example, using trucks to transport the goods to a terminal (such as a port) and / or from the terminal. For example, transportation may be provided by a carrier service (such as the shipper). Alternatively, transportation may also be provided by an external transportation service (such as a third-party transportation service). Summary of the Invention

[0003] When booking, it can be difficult to effectively predict which mode of transport—either a carrier's or an external transport service—is the most efficient. Despite the complexity, an electronic device and method are needed to address the issues of transparency and predictability regarding transport services at the time of booking.

[0004] Therefore, there is a need for an electronic device and method for generating carrier transportation data to mitigate, alleviate or resolve existing deficiencies and provide dynamic and more accurate transportation data forecasts based on the turnaround time of external transportation services.

[0005] A method, executed by an electronic device, is disclosed for generating carrier transportation data. The method includes obtaining historical data associated with one or more previous shipments involving external transportation. The method includes, for example, predicting an external turnaround time parameter by applying a predictive model to the historical data, the parameter indicating the turnaround time of containers disposed of via external transportation. The method includes generating carrier transportation data associated with a carrier transportation service based on the predicted external turnaround time parameter. The method includes transmitting the carrier transportation data to a user for booking shipments.

[0006] An electronic device is disclosed, the electronic device including a memory circuit system, a processor circuit system, and an interface, wherein the electronic device is configured to perform any of the methods disclosed herein.

[0007] A computer-readable storage medium is disclosed for storing one or more programs, the programs including instructions that, when executed by an electronic device (optionally having a display and a touch-sensitive surface), cause the electronic device to perform any of the methods disclosed herein.

[0008] One advantage of this disclosure is that the disclosed electronic device and method provide carrier transportation data utilizing predicted turnaround times of external transportation services. The disclosed carrier transportation data is advantageously generated using external turnaround time parameters of external transportation services, which are predicted by applying a predictive model to historical data related to external transportation. In this way, the carrier transportation data can adapt to various changes despite complex situations, and thus becomes more accurate.

[0009] The publicly available carrier transportation data is advantageously provided to users at the time of booking shipment, displaying the estimated turnaround time for external transportation services. This allows for increased transparency and reduces the risk of additional costs, such as those due to demurrage and delays. In other words, thanks to the advance delivery of carrier transportation data, users have a clearer understanding of the estimated timeframe required for the return of empty containers. Attached Figure Description

[0010] The above and other features and advantages of this disclosure will be apparent to those skilled in the art from the following detailed description of exemplary embodiments with reference to the accompanying drawings, in which: Figures 1A to 1B This is a flowchart illustrating an exemplary method for generating carrier transportation data executed by an electronic device according to the present disclosure. Figure 2 This is a block diagram illustrating an exemplary electronic device according to the present disclosure, and Figure 3 An example user interface according to this disclosure is shown. Detailed Implementation

[0011] Various exemplary embodiments and details are described below with reference to the accompanying drawings, where applicable. It should be noted that the drawings may be drawn to scale or not, and elements with similar structure or function are indicated by the same reference numerals in all the drawings. It should also be noted that the drawings are intended only to facilitate the description of embodiments. The drawings are not intended as an exhaustive description of this disclosure or a limitation on the scope of this disclosure. Furthermore, the illustrated embodiments need not possess all the aspects or advantages shown. Aspects or advantages described in connection with a particular embodiment are not necessarily limited to that embodiment and can be practiced in any other embodiment, even if not so illustrated or explicitly described.

[0012] For clarity, the accompanying drawings are schematic and simplified, and only details that aid in understanding this disclosure are shown, while other details are omitted. Throughout, the same reference numerals are used for the same or corresponding parts.

[0013] The term "container" as used herein refers to the shell in which goods are packaged for transport. For example, a container can be considered a box. The term "goods" as used herein refers to objects to be placed in a container for transport. For example, goods can be considered cargo, such as freight, such as objects to be shipped. It should be noted that the term "goods" is used interchangeably with "goods." For example, goods can include commodities that can be placed in a container, such as consumer goods from large manufacturing companies, such as shoes, clothing, toys, and other general consumer goods, as well as fast-moving consumer goods such as packaged food, beverages, cosmetics, and pharmaceuticals. For example, the term "goods" as used herein can be considered as goods that can be packaged, for example, in rectangular stackable cartons having variable weight and volume.

[0014] The goods disclosed herein can be considered as objects and / or articles to be placed in a container for shipment. For example, goods can be considered as cargo articles, such as freight articles, such as objects to be shipped. For example, goods can be considered as articles that can be loaded into a container. It should be noted that in some examples, the term "goods" can be used interchangeably with the term "cargo". For example, goods can include articles that can be placed in a container, such as merchandise. For example, merchandise can include materials such as petroleum, crude oil, raw materials, timber, industrial raw materials, plants (such as fruits and vegetables, seeds, beans), food (such as tea, coffee), fertilizer, paper, metals, chemicals, vehicles, fossil fuels, stone, tiles, glass, plastics, and rubber. For example, merchandise can include consumer goods manufactured by companies, such as shoes, clothing, toys, and other general consumer goods, as well as fast-moving consumer goods such as packaged food, beverages, cosmetics, and pharmaceuticals. Examples of goods include rectangular stackable cartons with variable weight and volume that are shipped in one or more dry containers.

[0015] For example, a shipment can be considered as the transport of goods from an origin location to a destination location. For example, a shipment can be considered as the loading of a container containing goods (e.g., one or more types of cargo). A shipment may involve one or more modes of transport, such as sea, land, and / or air transport. For example, a shipment can be performed using one or more of land vehicles, aircraft, and ocean-going vessels.

[0016] For example, the consignee can be considered an entity that has booked the shipment, such as an individual. For example, the shipment can be delivered to the consignee or to a location chosen by the consignee. The consignee can be considered, for example, an electronic device (such as...). Figure 2 Users of electronic devices (300).

[0017] Demurrage can be considered, for example, the time from the arrival of the container at the terminal and / or port to the collection of the container from the terminal and / or port for delivery to the recipient and / or destination (e.g., for unloading at least a portion of the container).

[0018] Detention can be considered as the time, for example, from the collection of a container from a terminal and / or port (e.g., for unloading) until the container returns to the terminal and / or port (e.g., after unloading). For example, the returned container may contain fewer items than it was before being shipped from the terminal and / or port to the recipient. In some examples, the returned container may be empty. In this document, demurrage and / or retention may be referred to as D&D.

[0019] Turnover time can be considered as the time from the arrival of a loaded container (e.g., a container containing goods) at the port to the return of an unloaded container (e.g., a container not containing goods, such as an empty container and / or a partially empty container) to the port. For example, the time between the arrival of a container at the terminal for unloading from a vessel and the time between the container's return to the terminal (e.g., after the container has been emptied of its contents destined for the recipient) can be considered turnover time. In some examples, turnover time includes only demurrage time. For example, turnover time can be considered as the time between the departure of a container from the terminal for delivery to the recipient and the return of the container to the terminal (e.g., after the container has been emptied of its contents destined for the recipient). Turnover time disclosed herein can include demurrage turnover time and / or demurrage turnover time. Demurrage turnover time is, for example, the time from the arrival of a container at the terminal and / or port to the collection of the container from the terminal and / or port for delivery to the recipient and / or destination (e.g., unloading at least a portion of the container). Detention and turnaround time is, for example, the time from when a container is collected from a terminal and / or port (e.g., unloaded) until it is returned to the terminal and / or port (e.g., after unloading). For example, the returned container may contain fewer items than it did before being shipped from the terminal and / or port to the recipient. In some examples, the returned container may be empty.

[0020] The extension time disclosed herein can be considered as an additional period of time (e.g., days, hours, etc.) required to return the container to the port. The extension time disclosed herein can also be considered, for example, as an additional period of time (e.g., days, hours, etc.) required to remove the container from the port and / or terminal. In other words, the granted extension time can be considered, for example, as additional time the container can be stored at the port. Extension time can be selected at the time of booking, and no additional fees will be incurred after booking. This extension time may be referred to as free extension time. The extension time can vary depending on the goods in the container, the consignee associated with the container, the attributes of the container (e.g., container type and / or size), port location, booking type, container type, etc. In some examples, the extension time may be granted by the competent authority associated with the port. In some examples, the extension period may be extended. For example, extension time can be selected for one or more containers (e.g., selected by the consignee).

[0021] For example, a booking request is based on user input provided by the user. In one or more examples, the booking request includes a user code, a country code, a product code, and a contract code. The user code, country code, product code, and / or contract code can be considered shipping attributes (e.g., characteristics). The user code, for example, indicates or is associated with a user. For example, the user code can be considered a user identifier associated with a user, such as an identifier uniquely associated with the user. The user identifier can, for example, include codes indicating the user's identity (e.g., including one or more letters and / or numbers, such as alphanumeric codes). The country code, for example, indicates a country. For example, the country code includes one or more letters and / or numbers indicating a country, such as alphanumeric codes. The product code, for example, indicates a product. For example, the product code can be considered a product number (e.g., a Harmonized System (HS) code). The product code includes, for example, codes (e.g., alphanumeric codes, numeric codes, strings, etc.). The contract code, for example, indicates the contract associated with the shipment. In other words, the contract code can indicate the contract (e.g., an agreement) performed on which a shipment (e.g., a historical shipment) associated with the user is based. The contract code includes, for example, codes (e.g., alphanumeric codes, numeric codes, strings, etc.). The contract code can be, for example, the contract number.

[0022] Transportation can be viewed as moving goods from a first location (such as a port and / or dock) to a second location (such as a warehouse and / or storage facility). For example, transportation can be viewed as the inland transport of containers and / or goods, such as transport to and from a port. For example, transportation may involve using one or more modes of transport (such as land transport) to move goods. For example, the transport of goods can be carried out via a type of truck, van, trailer, train, etc. For example, a shipment includes: a segment of the shipment / journey that is not a transport segment but is carried out by a cargo ship or cargo plane; and another segment used for transport, such as a transport segment. For example, the transport of containers and / or goods can be viewed as a transport segment of the journey and / or shipment. For example, transportation can be viewed as pre-shipment or post-shipment transport of goods. For example, pre-shipment transport of goods may include transporting goods to the origin of the shipment, such as a port and / or dock. Post-shipment transport of goods may include transporting goods from the destination of the shipment (such as a port and / or dock).

[0023] For example, when a container containing goods is to be shipped from Mumbai to Copenhagen, pre-shipment transportation may include moving the goods from a manufacturing and / or storage facility to the port of Mumbai, such as the origin of the shipment. The container will then be shipped to Copenhagen, such as the port of destination, via cargo ship. Post-shipment transportation may include (e.g., by truck) moving the goods from the port of Copenhagen to a storage facility and / or the final inland destination.

[0024] Transportation can be performed, for example, by a shipping provider (e.g., a carrier, such as a shipper). A shipping provider can be considered a provider of the shipment and / or transport of containers. In this disclosure, when transportation is performed by a shipping provider, the transportation is referred to as carrier transportation. When transportation is performed by a service provider different from the shipping provider, such as an external transportation service provider, the transportation in this disclosure is referred to as external transportation, such as third-party transportation and / or commercial transportation. In other words, commercial transportation can be considered as transportation not performed by a carrier provider.

[0025] During the journey of a container, the consignee plays a crucial role, as they receive the loaded containers and are responsible for returning the empty ones. To facilitate this, carrier providers (such as shippers) offer consignees a standard number of extension days for the return of the containers. However, if the consignee exceeds the allowed extension days, they may incur penalties known as demurrage and storage charges (D&D).

[0026] After the container loading is completed, the carrier on behalf of the user assumes responsibility for organizing and supervising the transport of the container from the port to the desired inland destination, which is accomplished through a transport service selected from carrier transport services and external transport services provided by the carrier. However, if the transport service exceeds the stipulated delay time for the return of the container, the user will ultimately bear the resulting D&D costs.

[0027] This disclosure addresses these challenges by predicting external turnaround time parameters for external transportation services and adapting carrier transportation data (e.g., including costs and times) to the predictions, thereby providing users with greater transparency when selecting transportation services. In particular, this disclosure allows for the anticipation and potential avoidance of unforeseen delays.

[0028] This disclosure can be viewed as predicting container turnaround times (referred to herein as external turnaround time parameters) for external transportation services (through the application of predictive models, such as machine learning and statistical modeling techniques), enabling the delivery of carrier services with adaptive carrier transportation data to users. For example, when the external turnaround time parameter exceeds a threshold and users may face demurrage and detention penalties, the disclosed technology allows for the prediction of such scenarios and the generation of carrier transportation data for use in delivering carrier transportation services to users during the scheduled shipment period, so that users can book such services.

[0029] When transport services are provided by external transportation services and / or carrier transportation services, this disclosure utilizes machine learning algorithms trained on historical data to generate accurate predictions of container turnaround times for a specific port. For example, by analyzing various parameters provided in historical data, this disclosure provides users with personalized insights into the expected duration of container handling. With this information, users can select carrier transportation services to mitigate the risk of penalties and optimize logistics planning through data-driven decision-making.

[0030] It is understood that external transport can be considered as primary transport, while carrier transport can be considered as secondary transport. In this disclosure, the terms "external transport" and "primary transport" are used interchangeably. Similarly, the terms "carrier transport" and "secondary transport" are used interchangeably.

[0031] Figures 1A to 1B A flowchart illustrating an exemplary method 100 performed by an electronic device is shown. Method 100 can be considered as a method for generating carrier transportation data, for example, a method for transmitting carrier transportation data according to this disclosure. Method 100 can be performed by an electronic device, such as the electronic devices disclosed herein, such as... Figure 2 Electronic device 300.

[0032] Method 100 includes obtaining, in S102, historical data associated with one or more previous shipments involving external transportation. Historical data can be viewed, for example, as prior (e.g., past) data associated with, for example, one or more previous shipments by a user. For instance, historical data includes data associated with one or more previous shipments involving external transportation within a given time period (e.g., the previous year). Historical data may include, for example, data associated with one or more previous shipments involving external transportation in the year or years preceding the user's current booked shipment. In one or more example methods, historical data includes one or more of the following: historical transaction data associated with previous transactions, historical container movement data indicating container movement, historical transportation data of external transportation and / or carrier transportation, and historical seasonality factors of external transportation and / or carrier transportation.

[0033] For example, historical transaction data can be viewed as historical data associated with a transaction. This transaction might be a booking, such as a booking for one or more previous shipments involving external transportation and / or carrier transport, including booking data and transaction details. Obtaining historical data, for example, involves obtaining historical transaction data associated with one or more previous shipments involving external transportation and / or carrier transport, provided to multiple users over a period of time, for one or more users, such as a user at a given location.

[0034] For example, historical container movement data indicates container movements associated with one or more previous shipments involving external transport and / or carrier transport.

[0035] Historical transportation data, for example, indicates external transportation and / or carrier transportation associated with one or more previous shipments. For instance, historical transportation data may indicate historical turnaround times associated with one or more previous shipments involving external transportation and / or carrier transportation.

[0036] For example, historical seasonality factors indicate seasonality associated with one or more previous shipments involving external transportation and / or carrier transportation.

[0037] In some examples, historical data is extracted from one or more sources, such as one or more databases, which for example store booking data and / or container movement data.

[0038] The method 100 includes predicting an external turnaround time parameter S118, which indicates the turnaround time of containers disposed of via external transportation, optionally by applying a prediction model to historical data in S118A. For example, the external turnaround time parameter indicates the time period for an external carrier to return a container. In other words, the external turnaround time parameter disclosed herein can be considered as a parameter indicating the turnaround time of containers transported by external carriers. For example, the external turnaround time parameter may include a value indicating the turnaround time of a container (e.g., an integer, a decimal, etc.).

[0039] For example, a predictive model can be viewed as a model configured to predict external turnaround time parameters by taking historical data as input. In other words, for example, the predictive model is applied to one or more of the following: historical transaction data associated with previous transactions, historical container movement data indicating container movement, historical transportation data of external transport and / or carrier transport, and historical seasonal factors of external transport and / or carrier transport.

[0040] Method 100 includes generating carrier transportation data associated with a carrier transportation service, S120, based on predicted external turnaround time parameters. The carrier transportation service can be considered as a transportation service provided by the shipping carrier (e.g., the shipper). The carrier transportation data can be considered as data indicative of carrier transportation, such as carrier transportation value (e.g., cost) and / or carrier transportation turnaround time. For example, carrier transportation data includes carrier transportation value (e.g., cost) and / or carrier transportation turnaround time. In one or more example methods, generating S120 carrier transportation data includes generating S120A carrier transportation data based on predicted external turnaround time parameters and container pickup parameters. For example, a user can provide (e.g., select) container pickup parameters. For example, container pickup parameters indicate a date, such as the date the carrier transportation company can pick up goods (e.g., shipped items). In one or more example methods, carrier transportation data includes container return parameters and / or carrier transportation value. Container return parameters may indicate the time period during which the carrier's transportation will be carried out, such as a projected time period. For example, container return parameters indicate the turnaround time of the carrier's transportation service, such as the date on which the carrier may return items (e.g., shipped items). The carrier's transportation value may indicate the charges and / or costs associated with selecting the carrier's transportation service. In other words, for the carrier's transportation service to be offered to the user, the user can pay the charges and / or costs indicated by the carrier's transportation value.

[0041] In one or more example methods, method 100 includes determining an external transport risk level (S121) based on the difference between external transport return parameters and container pickup parameters. The external transport return parameters can be considered as parameters providing a predicted date for container return, determined based on predicted external turnaround time parameters. For example, the external transport risk level indicates the risk of delays in container return, such as incurring D&D charges. In other words, the external transport risk level may indicate the risk that external transport may exceed the D&D period (e.g., permitted extension period and / or selected extension period). For example, the external transport risk level can be indicated using one or more terms (e.g., low risk, medium risk, and / or high risk). In some examples, the external transport risk level can be indicated numerically.

[0042] Method 100 includes transmitting carrier transportation data (and optional external turnaround time parameters and / or optional external transportation risk level) to the user in S122 to book shipment.

[0043] It can be done through a user interface (such as, Figure 3The user interface shown herein) executes a shipment booking. A shipment booking may involve a booking request and / or a booking response. A booking request can be considered a request for a shipment booking. For example, a user may submit a booking request via a booking platform. In some examples, the user may be a booking agent. In some examples, the user is the consignee. For example, the user is an electronic device (such as an electronic device communicatively connected to the electronic devices disclosed herein, e.g., ...). Figure 2 The user of the electronic device 300. For example, a booking request includes information associated with a booking. In some examples, a booking request may be viewed as a shipment request for one or more items (e.g., goods).

[0044] In one or more example methods, the prediction model is a machine learning prediction model. In one or more example methods, the machine learning prediction model includes one or more of the following: regression prediction model, decision tree model, random forest model, and gradient boosting model.

[0045] Regression prediction models can be viewed as models configured to perform one or more regression techniques (such as linear regression and / or logistic regression). For example, regression techniques can be viewed as supervised machine learning techniques.

[0046] Decision tree models can be viewed as a predictive technique that uses a tree-like structure (such as a branching structure) to represent decisions and outcomes (such as possible outcomes). For example, the tree structure of a decision tree model can be based on historical data associated with one or more previous shipments involving external transportation.

[0047] Random forest models can be viewed as ensemble learning methods for classification, regression, and other tasks, which operate by building an ensemble of decision trees based on historical data. For example, historical data may be influenced by the randomness of the packing and features used to build each individual tree to predict external turnaround time parameters.

[0048] For example, a gradient boosting model can be configured to perform one or more gradient boosting techniques. For instance, a gradient boosting model can be configured to iteratively adjust one or more predictions associated with the prediction model. In some examples, the gradient boosting model is a Lightweight Gradient Boosting Machine (LightGBM). In some examples, the gradient boosting model is Extreme Gradient Boosting (XGBoost).

[0049] In one or more example methods, method 100 includes calculating one or more commodity transportation parameters (S104) based on historical data. In some examples, the historical data is transformed to extract one or more commodity transportation parameters and / or consignee transportation parameters.

[0050] Goods transportation parameters can be considered as parameters indicating the transportation of goods. It is understood that the transportation of various goods has various delivery times. In one or more example methods, one or more goods transportation parameters for at least one good indicate at least one of the following for said at least one good: the proportion of delayed containers for at least one good; the proportion of the permitted extension time used for at least one good relative to the permitted extension time for shipment; and the proportion of the selected extension time used for at least one good relative to the selected extension time for shipment. For example, one or more goods transportation parameters for each of a variety of goods indicate one or more of the following for each good: the proportion of delayed containers; the proportion of the permitted extension time used relative to the permitted extension time for shipment; and the proportion of the selected extension time used relative to the selected extension time for shipment.

[0051] For example, the proportion of delayed containers for a commodity can be viewed as the proportion of delayed containers shipped. For example, the proportion of delayed containers for a commodity can be viewed as the proportion of delayed containers to the total number of containers shipped for a given commodity. For example, the proportion of delayed containers for a commodity is the percentile returned for container delays for each commodity based on historical data (e.g., one year's data) and for various types of shipments (e.g., ad hoc shipments and contract-based shipments).

[0052] Granted extension time can be considered, for example, an extension time granted without user selection. For instance, granted extension time can be considered automatically granted extension time, such as extension time granted by a port authority. For example, the percentage of used granted extension time relative to the total granted extension time for shipment is the percentile of used granted extension time relative to the total granted extension time for each commodity based on historical data (e.g., one year's data) and for various shipments (e.g., contract-based shipments). For example, the percentile of used granted extension time relative to the total granted extension time for shipment for each commodity can be based on the ratio of consumed free shipment time days to granted free shipment time days for each commodity.

[0053] The proportion of selected delay time used for at least one good to selected delay time for shipment can be, for example, regarded as the proportion of purchased (e.g., purchased by the consignee) delay time used for at least one good to purchased (e.g., purchased by the consignee) delay time for shipment. For example, the proportion of selected delay time used to selected delay time for shipment is a percentile for each good based on historical data (e.g., one year's data) and for various shipments (e.g., ad hoc shipments and contract-based shipments) based on the ratio of consumed shipment delay time to purchased shipment delay time.

[0054] In one or more example methods, predicting the S118 external turnaround time parameter includes determining the S118A commodity risk factor based on one or more commodity transportation parameters. For example, a commodity risk factor can be considered as an indication of the risk of incurring charges such as D&D fees for a given commodity. In some examples, determining the S118A commodity risk factor includes determining the commodity risk factor for each port (e.g., for each port) based on one or more commodity transportation parameters. In some examples, determining the S118A commodity risk factor can be considered as deriving and / or calculating the commodity risk factor based on one or more commodity transportation parameters. In one or more example methods, determining the S118A commodity risk factor based on one or more commodity transportation parameters includes applying one or more standardized functions (S118AA) to one or more commodity transportation parameters. In some examples, determining the S118A commodity risk factor based on one or more commodity transportation parameters includes applying one or more symbolic modal functions to one or more commodity transportation parameters. In other words, the one or more standardized functions include, for example, symbolic modal functions.

[0055] In one or more example methods, method 100 includes a grouping S106 of consignees based on whether the consignee is associated with a previous shipment. For example, grouping consignees S106 can be considered as classifying consignees based on whether they are associated with a previous shipment (e.g., new consignees versus existing consignees or users). In one or more example methods, a first consignee group includes consignees associated with one or more previous shipments. For example, the first consignee group can be considered as a consignee group including existing consignees. In one or more example methods, a second consignee group includes consignees not associated with a previous shipment. For example, the second consignee group can be considered as a consignee group including consignees that do not exist (such as new consignees).

[0056] In one or more example methods, method 100 includes calculating one or more consignee transportation parameters S108 based on historical data. In some examples, calculating one or more consignee transportation parameters S108 based on historical data can be viewed as transforming one or more consignee transportation parameters based on historical data. In other words, one or more consignee transportation parameters are extracted and transformed for each consignee using historical data. Consignee transportation parameters can be viewed as parameters indicating the consignee's shipping mode, which helps us understand the consignee's transportation mode.

[0057] In one or more example methods, one or more consignee transportation parameters for at least one consignee indicate at least one of the following for the at least one consignee: the proportion of delayed containers for the at least one consignee; the proportion of permitted extension time used by the at least one consignee to the permitted extension time for shipment; and the proportion of selected extension time used by the at least one consignee to the selected extension time for shipment. For example, one or more consignee transportation parameters for each of a plurality of consignees indicate at least one of the following: the proportion of delayed containers for each consignee; the proportion of permitted extension time used by each consignee to the permitted extension time for shipment; and the proportion of selected extension time used by each consignee to the selected extension time for shipment.

[0058] For example, the proportion of delayed containers for a consignee can be considered as the proportion of delayed containers shipped by the consignee. Alternatively, the proportion of delayed containers for a consignee can be considered as the proportion of delayed containers to the total containers shipped by a given consignee. Or, the proportion of delayed containers for a consignee can be a percentile based on the consignee's proportion of delayed shipments / containers.

[0059] The proportion of the consignee's used permitted extension time to the total permitted extension time of the shipment can be regarded, for example, as the proportion of the consignee's used permitted extension time (e.g., granted by the port authority) to the purchased extension time of the shipment (e.g., purchased by the consignee). For example, the proportion of the consignee's used selected extension time to the total selected extension time of the shipment is based on the percentile of the ratio of the consignee's consumed days to the purchased days.

[0060] The proportion of the consignee's used selected extension time to the total selected extension time of shipment can be considered, for example, as the proportion of the consignee's used purchase (e.g., purchased by the consignee) extension time to the total purchase (e.g., purchased by the consignee) extension time of shipment. For example, the proportion of the consignee's used selected extension time to the total selected extension time of shipment is based on the percentile of the consignee's consumed days to the purchased days.

[0061] In one or more example methods, predicting the S118 external turnaround time parameter includes determining the S118B consignee risk factor based on one or more consignee transportation parameters. In some examples, determining the S118B consignee risk factor includes determining the consignee risk factor for each port (e.g., for each port) based on one or more consignee transportation parameters. In some examples, determining the S118B consignee risk factor may be viewed as deriving and / or calculating a commodity risk factor based on one or more consignee transportation parameters. For example, a consignee risk factor may be viewed as an indication of the risk of incurring charges such as D&D fees for a given consignee. In one or more example methods, determining the S118B consignee risk factor based on one or more consignee transportation parameters includes applying one or more standardized functions (S118BA) to one or more consignee transportation parameters. In some examples, determining the S118B consignee risk factor based on one or more consignee transportation parameters includes applying one or more symbolic modal functions to one or more consignee transportation parameters.

[0062] In one or more example methods, method 100 includes obtaining a transaction volume parameter associated with the consignee in S110. In one or more example methods, the transaction volume parameter indicates the consignee's historical transaction volume involving external transportation. The transaction volume parameter may indicate a number of times, such as the number of historical external transportations associated with a given consignee. In other words, the transaction volume parameter may, for example, indicate the number of times the consignee has used external transportation services, such as historical times.

[0063] In some examples, obtaining the transaction volume parameters associated with the consignee in S110 includes receiving and / or retrieving the transaction volume parameters associated with the consignee. In some examples, obtaining the transaction volume parameters associated with the consignee in S110 includes generating the transaction volume parameters associated with the consignee.

[0064] In one or more example methods, method 100 includes determining whether the transaction volume parameter S112 meets a first criterion. In some examples, consignees may be grouped (e.g., grouped and / or categorized) based on whether the transaction volume parameter associated with the consignee meets the first criterion. In one or more example methods, the first criterion is based on a first threshold. In some examples, whether the transaction volume parameter meets the first criterion depends on whether the transaction volume parameter is greater than or less than the first threshold. For example, when the transaction volume parameter is greater than or equal to the first threshold, the transaction volume parameter may be considered to meet the first criterion. For example, when the transaction volume parameter is less than the first threshold, the transaction volume parameter may be considered not to meet the first criterion. For example, the first threshold is a value. For example, the threshold may be a value of 50. In some examples, when the transaction volume parameter meets the first criterion, the consignee may be considered a large consignee. In some examples, when the transaction volume parameter does not meet the first criterion, the consignee may be considered a small consignee.

[0065] Seasonality can be viewed as a property of time series, where the turnover time parameter undergoes regular and predictable changes (e.g., patterns) that may repeat over a period of time (such as each calendar year). For example, seasonality can be viewed as a seasonal pattern of turnover time associated with a dataset (e.g., a time series).

[0066] Seasonal parameters can be viewed as parameters indicating the seasonality of turnaround time, for example, based on historical data. In some examples, seasonal parameters include numerical values ​​(e.g., Boolean values, decimals, fractions, integers, etc.) indicating characteristics of a time series where turnaround time undergoes periodic and / or predictable variations (e.g., patterns) that may repeat over a period of time (such as each calendar year). For example, seasonal parameters can indicate seasonal patterns associated with a dataset (e.g., a time series). For example, each seasonal parameter can be associated with a corresponding time period (such as a corresponding season (e.g., a month or a quarter of a year)). For example, seasonal parameters can be viewed as numerical values ​​indicating the seasonal effects associated with a given month (e.g., the impact on revenue per volume). Seasonal parameters can be viewed as indicating seasonality within a given month. For example, seasonal parameters are associated with a particular port.

[0067] In one or more example methods, method 100 includes: after determining that the transaction volume parameter meets a first criterion, obtaining a seasonal parameter associated with the consignee in step S114. In one or more example methods, method 100 includes: after determining that the transaction volume parameter meets the first criterion, receiving and / or retrieving the seasonal parameter associated with the consignee. In one or more example methods, method 100 includes: after determining that the transaction volume parameter meets the first criterion, generating the seasonal parameter associated with the consignee.

[0068] For example, for large consignees, seasonal parameters are obtained to assess the turnaround time of specific commodities at the port, as well as any combination of container types / sizes and the month of unloading.

[0069] In one or more example methods, method 100 includes: after determining that the transaction volume parameter does not meet a first criterion, preventing S113 from obtaining the seasonal parameter associated with the consignee. For example, due to a lack of accuracy in the seasonal parameter associated with small consignees, such as due to a lack of data, the seasonal parameter may not be available for small consignees.

[0070] In one or more example methods, predicting the external turnaround time parameter S118 includes generating the turnaround time based on a prediction model, S118C, based on historical data. For example, the turnaround time based on the prediction model is obtained as the output of a prediction model applied to historical data. In other words, the turnaround time based on the prediction model is the output of S118A. For example, the turnaround time based on the prediction model can be considered as the turnaround time provided by the prediction model. In other words, the output of the prediction model may be the turnaround time based on the prediction model. For example, the turnaround time based on the prediction model indicates, for example, the time from when an external transportation provider collects the container from the terminal and / or port until, for example, when the external transportation provider returns the container to the terminal and / or port.

[0071] In one or more example methods, generating S118C turnaround time based on a predictive model based on historical data includes generating a set of aggregated data (S118CA) based on historical delay time data and corresponding historical turnaround time data. In one or more example methods, generating S118C turnaround time based on a predictive model based on historical data includes applying the predictive model (S118C) to the set of aggregated data. For example, the granted delay time data and corresponding actual turnaround time for each port are aggregated into the set, and for each combination of container type and booking type (e.g., ad hoc and / or contract-based shipments), the data is fitted to an ML-based regression model to predict the external turnaround time for shipments transported by external transportation services. For example, the set of aggregated data can be generated for a given port (e.g., for each port). For example, the set of aggregated data can be generated based on historical delay time data and corresponding historical delay time data for a given port. For example, the set of aggregated data can be considered as input to the predictive model. In some examples, the set of aggregated data associated with a given booking type can be provided as input to the predictive model. For example, the set of aggregated data associated with a given size of container (such as 20 feet and / or 40 feet) can be provided as input to the predictive model. In some examples, the set of aggregated data associated with a given booking type, such as ad-hoc bookings (e.g., SPOT bookings) and / or contract bookings, can be provided to the predictive model. For example, historical turnaround time data can be viewed as turnaround times associated with one or more previous shipments involving external transportation.

[0072] In one or more example methods, predicting the S118 external turnaround time parameter includes generating the S118D external turnaround time parameter based on the turnaround time of the prediction model and one or more of the following: commodity risk factors, consignee risk factors, and seasonality parameters.

[0073] In one or more example methods, transmitting carrier transportation data S122 includes causing S122A to display a user interface object representing the carrier transportation data. This is in Figure 3 As shown in the image.

[0074] For example, a user interface object can be viewed as an object of the user interface, such as a component. For instance, a user interface object can be configured to provide information (such as carrier transportation data) to a user of an electronic device for selection and / or interaction with the user. In some examples, a user interface object can be configured to allow a user of the electronic device to interact with the user interface object, such as by selecting and / or clicking on the user interface object.

[0075] For example, the electronic device is configured to cause the display of a user interface object representing carrier transportation data. For example, the electronic device could be a server device that sends data to cause the display of the user interface object. For example, the electronic device could be configured to transmit data via an electronic device (such as...). Figure 2 The display of the electronic device 300 shows user interface objects.

[0076] In one or more example methods, method 100 includes causing S124 to display a user interface object that indicates one or more of the following: external turnaround time parameters and external transportation risk levels.

[0077] Figure 2 A block diagram of an exemplary electronic device 300 according to the present disclosure is shown. The electronic device 300 includes a memory circuitry 301, a processor circuitry 302, and an interface 303. The electronic device 300 is configured to perform... Figures 1A to 1B Any of the methods disclosed herein. In other words, electronic device 300 is configured to generate and / or transmit carrier transportation data. In some examples, electronic device 300 is a server device configured to provide carrier transportation data.

[0078] In some examples, electronic device 300 is a transport data generator and / or provider device.

[0079] Electronic device 300 is configured (e.g., via memory circuitry 301 and / or interface 303) to obtain historical data associated with one or more previous shipments involving external transportation.

[0080] Electronic device 300 is configured to predict (e.g., via processor 302) an external turnaround time parameter by applying a prediction model to historical data (e.g., via processor 302), the external turnaround time parameter indicating the turnaround time of containers disposed of by external transportation.

[0081] Electronic device 300 is configured to generate (e.g., via processor 302) carrier transportation data associated with the carrier transportation service based on predicted external turnaround time parameters.

[0082] Electronic device 300 is configured to transmit carrier transportation data (e.g., via processor 302 and / or interface 303) to a user for booking shipment.

[0083] Processor circuitry 302 is optionally configured to execute Figures 1A to 1BThe operation of electronic device 300 may be embodied in any of the operations disclosed herein (such as any one or more of the following operations: S102, S104, S106, S108, S110, S112, S113, S114, S116, S118, S118A, S118AA, S118BA, S118BB, S118C, S118CA, S118CB, S118D, S120, S120A, S120B, S122, S122A, S124). The operation of electronic device 300 may be embodied in the form of executable logic routines (e.g., lines of code, software programs, etc.) stored on a non-transitory computer-readable medium (e.g., memory circuitry 301) and executed by processor circuitry 302.

[0084] Furthermore, the operation of electronic device 300 can be considered as a method configured to be performed by electronic device 300. Additionally, while the described functions and operations can be implemented in software, such functionality can also be achieved via dedicated hardware or firmware, or some combination of hardware, firmware, and / or software.

[0085] The memory circuit system 301 may be one or more of a buffer, flash memory, hard disk drive, removable media, volatile memory, non-volatile memory, random access memory (RAM), or other suitable means. In a typical arrangement, the memory circuit system 301 may include non-volatile memory for long-term data storage and volatile memory used as system memory for the processor circuit system 302. The memory circuit system 301 may exchange data with the processor circuit system 302 via a data bus. Control lines and an address bus may also exist between the memory circuit system 301 and the processor circuit system 302. Figure 2 (Not shown in the image). The memory circuitry 301 is considered a non-transitory computer-readable medium.

[0086] The memory circuit system 301 can be configured to store historical data, external turnaround time parameters, carrier transportation data, container pickup parameters, container return parameters, carrier transportation value, external transportation risk level, prediction model, one or more commodity transportation parameters, one or more consignee transportation parameters, commodity risk factors, consignee risk factors, first consignee group, second consignee group, transaction volume parameters, seasonality parameters, turnaround time based on prediction model, a set of summary data, historical turnaround time data, and / or user interface objects in a portion of the memory.

[0087] Figure 3 An example user interface according to this disclosure is shown. Figure 3 User interface 50 is shown. Electronic devices (such as...) Figure 3The electronic device 300 can be configured to cause the display of a user interface (UI) 50.

[0088] User interface 50 includes a UI object 48 representing the origin location of the shipment and a UI object 49 representing the destination location. User interface 50 relates to container HM592448 for shipment, which has corresponding user interface objects 54, 56, 60, and 66, representing: container pickup parameters as September 12, 2023, external transport return parameters as September 20, 2023, external transport risk level, and carrier transport value (such as carrier transport value for a given location (e.g., transport destination)).

[0089] For example, the external transport return parameter for September 20, 2023, is considered to have a high risk of incurring D&D costs. This could be because, for example, the time interval between the container pickup parameter (denoted by 54) and the predicted external transport return parameter (denoted by 56, which is based on the predicted external turnaround time parameter) is greater than the number of days of delay. In this example, the difference is 4 days.

[0090] User interface 50 includes a UI object representing a D&D value of 400, such as 400 in a given currency. The D&D value can be viewed as a D&D penalty corresponding to the number of billing days. For example, the D&D penalty can be determined (e.g., set) by the port authority of the destination port (e.g., Ho Chi Minh City).

[0091] User interface 50 is displayed to the user, allowing the user to book the shipment by considering external transportation, the impact of external transportation on turnaround time, and the risk level of external transportation. User interface 50 provides carrier transportation data to the user via UI object 66. Carrier transportation data includes, for example, carrier transportation value, optional carrier transportation destination, and optional container return parameters based on carrier transportation turnaround time parameters.

[0092] Users, such as consignees, can book shipments, such as carrier transportation services, by selecting UI object 64 for choosing carrier transportation services. One or more carrier transportation destinations and / or scheduled carrier transportation locations are provided via user interface object 50. One or more predicted carrier transportation destinations can be predicted, for example, based on historical data associated with one or more previous shipments involving external transportation.

[0093] Users can select a location, such as a location different from the provided location, and generate and / or provide carrier transportation value for that location, such as via user interface object 50.

[0094] The implementation of the methods and products (electronic devices) according to this disclosure is set forth in the following terms: Clause 1. A method performed by an electronic device, the method comprising: Obtain historical data associated with one or more previous shipments involving external transportation; The external turnaround time parameter, which indicates the turnaround time of containers disposed of via external transportation, is predicted by applying a predictive model to the historical data. Based on the predicted external turnaround time parameters, carrier transportation data associated with the carrier's transportation services is generated; and The carrier's transportation data is transmitted to the user for use in booking shipments.

[0095] Clause 2. The method described in Clause 1, wherein the prediction model is a machine learning prediction model.

[0096] Clause 3. The method described in Clause 2, wherein the machine learning prediction model includes one or more of the following: regression prediction model, decision tree model, random forest model, and gradient boosting model.

[0097] Clause 4. The method according to any one of the preceding clauses, wherein the method includes calculating one or more commodity transportation parameters based on the historical data, wherein the one or more commodity transportation parameters of the at least one commodity indicate at least one of the following for the at least one commodity: The proportion of delayed containers for at least one of the aforementioned commodities, The proportion of the permitted extension time used for the at least one commodity to the permitted extension time for shipment; and The proportion of the selected delay time used for the at least one commodity to the selected delay time for shipment.

[0098] Clause 5. The method according to any one of the preceding clauses, wherein predicting the external turnaround time parameter includes determining commodity risk factors based on the one or more commodity transportation parameters.

[0099] Clause 6. The method according to Clause 5, wherein determining the commodity risk factors based on the one or more commodity transportation parameters includes applying one or more standardized functions to the one or more commodity transportation parameters.

[0100] Clause 7. The method according to any one of the preceding clauses, wherein the method includes grouping the consignees based on whether the consignee is associated with a previous shipment, wherein a first group of consignees includes consignees associated with one or more previous shipments, and wherein a second group of consignees includes consignees not associated with previous shipments.

[0101] Clause 8. The method according to any one of the preceding clauses, wherein the method includes calculating one or more consignee transportation parameters based on the historical data, wherein the one or more consignee transportation parameters of the at least one consignee indicate at least one of the following for the at least one consignee: The proportion of delayed containers for at least one consignee The proportion of the approved extension time used by the at least one consignee to the total approved extension time for shipment; and The proportion of the selected extension time used by at least one consignee to the selected extension time for shipment.

[0102] Clause 9. The method according to any one of the preceding clauses, wherein predicting the external turnaround time parameter includes determining consignee risk factors based on the one or more consignee transportation parameters.

[0103] Clause 10. The method according to Clause 9, wherein determining the consignee risk factors based on the one or more consignee transportation parameters includes applying one or more standardized functions to the one or more consignee transportation parameters.

[0104] Clause 11. The method according to any one of the preceding clauses, wherein the method comprises: Obtain transaction volume parameters associated with the consignee, wherein the transaction volume parameters indicate the historical consignee transaction volume involving external transportation; Determine whether the transaction volume parameter meets the first criterion; and After determining that the transaction volume parameter meets the first criterion, seasonal parameters associated with the consignee are obtained.

[0105] Clause 12. The method according to any one of the preceding clauses, wherein applying the predictive model to the historical data includes generating a turnaround time based on the predictive model based on the historical data.

[0106] Clause 13. The method according to Clause 12, wherein generating the turnaround time based on the historical data includes: - Generate a set of summary data based on historical delay time data and corresponding historical turnaround time data; and - Apply the prediction model to the set of aggregated data.

[0107] Clause 14. The method according to any one of the preceding clauses, wherein predicting the external turnaround time parameter comprises generating the external turnaround time parameter based on the turnaround time based on the prediction model and one or more of the following: the commodity risk factor, the consignee risk factor and the seasonality parameter.

[0108] Clause 15. The method according to any one of the preceding clauses, wherein generating the carrier transportation data includes generating the carrier transportation data based on the external turnaround time parameters and container pickup parameters, the carrier transportation data including container return parameters and / or carrier transportation value.

[0109] Clause 16. The method according to any one of the preceding clauses, wherein generating the accrediting party's transportation data includes determining the external transportation risk level based on the difference between external transportation return parameters and container pickup parameters.

[0110] Clause 17. The method according to any one of the preceding clauses, wherein transmitting the carrier's transportation data includes causing a user interface object representing the carrier's transportation data to be displayed.

[0111] Clause 18. The method according to any one of the preceding clauses, wherein the method includes causing a user interface object to be displayed, the user interface object indicating one or more of the following: the external turnaround time parameter and the external transportation risk level.

[0112] Clause 19. The method according to any one of the preceding clauses, wherein the historical data includes one or more of the following: historical transaction data associated with previous transactions, historical container movement data indicating container movement, historical transportation data of external transportation and / or carrier transportation, and historical seasonal factors of external transportation and / or carrier transportation.

[0113] Clause 20. An electronic device comprising a memory circuitry, a processor circuitry, and an interface, wherein the electronic device is configured to perform any of the methods according to any one of Clauses 1 to 19.

[0114] Clause 21. A computer-readable storage medium storing one or more programs, said one or more programs including instructions that, when executed by an electronic device, cause the electronic device to perform any of the methods described in Clauses 1 to 19.

[0115] The use of terms such as "first," "second," "third," and "fourth," "primary," "secondary," and "auxiliary," etc., does not imply any particular order, but is included to identify individual elements. Furthermore, the use of terms such as "first," "second," "third," and "fourth," "primary," "secondary," and "auxiliary," etc., does not indicate any order or importance, but is used to distinguish one element from another. It should be noted that the use of the terms "first," "second," "third," and "fourth," "primary," "secondary," and "auxiliary," etc., here and elsewhere, is solely for labelling purposes and is not intended to indicate any particular spatial or temporal order. Moreover, the labeling of a first element does not imply the existence of a second element, and vice versa.

[0116] It is understood that the accompanying drawings include some circuit systems or operations shown in solid lines and some circuit systems or operations shown in dashed lines. The circuit systems or operations included in the solid lines are those included in the most broad example embodiments. The circuit systems or operations included in the dashed lines are example embodiments that can be included in or part of the circuit systems or operations of the solid-line example embodiments, or are further circuit systems or operations that can be taken in addition to the circuit systems or operations of the solid-line example embodiments. It should be understood that these operations do not need to be performed in the order presented. Furthermore, it should be understood that not all operations need to be performed. Exemplary operations can be performed in any order and in any combination.

[0117] It should be noted that the word "including" does not necessarily exclude the existence of other elements or steps besides those listed.

[0118] It should be noted that the words "one" or "a kind" preceding an element do not preclude the existence of multiple such elements.

[0119] It is important to note that the term "indication" can be considered as "associated with," "related to," "describe," "represent," and / or "define." The terms "indication," "associated with," "related to," "describe," "represent," and "define" are used interchangeably. The term "indication" can be considered as indicating a relation. For example, weight data indicating weight may include one or more weight parameters.

[0120] It is important to note that the term "based on" can be considered as "as a function of" and / or "derived from". The terms "based on" and "as a function of" are used interchangeably. For example, parameters determined "based on" a dataset can be considered as parameters determined "as a function of the dataset". In other words, parameters can be the output of one or more functions that take the dataset as input.

[0121] Functions can represent the relationship between inputs and outputs, such as mathematical relationships, database relationships, hardware relationships, logical relationships, and / or other suitable relationships.

[0122] It should be further noted that any reference numerals in the drawings do not limit the scope of the claims, exemplary embodiments may be implemented at least in part by means of both hardware and software, and certain “components,” “units,” or “devices” may be represented by the same hardware article.

[0123] The various exemplary methods, apparatuses, nodes, and systems described herein are set within the general context of method steps or processes. In one aspect, these method steps or processes may be implemented by a computer program product embodied in a computer-readable medium, including computer-executable instructions, such as program code, that are executed by a computer in a networked environment. Computer-readable media may include removable and non-removable storage devices, including but not limited to read-only memory (ROM), random access memory (RAM), optical disc (CD), digital versatile disc (DVD), etc. Generally, a program circuit system may include routines, programs, objects, components, data structures, etc., that perform a specified task or implement a particular abstract data type. The computer-executable instructions, associated data structures, and program circuit systems represent examples of program code for performing steps of the methods disclosed herein. Specific sequences of such executable instructions or associated data structures represent examples of corresponding actions for implementing the functions described in such steps or processes.

[0124] Although features have been shown and described, it should be understood that they are not intended to limit the scope of the claimed disclosure, and it will be apparent to those skilled in the art that various changes and modifications can be made without departing from the scope of the claimed disclosure. Therefore, this specification and accompanying drawings should be considered illustrative rather than restrictive. The claimed disclosure is intended to cover all alternatives, modifications, and equivalents.

Claims

1. A method performed by an electronic device, the method comprising: Obtain historical data associated with one or more previous shipments involving external transportation; The external turnaround time parameter, which indicates the turnaround time of containers disposed of via external transportation, is predicted by applying a predictive model to the historical data. Based on the predicted external turnaround time parameters, carrier transportation data associated with the carrier's transportation services is generated. as well as The carrier's transportation data is transmitted to the user for use in booking shipments.

2. The method according to claim 1, wherein the prediction model is a machine learning prediction model.

3. The method according to claim 2, wherein the machine learning prediction model includes one or more of the following: regression prediction model, decision tree model, random forest model, and gradient boosting model.

4. The method according to any one of the preceding claims, wherein the method includes calculating one or more commodity transportation parameters based on the historical data, wherein the one or more commodity transportation parameters of the at least one commodity indicate at least one of the following for the at least one commodity: The proportion of delayed containers for at least one of the aforementioned commodities, The proportion of the permitted extension time used for the at least one commodity to the permitted extension time for shipment; and The proportion of the selected delay time used for the at least one commodity to the selected delay time for shipment.

5. The method according to any one of the preceding claims, wherein predicting the external turnaround time parameter includes determining commodity risk factors based on the one or more commodity transportation parameters.

6. The method of claim 5, wherein determining the commodity risk factors based on the one or more commodity transportation parameters comprises applying one or more standardized functions to the one or more commodity transportation parameters.

7. The method according to any one of the preceding claims, wherein the method comprises grouping the consignees based on whether the consignees are associated with previous shipments, wherein a first group of consignees includes consignees associated with one or more previous shipments, and wherein a second group of consignees includes consignees not associated with previous shipments.

8. The method according to any one of the preceding claims, wherein the method comprises calculating one or more consignee transportation parameters based on the historical data, wherein the one or more consignee transportation parameters of the at least one consignee indicate at least one of the following for the at least one consignee: The proportion of delayed containers for at least one consignee The proportion of the approved extension time used by the at least one consignee to the total approved extension time for shipment; and The proportion of the selected extension time used by at least one consignee to the selected extension time for shipment.

9. The method according to any one of the preceding claims, wherein predicting the external turnaround time parameter includes determining consignee risk factors based on the one or more consignee transportation parameters.

10. The method of claim 9, wherein determining the consignee risk factors based on the one or more consignee transportation parameters comprises applying one or more standardized functions to the one or more consignee transportation parameters.

11. The method according to any one of the preceding claims, wherein the method comprises: Obtain transaction volume parameters associated with the consignee, wherein the transaction volume parameters indicate the historical consignee transaction volume involving external transportation; Determine whether the transaction volume parameter meets the first criterion; as well as After determining that the transaction volume parameter meets the first criterion, seasonal parameters associated with the consignee are obtained.

12. The method according to any one of the preceding claims, wherein predicting the external turnaround time parameter includes generating a turnaround time based on a prediction model based on historical data.

13. The method of claim 12, wherein generating the turnaround time based on the historical data comprises: - Generate a set of summary data based on historical delay time data and corresponding historical turnaround time data; as well as - Apply the prediction model to the set of aggregated data.

14. The method according to any one of the preceding claims, wherein predicting the external turnaround time parameter comprises generating the external turnaround time parameter based on the turnaround time based on the prediction model and one or more of the following: the commodity risk factor, the consignee risk factor and the seasonality parameter.

15. The method according to any one of the preceding claims, wherein generating the carrier transportation data comprises generating the carrier transportation data based on the external turnaround time parameters and container pickup parameters, the carrier transportation data including container return parameters and / or carrier transportation value.

Images