Method for providing emergency parameters and related electronic device

Abstract

A method for providing emergency parameters, executed by an electronic device, is disclosed. The method includes acquiring shipping data associated with shipping. The method includes determining one or more freight parameters based on the shipping data. The one or more freight parameters vary over time. The method includes determining emergency parameters indicating an emergency event associated with shipping based on the one or more freight parameters and the shipping data. The method includes providing the emergency parameters.

Description

[0001] This disclosure relates to the field of transportation and freight. This disclosure relates to a method and related electronic device for providing emergency parameters. Background Technology

[0002] When shipping is booked, shipping users, such as consignees, may be unable to comply with the booking agreement for shipments such as container shipping. For example, the consignee may cancel a container shipment late. Alternatively, the items or goods awaiting shipment in the container, or a full container, may not arrive at the time agreed in the booking, or in other words, the booking may be considered a "no-show." This can lead to a waste of resources such as shipping space and / or equipment. In some examples, these scenarios may result in shipping delays and / or additional charges for the shipper and / or consignee. Booking cancellations and no-shows can therefore lead to inefficient management of resources such as containers, equipment, vessels, and transportation. Summary of the Invention

[0003] Booking cancellations and no-shows can be considered contingency events. There is a need for an electronic device and method that could improve control over shipping-related contingency events. The costs of cancellations and / or no-shows can be influenced by many factors, such as freight rates, seasonality, port location, booking user data, etc.

[0004] Therefore, there is a need for an electronic device and method for providing emergency parameters that mitigates, alleviates, or resolves existing shortcomings and allows for the provision of more accurate, robust, and reliable predicted emergency parameters—such as parameters for cancellation and / or failure to meet deadlines.

[0005] A method for providing emergency parameters, executed by an electronic device, is disclosed. The method includes acquiring shipping data associated with shipping. The method includes determining one or more freight parameters based on the shipping data. The one or more freight parameters vary over time. The method includes determining emergency parameters indicating an emergency event associated with shipping based on the one or more freight parameters and the shipping data. The method includes providing the emergency parameters.

[0006] An electronic device is disclosed, comprising a memory circuit system, a processor circuit system, and an interface circuit, 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 one or more programs including instructions that, when executed by an electronic device, cause the electronic device to perform any of the methods disclosed herein.

[0008] The advantage of this disclosure is that the disclosed electronic device and method enable the determination of emergency parameters based on dynamic elements. In other words, this disclosure enables the dynamic determination of emergency parameters.

[0009] Advantageously, this disclosure enables the provision of contingency services to consignees, such as those for cancellations and / or no-shows, including dynamically adjusted contingency parameters for the consignee, such as pre-emptive cancellation and / or no-show fees adjusted for a given consignee. The dynamic nature of these contingency parameters allows for improved accuracy in their values, thereby enabling increased and / or optimized contingency service booking options for the consignee.

[0010] Furthermore, this disclosure enables the contingency parameters to be dynamically updated, such as periodically, to reflect changes in booking behavior and / or patterns made by the consignee (such as changes in shipping freight rates). Therefore, these dynamic updates allow for maintaining the increased and / or optimized likelihood of the consignee choosing to book contingency services within a given time period, regardless of changes in the consignee's booking behavior and / or patterns. In other words, this disclosure enables the provision of personalized and dynamic contingency values ​​to users booking shipping.

[0011] For example, this disclosure enables contingency parameters to be based on data specifically associated with the consignee, thereby improving the accuracy of the contingency parameters in terms of the shipping mode of the user (e.g., consignee or shipper), wherein the shipping mode of the consignee can be estimated based on the consignee's contingency service selection rate.

[0012] Advantageously, this disclosure can improve the efficiency of determining accurate emergency parameters, such as time efficiency. Attached Figure Description

[0013] The above and other features and advantages of this disclosure will be readily 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 A flowchart illustrating an exemplary method for improving emergency parameters performed by an electronic device according to the present disclosure is shown, and Figure 2 This is a block diagram illustrating an exemplary electronic device according to the present disclosure. Detailed Implementation

[0014] 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 shown or explicitly described.

[0015] 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.

[0016] Users, such as consignees, can book shipping, such as container shipping, with shippers. Shipping can be viewed as transporting, for example, one or more items located in a container, from a first location to a second location, where the first location can be considered the origin and the second location can be considered the destination. Shipping from the origin to the destination can be considered an origin-destination pair. For example, an origin-destination pair can be considered a transport corridor, such as a route characterized by an origin and a destination. Shipping can be carried out using one or more modes of transport, such as one or more types of vehicles. For example, shipping can be carried out using airplanes, ocean liners, and / or land vehicles.

[0017] An emergency can be considered a possibility, such as an unforeseen harmful event and / or situation associated with shipping. In other words, an emergency can be considered a scenario such as the consignee's user failing to fulfill one or more obligations stipulated in the booking agreement. Such an emergency is, for example, the cancellation and / or default of a shipment.

[0018] For example, shipping cancellations can be executed by a user, such as the user who booked the shipping. For instance, a user, such as the consignee, can cancel their previously booked shipping.

[0019] A breach of contract can be considered a situation where the object to be shipped, such as a container, does not appear at the origin location specified in the booking. For example, a container that has been booked for shipment is not loaded onto the shipping vessel at the time agreed upon in the booking.

[0020] Contingency parameters can be viewed, for example, as parameters associated with contingency events. For instance, such parameters could be associated with shipping cancellations and / or no-shows. These parameters could include, for example, values ​​and / or probabilities associated with the contingency event. For example, they could include cancellation and / or no-show values ​​associated with shipping—such as shipping bookings—which could be provided at the time of booking—e.g., before the shipping. Contingency parameters could also be viewed as safeguard values, such as safeguard costs, which safeguard against the opportunity costs that might result from cancellations and / or no-shows.

[0021] The emergency parameter can be determined, for example, based on one or more dynamic parameters and / or one or more dynamic elements, such as one or more freight parameters. For example, the emergency parameter includes dynamically determined values ​​associated with the emergency event, such as costs, which can be provided to the user in advance during the booking period.

[0022] Figures 1A to 1B A flowchart of an exemplary method 100 performed by an electronic device is shown, for example, for providing emergency parameters according to this disclosure.

[0023] A method 100 is disclosed. Method 100 is performed by an electronic device. Method 100 includes obtaining shipping data associated with shipping in step S102. Obtaining shipping data in step S102 includes, for example, receiving and / or retrieving shipping data associated with shipping. Obtaining shipping data in step S102 also includes, for example, generating shipping data associated with shipping. Shipping data can be considered as data associated with shipping. In one or more example methods, shipping data includes one or more of the following: booking data associated with a booking of shipping, booking user data associated with a user booking shipping, port data associated with a port of shipping, and commodity data associated with goods shipped.

[0024] Booking data can be viewed as data associated with shipping bookings. In other words, booking data can include information associated with shipping bookings. Booking data may include, for example, a user code identifying the user. The user code may include, for example, one or more letters and / or numbers (such as alphanumeric codes). In some examples, booking data may include one or more of the following: container type, container size, and freight rates associated with the origin and destination.

[0025] Booking user data can be viewed as user data associated with shipping bookings. For example, booking user data may include information associated with a user corresponding to a given booking. For instance, booking user data may include one or more of the following: user identifier, Booking data can indicate, for example, whether a user is associated with one or more previous shipments. For instance, booking data could indicate that a user is an existing user, such as someone who has previously booked one or more shipments. In some examples, booking data could indicate that a user is a new user, such as someone not associated with any previous shipments. For example, booking data could indicate that the current shipment booking is the user's first shipment booking.

[0026] Port data can be viewed as data associated with a port. For example, port data may include information indicating the location of the port of origin and / or the port of destination for a shipping vessel. Port data may include, for example, coordinates, such as coordinates indicating the location of the port. Port data may include, for example, port codes indicating the port. Port codes may include, for example, one or more letters and / or numbers, such as alphanumeric codes. Port data may include, for example, one or more of the following: a port code indicating the port of origin and a port code indicating the port of destination.

[0027] Commodity data can be viewed as data associated with the goods being shipped. For example, commodity data may indicate the type and / or class of the items being shipped. Commodity data may include, for example, commodity codes indicating the commodity and / or commodity type. Commodity codes may include, for example, one or more letters and / or numbers (such as alphanumeric codes). Commodity codes may be, for example, Harmonized System (HS) codes.

[0028] The method includes determining one or more freight parameters (S106) based on shipping data. These one or more freight parameters vary over time. These one or more freight parameters can be viewed as one or more parameters characterizing, for example, the usage patterns of freight for a given user, port, and / or commodity. For example, these one or more freight parameters can indicate contingency patterns, such as cancellation and / or no-show patterns associated with ports, users, and / or commodities. In some examples, these one or more freight parameters can characterize the impact of supply and / or demand associated with shipping. In some examples, these one or more freight parameters can characterize freight rates, such as ocean freight rates. In some examples, these one or more freight parameters include freight rates, contingency patterns, and / or supply and demand influencing factors.

[0029] In some examples, the one or more freight parameters can be viewed as one or more dynamic parameters and / or one or more dynamic elements.

[0030] The method 100 includes determining emergency parameters, S108 indicating an emergency event associated with shipping, based on one or more freight parameters and the shipping data. In some examples, emergency parameters are determined by grouping the shipping data—such as by transformation—based on one or more of the following: user code, port code, and commodity code.

[0031] In one or more example methods, determining the S108 contingency parameter based on one or more freight parameters and shipping data includes determining the S108A contingency probability based on the one or more freight parameters and the shipping data. This contingency probability can be considered as the probability of a contingency event occurring in the shipping process. For example, the contingency probability indicates the likelihood of cancellation and / or no-shows in the shipping process. The contingency probability is, for example, a value such as 0 or 1. In some examples, the contingency probability, expressed as a percentage, can be a value between 0 and 100.

[0032] In one or more example methods, determining the S108 emergency parameter includes determining an emergency value associated with the emergency parameter in S108B based on shipping data and one or more freight parameters. This emergency value can, for example, be considered a value associated with an emergency event. For instance, the emergency value can be considered a benchmark rate for the emergency parameter. In other words, the emergency value can, for instance, be considered a benchmark value for cancellations and / or defaults, such as cancellation and / or default fees. For example, the emergency value can include a cancellation and / or default security value. In one or more examples, the emergency parameter includes an emergency probability and an emergency value.

[0033] In one or more example methods, method 100 includes determining the S110 emergency parameters based on the emergency probability and the emergency value. In some examples, method 100 includes determining emergency parameters for one or more shipments, such as emergency parameters for each shipment, based on the emergency probability and the emergency value. The emergency probability can be considered as an emergency probability parameter.

[0034] Method 100 includes providing S112 emergency parameters. In some examples, providing S112 emergency parameters may include, for example, providing emergency parameters to things like... Figure 2 The user of the electronic device 300 displays a user interface object representing the emergency parameter. In some examples, providing the emergency parameter S112 may include displaying a user interface object representing the emergency parameter on a display coupled to the electronic device disclosed herein.

[0035] In some examples, providing emergency parameters may include, for example, sending the emergency parameters in response to a booking request.

[0036] In other words, this disclosure enables personalized and dynamic contingency values ​​for booking contingency services based on changes in shipping supply and demand.

[0037] In one or more example methods, determining the S108A contingency probability includes applying a predictive model to shipping data to provide one or more of the following: a first contingency probability associated with a user, a second contingency probability associated with a port, and a third contingency probability associated with a commodity.

[0038] In some examples, the probability of contingency is determined based on one or more of the following: booking data associated with shipping bookings, booking user data associated with users booking shipping, port data associated with the port of shipping, and commodity data associated with the goods being shipped. In other words, in some examples, the predictive model is applied to one or more of the following: booking data associated with shipping bookings, booking user data associated with users booking shipping, port data associated with the port of shipping, and commodity data associated with the goods being shipped.

[0039] In some examples, the predictive model can be configured to determine the probability of shipping cancellations and / or no-shows based on shipping data. In some examples, determining the S108A contingency probability can be viewed as analyzing shipping data, such as performing an analysis of the shipping data by applying the predictive model to that data.

[0040] Predictive models include, for example, machine learning models used to predict the probability of an emergency. A predictive model can be considered, for example, a machine learning predictive model. In one or more example methods, the predictive model includes a machine learning regression model, and / or a long short-term memory model, and / or a time series prediction model.

[0041] A machine learning regression model can be viewed as a machine learning model configured to perform one or more regression techniques. A machine learning regression model can, for example, include a logistic regression model. For instance, a machine learning regression model can be configured to perform logistic regression based on shipping data.

[0042] Long Short-Term Memory (LSTM) models can be viewed as recurrent neural networks capable of learning sequential dependencies in sequence prediction, and can include one or more neural networks and numerous memory blocks or units that can form chains. LSTM models can be executed using data sequences of varying input lengths and have one or more functions, such as classification and / or regression for predicting values ​​such as emergency values.

[0043] In one or more example methods, the time series forecasting model includes a machine learning-based time series forecasting model. In one or more example methods, the time series forecasting model can be configured to perform one or more forecasting techniques based on shipping data. In one or more examples, the forecasting techniques include machine learning-based time series forecasting techniques.

[0044] For example, applying time series forecasting techniques to shipping data includes applying an Autoregressive Integrated Moving Average (ARIMA) model to the shipping data. In some examples, applying time series forecasting techniques to shipping data includes applying an Autoregressive Integrated Moving Average (ARIMA) model with explanatory variables to the shipping data. In some examples, time series forecasting techniques include applying a Seasonal Autoregressive Integrated Moving Average (SARIMAX) model with exogenous factors. In one or more example methods, the time series forecasting model may be based on a recurrent neural network model.

[0045] The first contingency probability can be viewed, for example, as the probability of a contingency event occurring in a given user's shipment. For instance, the first contingency probability indicates the likelihood of a given user canceling and / or defaulting on their booking.

[0046] The second contingency probability can be viewed, for example, as the probability of an emergency event occurring in shipping at a given port. For instance, the second contingency probability indicates the likelihood of cancellation and / or no-shows at a given port.

[0047] The third contingency probability can be viewed, for example, as the probability of an emergency event occurring during the shipment of a given commodity. For instance, the third contingency probability indicates the likelihood of cancellation and / or non-delivery of a given commodity.

[0048] In one or more example methods, determining the emergency probability S108A includes combining the first emergency probability, the second emergency probability, and the third emergency probability S108AB into an emergency probability.

[0049] In some examples, determining the S108A emergency probability involves using a linear combination to combine the first, second, and third emergency probabilities into a single emergency probability. In other examples, determining the S108A emergency probability involves averaging the first, second, and third emergency probabilities to obtain the emergency probability.

[0050] In some examples, determining the S108A emergency probability involves using a weighted average to combine the first, second, and third emergency probabilities into a single emergency probability. For example, the first, second, and / or third emergency probabilities may be associated with one or more weights. Weights are, for example, values.

[0051] In one or more example methods, method 100 includes obtaining S104 historical shipping data. In one or more example methods, historical shipping data includes one or more of the following: historical freight rates, historical equipment data, historical booking user data for multiple users for emergency events, historical port data for multiple ports for emergency events, and historical commodity data for multiple commodities for emergency events. In some examples, historical shipping data may be considered as historical transaction data.

[0052] In one or more example methods, obtaining S104 historical shipping data includes receiving and / or retrieving historical shipping data. In one or more example methods, obtaining S104 historical shipping data includes generating historical shipping data. Historical shipping data can, for example, be considered as historical data associated with shipping. Historical shipping data can, for example, be associated with one or more users, such as one or more users who have booked at least one shipping trip.

[0053] Historical freight rates can be, for example, historical ocean freight rates, historical land freight rates, or historical air freight rates corresponding to the mode of transport used to transport goods. Each of these historical freight rates corresponds, for example, to an origin-destination pair. For instance, a historical freight rate may correspond to a given shipping route. In other words, a historical freight rate can indicate the shipping cost corresponding to an origin-destination pair—such as a given route. Historical freight rates may change dynamically over time. For example, historical freight rates indicate previous changes in freight rates. For instance, historical freight rates can vary based on variables such as supply and demand, congestion on a given shipping route, environmental conditions, fuel costs, seasonal variations in demand, etc.

[0054] Historical equipment data, for example, indicates equipment associated with one or more previous shipments. For instance, historical equipment data may indicate the type and / or size of containers used in one or more previous shipments.

[0055] Historical booking user data can indicate, for example, multiple unforeseen events associated with a user. For instance, historical booking user data could indicate the number of shipping cancellations and / or no-shows associated with a user.

[0056] Historical port data, for example, indicates the number of emergency events associated with the port. For instance, historical port data can indicate the number of shipping cancellations and / or no-shows associated with the port.

[0057] Historical merchandise data can indicate, for example, the number of incidents associated with a product. For instance, historical merchandise data could indicate the number of shipping cancellations and / or no-shows associated with a product.

[0058] In some examples, obtaining historical data S104 includes grouping one or more elements of the historical shipping data based on one or more of the following: users, ports, and goods.

[0059] In one or more example methods, determining one or more freight parameters S106 based on shipping data includes generating a first ratio S106A based on historical booking user data. This first ratio indicates the proportion of contingency events for each shipment of each of a plurality of users. In some examples, one or more freight parameters include a first ratio indicating the proportion of contingency events for each of a plurality of users. In some examples, determining one or more freight parameters S106 based on shipping data includes generating a first ratio based on historical booking user data. This first ratio indicates the proportion of contingency events for each shipment of at least one of a plurality of users—such as each user. For example, the first ratio may be considered as the ratio between the total number of cancellations and / or no-shows associated with the same user and the total number of cancellations and / or no-shows associated with the user and the total number of shipments associated with the same user. In other words, the first ratio may be considered as a first proportion generated by dividing the total number of contingency events—such as cancellations and / or no-shows associated with a customer—by the total number of bookings made by the same customer.

[0060] In one or more example methods, determining one or more freight parameters S106 based on shipping data includes generating a second ratio based on historical port data, S106B indicating the proportion of contingency events for each shipment at each of multiple ports. For example, one or more freight parameters include a second ratio. For example, this second ratio can be considered as the ratio between the total number of cancellations and / or no-shows associated with a port and the total number of shipments associated with that same port. This second ratio can be calculated for each port. In other words, the second ratio can be considered as a second ratio generated by dividing the total number of contingency events—such as cancellations and / or no-shows associated with a port—by the total number of bookings associated with that same port.

[0061] In one or more example methods, determining one or more freight parameters S106 based on shipping data includes generating a third ratio S106C based on historical commodity data. This third ratio indicates the proportion of contingency events for each shipment of each of a plurality of commodities. For example, one or more freight parameters include a first ratio, a second ratio, and / or a third ratio. For example, the third ratio can be considered as the ratio between the total number of cancellations and / or no-shows associated with a commodity and the total number of shipments associated with the same commodity. In other words, the third ratio can be considered as a third proportion generated by dividing the total number of contingency events—such as cancellations and / or no-shows associated with a commodity—by the total number of bookings associated with the same commodity.

[0062] For example, a first ratio, a second ratio, and / or a third ratio can be generated for one or more existing users.

[0063] In one or more example methods, determining one or more freight parameters S106 based on shipping data includes obtaining the current freight rates associated with the shipping (S106D). For example, one or more freight parameters include a first rate, a second rate, and / or a third rate and / or the current freight rate. In some examples, determining one or more freight parameters S106 based on shipping data includes receiving and / or retrieving the current freight rates associated with the shipping. In some examples, determining one or more freight parameters S106 based on shipping data includes generating the current freight rates associated with the shipping. For example, obtaining the current freight rates associated with the shipping (S106D) includes obtaining the current freight rates associated with the origin-destination pair corresponding to the shipping.

[0064] In some examples, the current freight rate is the freight rate at the time contingency parameters are determined—such as at the time of booking. The current freight rate can be viewed as the freight rate for the origin-destination pair over a given time period, such as one hour, one day, or one week, for the planned shipping time, such as the planned shipping date. In some examples, the current freight rate can be the freight rate for a given time period during the booking of the shipment.

[0065] In other words, the method may include obtaining freight rates, such as capturing origin-destination pairs, when calculating initial contingency parameters, such as when executing a booking.

[0066] In one or more example methods, determining one or more freight rate parameters S106 based on shipping data includes determining a fourth ratio S106E based on historical shipping data, which indicates the proportion of users who have experienced an emergency.

[0067] This fourth ratio can be viewed as the ratio of users who have experienced an emergency to the total number of existing users. In other words, determining one or more freight rate parameters in S106 based on shipping data includes, for example, extracting from historical shipping data the proportion of users who experienced an emergency and subsequently paid for it. This fourth ratio can be extracted from historical shipping data, as shown in Table 1.

[0068] In one or more example methods, determining one or more freight parameters S106 based on shipping data includes determining a fifth ratio S106F based on a fourth ratio, which indicates the proportion of users who may choose contingency services when booking shipping.

[0069] This emergency service can be viewed as a service for selecting emergency parameters—such as prepayment—such as cancellation fees and / or default fees.

[0070] The fifth ratio can be viewed as the ratio between the number of users who might choose contingency services when booking a shipment and the total number of users. In some examples, the fifth ratio in S106F is determined based on the fourth ratio, which includes reducing—such as halving—the fourth ratio. For example, suppose 50% of users who have experienced an emergency in the past might select an emergency service when booking a shipment—such as in advance.

[0071] In one or more example methods, determining the S108B contingency value based on shipping data includes determining the S108BA seasonality factor based on one or more freight parameters and historical shipping data, and applying the seasonality factor to the S108BB contingency value.

[0072] For example, one or more time series forecasting and / or time series analysis techniques can be used to identify seasonal factors. For example, one or more of the following methods can be used to identify seasonal factors: Autoregressive Integrated Moving Average (ARIMA), Autoregressive Integrated Moving Average with Explanatory Variables (ARIMAX), and Seasonal Autoregressive Integrated Moving Average (SARIMAX).

[0073] This seasonality factor can be considered as an indicator of seasonality. In some examples, this seasonality factor can be considered as a seasonal parameter. Seasonality can be viewed as a characteristic that shows a pattern of change over time—such as a periodic pattern that changes over time. In some examples, seasonality factors include numerical values ​​(e.g., Boolean values, decimals, fractions, integers, etc.) that indicate characteristics of, for example, time series, where data such as historical shipping data undergoes periodic and / or predictable changes (e.g., patterns) that may repeat over a period of time (such as each calendar year).

[0074] For example, seasonal factors can indicate seasonal patterns associated with a dataset (e.g., a time series). Each seasonal factor can be associated with a corresponding time period (e.g., a day, a week, a month, or a season, such as a quarter of a year). A seasonal factor can be viewed, for example, as a value indicating the seasonal impact associated with a given time period (e.g., on emergency values).

[0075] In one or more example methods, determining the S108B contingency value based on shipping data includes determining the contingency value by simulating the initial S108BC contingency value. This initial contingency value can, for example, be considered as an initial base rate associated with shipping, such as an initial base value.

[0076] In some examples, simulating 108BC initial emergency values ​​includes determining the corresponding user selection proportions for at least one (e.g., each) initial emergency value. For example, determining the corresponding user selection proportions may include generating a uniform distribution that indicates multiple predicted user selection proportions for a given initial emergency value.

[0077] For example, let's assume that 8% of users experienced cancellations and 4% experienced no-shows at the port. For example, by assuming that 50% of users would choose emergency services, the proportions of users who might choose to cancel services and those who might choose no-show services become 4% and 2%, respectively. For example, this uniform distribution could be between 15% and 35%. For example, the initial emergency value (e.g., the base rate) could range from $10 to $25.

[0078] Table 1 illustrates historical shipping data, including historical emergency values.

[0079] Table 1:

[0080] For example, from Table 1, shipping-level transaction data, freight rates, and corresponding paid cancellation and default fees can be extracted and grouped based on origin and destination.

[0081] For example, for each initial contingency value (e.g., a base rate) within a defined range (e.g., $15 to $25), a corresponding value for users likely to choose the product can be obtained from a uniform distribution, and the obtained initial contingency value (e.g., the base rate) can be applied to them. For example, cancellation fees and no-show fees are applied to the proportion of users likely to choose to cancel the service (e.g., 4%) and the proportion of users likely to choose to no-show the service (e.g., 2%), respectively, and the total value for the entire user pool is ultimately calculated.

[0082] In one or more example methods, determining the S108B contingency value based on shipping data includes selecting an initial S108BD contingency value from the initial contingency values ​​simulated, which achieves an acceptable user selection ratio and a maximum total contingency value across simulations.

[0083] This acceptable user selection ratio, for example, indicates the target proportion of users selecting emergency services, such as the proportion of users selecting emergency services that meets criteria (such as exceeding a threshold, such as 50%). This maximum total emergency value can be considered as the highest initial emergency value.

[0084] The selected initial emergency value can be, for example, considered as the maximum initial emergency value to achieve an acceptable proportion of user selection. The selected initial emergency value can be selected, for example, across one or more simulations. For example, the selected initial emergency value can be selected between 200 and 5000 simulations, such as simulations performed for one or more initial emergency values ​​(e.g., each initial emergency value) out of 1000 simulations.

[0085] In some examples, the selected initial emergency value can be an emergency value with an acceptable proportion of user selections and / or the maximum total emergency value. For example, selecting the maximum aggregate value that provides a satisfactory adoption rate or selection rate.

[0086] In one or more example methods, determining the S108B contingency value based on shipping data includes determining the S108BE contingency value based on the current freight rates and the selected initial contingency value.

[0087] In some examples, determining the S108B contingency value based on shipping data includes determining the difference, such as changes, between the freight rates used to calculate the selected initial contingency value and the current freight rates.

[0088] In some examples, determining the S108BE emergency value based on the current freight rate and the selected initial emergency value involves using the difference between the freight rate used to calculate the selected initial emergency value and the current freight rate to determine—such as—the corresponding change in the selected emergency value. In some examples, determining the S108BE emergency value based on the current freight rate and the selected initial emergency value may include updating—such as adjusting—the selected initial emergency value based on the difference between the current freight rate and the freight rate at the time of simulation.

[0089] The update applied to the initial emergency value (such as for providing an updated initial emergency value) can be linearly proportional to the difference between the current freight rate and the freight rate at the time of simulation. In other words, determining the S108BE emergency value may include using the change between the freight rate since the calculation of the selected initial emergency value and the current freight rate to determine the corresponding change of the selected initial emergency value.

[0090] In one or more example methods, simulating the initial emergency value of S108BC includes obtaining a first range of S108BC_1 user selection ratios. In one or more example methods, the user selection ratio indicates the target proportion of users selecting emergency services.

[0091] In one or more example methods, simulating the initial emergency value of S108BC includes receiving and / or retrieving a first range of S108BC_1 user selection ratios. In one or more example methods, the user selection ratio indicates a target proportion of users selecting emergency services.

[0092] The first range is, for example, a predetermined range. For example, the first range may include a range of 20%-80%. For example, the first range may include a range of 10%-50%. For example, the first range may include a range of 15%-35%.

[0093] This first range can be considered, for example, a first criterion. This first range indicates, for example, an acceptable proportion of user selection. This first range includes, for example, one or more first thresholds. For instance, this first range includes a first lower threshold and / or a first upper threshold. In some examples, when the simulated initial emergency value achieves a proportion of user selection within the first range, it can be considered that the first criterion is met.

[0094] For example, when the proportion of user selections achieved by the simulated initial emergency value is greater than or equal to a first lower threshold, the simulated adoption rate can be considered to meet the first criterion. In some examples, when the proportion of user selections achieved by the simulated initial emergency value is lower than the first lower threshold, the simulated adoption rate can be considered to not meet the first criterion.

[0095] In some examples, a simulated adoption rate that fails to meet the first criterion is considered to have a user selection rate greater than or equal to a first upper limit threshold, achieved by the simulated initial emergency value. In other examples, a simulated adoption rate that meets the first criterion is considered to have a user selection rate lower than the first upper limit threshold, achieved by the simulated initial emergency value.

[0096] In one or more example methods, simulating the initial emergency value of S108BC includes obtaining a predetermined second range of candidate emergency values ​​used by S108BC_2 for simulation. In another example method, simulating the initial emergency value of S108BC includes receiving and / or retrieving a predetermined second range of candidate emergency values ​​used by S108BC_2 for simulation. In other words, the predetermined second range can be considered as a range indicating the range of the candidate simulated benchmark rate—such as the simulated benchmark rate value. The predetermined second range may include a range of 20%-80%. For example, the predetermined second range may include a range of 10%-50%. For example, the predetermined second range may include a range of 15%-25%.

[0097] The predetermined second range can be considered, for example, as a second criterion. The predetermined second range, for example, indicates an acceptable candidate emergency value. The predetermined second range, for example, includes one or more second thresholds. For example, the predetermined second range includes a second lower threshold and / or a second upper threshold. In some examples, when the simulated candidate emergency value is within this predetermined second range, it can be considered that the second criterion is met.

[0098] For example, when the simulated candidate emergency value is greater than or equal to the second lower limit threshold, it can be considered that the simulated candidate emergency value meets the second criterion. In some examples, when the simulated candidate emergency value is lower than the second lower limit threshold, it can be considered that the simulated candidate emergency value does not meet the second criterion.

[0099] In some examples, a simulated candidate emergency value greater than or equal to the second upper limit threshold can be considered as not meeting the second criterion. In other examples, a simulated candidate emergency value lower than the second upper limit threshold can be considered as meeting the second criterion.

[0100] In one or more example methods, simulating the initial emergency value of S108BC includes simulating the initial emergency value of S108BC_3 based on a first range and a predetermined second range.

[0101] In other words, simulating the initial emergency value of S108BC involves selecting an initial emergency value within a first range and / or a predetermined second range. In some examples, simulating the initial emergency value of S108BC_3 based on the first range and the predetermined second range involves repeating the simulation of S108BC_3, for example, 1000 times for each candidate emergency value in the second range.

[0102] In one or more example methods, determining the S108B contingency value based on shipping data and one or more freight parameters includes: for each simulated initial contingency value, determining the total contingency value for users who may select contingency services under the S108BF according to a fifth ratio.

[0103] The total emergency value can be considered as the aggregated emergency value of all users who may select emergency services based on the fifth ratio.

[0104] In one or more example methods, contingency parameters include cancellation parameters and / or no-show parameters associated with shipping.

[0105] Cancellation parameters can, for example, indicate the likelihood of cancellation. In some examples, cancellation parameters can indicate values ​​associated with the cancellation of a shipment, such as costs.

[0106] For example, a default parameter can indicate the probability of default. In some examples, a default parameter can indicate a value associated with a shipping default, such as cost.

[0107] In one or more example methods, the S112 emergency parameters are provided before the shipment has been initiated.

[0108] In one or more example methods, for new users, such as those not present in the system or those who have never made a booking before, transaction data can be sorted to identify the user's first transaction (e.g., the earliest) and its corresponding goods and ports. In some examples, for new users, the contingency probability (the probability of cancellation and no-shows based on the collected data) can be calculated by dividing the total number of cancellations / no-shows by the total number of bookings. For example, for new users, the seasonality of cancellations / no-shows for each good, customer, and port can be obtained by taking an average.

[0109] 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 provide emergency parameters. In some examples, electronic device 300 is an emergency parameter determining electronic device. In some examples, electronic device 300 is an emergency parameter providing electronic device. In some examples, the electronic device is a booking electronic device, such as a booking server device for shipping, such as a shipping booking server device.

[0110] Electronic device 300 is configured (e.g., via memory circuitry 301 and / or interface 303) to acquire shipping data associated with shipping.

[0111] Electronic device 300 is configured to determine one or more freight parameters based on shipping data (e.g., via processor circuitry 302). These one or more freight parameters change over time.

[0112] The electronic device 300 is configured to determine emergency parameters indicating emergency events associated with shipping based on one or more freight parameters and shipping data (e.g., via processor circuitry 302).

[0113] Electronic device 300 is configured (e.g., via processor circuitry 302 and / or interface 303) to provide emergency parameters.

[0114] Processor circuitry 302 is optionally configured to execute Figures 1A to 1B Any of the operations disclosed herein (such as one or more of the following: S102, S104, S106, S106A, S106B, S106C, S106D, S106E, S106F, S108, S108A, S108AA, S108AB, S108B, S108BA, S108BB, S108BC, S108BC_1, S108BC_2, S108BC_3, S108BD, S108BE, S108BF, S110, S112). 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.

[0115] 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 functions can also be implemented via dedicated hardware or firmware, or some combination of hardware, firmware, and / or software.

[0116] 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 devices. 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.

[0117] The memory circuit system 301 can be configured to store in a portion of the memory shipping data, historical shipping data, one or more freight parameters, contingency parameters, contingency values, contingency probabilities, first contingency probability, second contingency probability, third contingency probability, prediction model, first ratio, second ratio, third ratio, fourth ratio, fifth ratio, seasonal factors, current freight rates, initial contingency values, simulated initial contingency values, acceptable user selection ratios, maximum total contingency values ​​across simulations, first range, second range, total contingency values, cancellation parameters, and / or no-show parameters.

[0118] 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 shipping data associated with one or more shipping lines; Based on the shipping data, one or more freight parameters are determined, wherein the one or more freight parameters change over time; Based on the one or more freight parameters and the shipping data, emergency parameters are determined to indicate emergency events associated with the shipping. Provide this emergency parameter.

[0119] Clause 2. The method described in Clause 1, wherein the shipping data includes one or more of the following: booking data associated with a booking of the shipping, booking user data associated with a user who booked the shipping, port data associated with a port of the shipping, and commodity data associated with the goods of the shipping.

[0120] Clause 3. The method according to any one of the preceding clauses, wherein determining the contingency parameters based on the one or more freight parameters and the shipping data includes: - Determine the probability of contingency based on one or more freight parameters and the shipping data.

[0121] Clause 4. The method described in Clauses 2 and 3, wherein determining the emergency probability includes: - Apply the predictive model to the shipping data to provide one or more of the following: a first contingency probability associated with the user, a second contingency probability associated with the port, and a third contingency probability associated with the commodity.

[0122] Clause 5. The method according to Clause 4, wherein determining the emergency probability includes combining the first emergency probability, the second emergency probability, and the third emergency probability into the emergency probability.

[0123] Clause 6. The method according to any one of Clauses 3-5, wherein the predictive model includes a machine learning regression model, and / or a long short-term memory model, and / or a time series prediction model.

[0124] Clause 7. The method according to any one of the preceding clauses, wherein the method comprises: - Obtain historical shipping data, which includes one or more of the following: historical freight rates, historical equipment data, historical booking user data for multiple users for emergency events, historical port data for multiple ports for emergency events, and historical commodity data for multiple commodities for emergency events.

[0125] Clause 8. The method described in Clause 7, wherein determining the one or more freight parameters based on the shipping data includes: - A first ratio is generated based on historical booking user data, which indicates the proportion of contingency events for each user per shipment among the multiple users; - A second ratio is generated based on historical port data, indicating the proportion of contingency events for each shipment at each of the multiple ports; and - A third ratio is generated based on historical commodity data, which indicates the proportion of emergency events for each commodity in each shipment among the multiple commodities.

[0126] Clause 9. The method according to any one of the preceding clauses, wherein determining the emergency parameter includes: - Determine the emergency value associated with the emergency parameter based on the shipping data and the one or more freight parameters.

[0127] Clause 10. The method described in Clauses 7 and 9, wherein determining the contingency value based on the shipping data includes: determining a seasonal factor based on the one or more freight parameters and historical shipping data, and applying the seasonal factor to the contingency value.

[0128] Clause 11. The method according to any one of Clauses 9-10, wherein determining the one or more freight parameters based on the shipping data includes: - Obtain the current freight rates associated with this shipment.

[0129] Clause 12. The method according to any one of the preceding clauses, wherein determining the contingency value based on the shipping data includes determining the contingency value by simulating one or more initial contingency values.

[0130] Clause 13. The method described in Clauses 11 and 12, wherein determining the contingency value based on the shipping data includes: - Select an initial emergency value from the simulated initial emergency values, which achieves an acceptable user selection ratio and a maximum total emergency value across simulations; and - The emergency value is determined based on the current freight rate and the selected initial emergency value.

[0131] Clause 14. The method according to any one of the preceding clauses, wherein determining the one or more freight parameters based on the shipping data includes: - A fourth ratio is determined based on this historical shipping data, which indicates the proportion of users who experienced an emergency event; - The fifth ratio is determined based on the fourth ratio, which indicates the proportion of users who may choose emergency services when booking the shipment; Clause 15. The method according to any one of Clauses 12 to 14, wherein simulating the initial emergency value includes: - Obtain a first range of the user selection ratio, wherein the user selection ratio indicates the target ratio of users who select the emergency service; - Obtain a predetermined second range of candidate emergency values ​​for simulation; and - The initial emergency value is simulated based on the first range and the predetermined second range.

[0132] Clause 16. The method according to any one of Clauses 9 and 14-15, wherein determining the contingency value based on the shipping data and the one or more freight parameters comprises: determining the total contingency value for each simulated initial contingency value according to the fifth ratio for users who may select the contingency service.

[0133] Clause 17. The method according to any one of the preceding clauses, wherein the method includes determining the emergency parameter based on the emergency probability and the emergency value.

[0134] Clause 18. The method according to any one of the preceding clauses, wherein the contingency parameters include cancellation parameters associated with the shipping and / or default parameters associated with the shipping.

[0135] Clause 19. The method according to any one of the preceding clauses, wherein the provision of the contingency parameters is performed prior to the commencement of the shipping.

[0136] 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.

[0137] 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 according to items 1 to 19.

[0138] The use of terms such as "first," "second," "third," and "fourth," "primary," "secondary," and "tertiary" 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" does not indicate any order or importance, but is used to distinguish one element from another. Note 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.

[0139] 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 exemplary embodiments. The circuit systems or operations included in the dashed lines are exemplary 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.

[0140] It should be noted that the word "including" does not necessarily exclude the existence of other elements or steps besides those listed.

[0141] It should be noted that the words "one" or "a kind" preceding an element do not preclude the existence of multiple such elements.

[0142] It is important to note that the term "indicates..." can be considered as "associated with", "related to", "describes", "represents", and / or "defines". The terms "indicates...", "associated with", "related to", "describes", "represents", and "defines" are used interchangeably. The term "indicates..." can be considered as indicating a relationship. For example, weight data indicating weights may include one or more weight parameters.

[0143] It is important to note that the term "based on" can be interpreted 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 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.

[0144] Functions can represent the relationship between inputs and outputs, such as mathematical relationships, database relationships, hardware relationships, logical relationships, and / or other suitable relationships.

[0145] It should also be 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 both hardware and software, and several “components,” “units,” or “devices” may be represented by the same hardware object.

[0146] The various exemplary methods, apparatuses, nodes, and systems described herein are described in 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 functionality described in such steps or processes.

[0147] 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 shipping data associated with one or more shipping lines; One or more freight parameters are determined based on the shipping data, wherein the one or more freight parameters change over time; Emergency parameters indicating contingency events associated with the shipping are determined based on the one or more freight parameters and the shipping data. Provide the aforementioned emergency parameters.

2. The method of claim 1, wherein, The shipping data includes one or more of the following: booking data associated with booking the shipping, booking user data associated with the user who booked the shipping, port data associated with the port of the shipping, and commodity data associated with the goods in the shipping.

3. The method according to any of the preceding claims, wherein, Determining emergency parameters based on the one or more freight parameters and the shipping data includes: - Determine the probability of contingency based on the one or more freight parameters and the shipping data.

4. The method according to claims 2 and 3, wherein, Determining the emergency probability includes: - Apply the predictive model to the shipping data to provide one or more of the following: a first contingency probability associated with the user, a second contingency probability associated with the port, and a third contingency probability associated with the goods.

5. The method of claim 4, wherein, Determining the emergency probability includes combining the first emergency probability, the second emergency probability, and the third emergency probability into the emergency probability.

6. The method of any one of claims 3 to 5, wherein, The prediction models include machine learning regression models, and / or long short-term memory models, and / or time series prediction models.

7. The method according to any of the preceding claims, wherein, The method includes: - Obtain historical shipping data, wherein the historical shipping data includes one or more of the following: historical freight rates, historical equipment data, historical booking user data for multiple users for emergency events, historical port data for multiple ports for emergency events, and historical commodity data for multiple commodities for emergency events.

8. The method of claim 7, wherein, Determining the one or more freight parameters based on the shipping data includes: - A first ratio is generated based on historical booking user data, the first ratio indicating the proportion of contingency events for each user per shipment among the plurality of users; - A second ratio is generated based on historical port data, indicating the proportion of contingency events for each shipment at each of the plurality of ports; and - A third ratio is generated based on historical commodity data, which indicates the proportion of emergency events for each commodity in each shipment among the plurality of commodities.

9. The method of any of the preceding claims, wherein, Determining the emergency parameters includes: - Determine the emergency value associated with the emergency parameter based on the shipping data and the one or more freight parameters.

10. The method of any of the preceding claims, wherein, Determining the contingency value based on the shipping data includes determining the contingency value by simulating one or more initial contingency values.

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