CN118941175A - Cargo flight booking method, device, electronic device and storage medium - Google Patents
Cargo flight booking method, device, electronic device and storage medium Download PDFInfo
- Publication number
- CN118941175A CN118941175A CN202310531259.8A CN202310531259A CN118941175A CN 118941175 A CN118941175 A CN 118941175A CN 202310531259 A CN202310531259 A CN 202310531259A CN 118941175 A CN118941175 A CN 118941175A
- Authority
- CN
- China
- Prior art keywords
- booking
- information
- flight
- transported
- characteristic information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06311—Scheduling, planning or task assignment for a person or group
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0633—Managing shopping lists, e.g. compiling or processing purchase lists
- G06Q30/0635—Managing shopping lists, e.g. compiling or processing purchase lists replenishment orders; recurring orders
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Theoretical Computer Science (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Educational Administration (AREA)
- Game Theory and Decision Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the invention discloses a freight flight booking method, a device, electronic equipment and a storage medium, wherein the booking method comprises the following steps: acquiring article characteristic information of an article to be transported and flight characteristic information of a current candidate flight; inputting the article characteristic information of the article to be transported and the flight characteristic information of the current candidate flight into a booking recommendation model to generate recommendation booking information for the article to be transported by using the booking recommendation model; displaying the recommended booking information and monitoring the operation on the recommended booking information; generating target booking information for the to-be-transported goods according to the operation of recommending booking information, and booking the to-be-transported goods according to the target booking information. The embodiment of the invention can make booking recommendation for staff by using a booking recommendation model, and the recommendation result can be further operated by the staff, and the booking is completed by using algorithm recommendation to guide booking, so that the advantages of the algorithm and the wisdom of the staff are integrated, and the booking accuracy and booking efficiency are improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of logistics, in particular to a method, a device, electronic equipment and a storage medium for booking a freight flight.
Background
At present, in aviation booking service, a worker (such as a sorting person or an operator) confirms the transportation goods volume on site, collects flight information corresponding to the line goods volume off line, and then manually selects flights and applies for booking, namely, manually booking. In the process of realizing the invention, the inventor finds that the manual booking depends on personnel experience, and the personnel experience is different, so that the booking accuracy is not guaranteed, and the manual booking efficiency is low.
Disclosure of Invention
The embodiment of the invention provides a freight flight booking method, a freight flight booking device, electronic equipment and a storage medium, which can improve booking accuracy and booking efficiency.
In a first aspect, a method for booking a freight flight provided by an embodiment of the present invention includes:
acquiring article characteristic information of an article to be transported and flight characteristic information of a current candidate flight;
Inputting the article characteristic information of the article to be transported and the flight characteristic information of the current candidate flight into a booking recommendation model to generate recommendation booking information for the article to be transported by using the booking recommendation model;
Displaying the recommended booking information and monitoring the operation on the recommended booking information;
generating target booking information for the to-be-transported object according to the operation of recommending booking information, and booking the to-be-transported object according to the target booking information.
In a second aspect, a cargo flight booking device provided by an embodiment of the present invention includes:
the acquisition module is used for acquiring the article characteristic information of the article to be transported and the flight characteristic information of the current candidate flight;
The recommendation module is used for inputting the article characteristic information of the article to be transported and the flight characteristic information of the current candidate flight into a booking recommendation model so as to generate recommendation booking information for the article to be transported by utilizing the booking recommendation model;
The display module is used for displaying the recommended booking information and monitoring the operation of the recommended booking information;
And the booking module is used for generating target booking information for the to-be-transported object according to the operation of recommending booking information, and booking the to-be-transported object according to the target booking information.
In a third aspect, an electronic device provided by an embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the method for booking a cargo flight according to any of the embodiments of the present invention when executing the program.
In a fourth aspect, embodiments of the present invention provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method for booking flights according to any of the embodiments of the present invention.
According to the scheme provided by the embodiment of the invention, the article characteristic information of the article to be transported and the flight characteristic information of the current candidate flight can be obtained; inputting the article characteristic information of the article to be transported and the flight characteristic information of the current candidate flight into a booking recommendation model to generate recommendation booking information for the article to be transported by using the booking recommendation model; displaying the recommended booking information and monitoring the operation on the recommended booking information; generating target booking information for the to-be-transported goods according to the operation of recommending booking information, and booking the to-be-transported goods according to the target booking information. In the embodiment of the invention, the booking recommendation model can be used for making booking recommendation for the staff, the recommendation result can be further operated by the staff, the booking is guided by the algorithm recommendation, the booking is completed by combining the algorithm recommendation and the manual operation, the algorithm advantage and the wisdom of the person are integrated, and the booking accuracy and the booking efficiency are improved.
Drawings
In order to more clearly illustrate the technical solutions of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and should not be considered as limiting the scope, and that other related drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for booking a cargo flight according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a training method of a cabin booking recommendation model according to an embodiment of the present invention;
fig. 3 is another flow chart of a method for booking a freight flight according to an embodiment of the present invention;
FIG. 4 is an exemplary diagram of a hold page provided by an embodiment of the present invention;
FIG. 5 is another exemplary illustration of a hold page provided by an embodiment of the present invention;
fig. 6 is a schematic structural diagram of a cargo flight booking apparatus according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Fig. 1 is a schematic flow chart of a cargo flight booking method according to an embodiment of the present invention, where the method may be applied in a scenario of transporting cargo using air resources, and the method may be performed by a cargo flight booking device according to an embodiment of the present invention, where the device may be implemented in software and/or hardware. In a specific embodiment, the device may be integrated in an electronic device, and the electronic device may be a computer, a personal computer, or the like, on which the cabin booking system provided by the embodiment of the present invention may be installed. The following embodiments will be described taking the integration of the device in an electronic apparatus as an example. Referring to fig. 1, the method may specifically include the steps of:
Step 101, acquiring article characteristic information of articles to be transported and flight characteristic information of current candidate flights.
Specifically, the articles to be transported may be any articles that need to be transported by air resources, including, but not limited to, flowers, seafood, fruits, vegetables, etc.; the item characteristic information may include transportation characteristic information of the item, such as a shipping time, an origin-sorting place, an origin airport, a destination airport, a carrier name, a flight type (early, medium, late), etc., and self characteristic information, such as an item type, a size, a booking amount, etc., which may be composed of characteristics of a plurality of dimensions or types; the article characteristic information of the article to be transported can be obtained according to user input, namely the article characteristic information can be input by a user on a designated page; or the article characteristic information of the article to be transported can also be predicted according to the historical transportation information, namely the electronic equipment can analyze the articles transported by the aviation resources in the past preset time period so as to predict the articles possibly needing to be transported in the future appointed time period and the related information thereof, thereby obtaining the article characteristic information of the article to be transported. The current candidate flights can be searched or collected according to the item characteristic information of the item to be transported, and the number of the current candidate flights can be multiple; the flight characteristic information may include the flight number of the candidate flight, the origin airport, the destination airport, the departure time, the arrival time, the carrier, the available space, the price calculation mode, the type of load, the operation quality, etc., and may also be composed of characteristics of multiple dimensions or types.
Step 102, inputting the item characteristic information of the item to be transported and the flight characteristic information of the current candidate flight into a booking recommendation model to generate recommendation booking information for the item to be transported by using the booking recommendation model.
The booking recommendation model can be located on the electronic equipment, the booking recommendation model can be obtained through training data in advance, the training data can be created through collecting a large number of historical booking tasks, the historical booking tasks can be realized through manual booking, the booking can also be recommended by a model or algorithm and can be realized through manual confirmation, the historical booking tasks can comprise relevant information of articles of the historical booking (namely article characteristic information of the historical transportation articles), relevant information of candidate flights when the articles are booked for the corresponding articles at historical moments (namely, flight characteristic information of the historical candidate flights), flights booked for the corresponding articles and relevant information (namely, actual booking information). The article characteristic information of the historical transportation article and the flight characteristic information of the historical candidate flights can be used as sample data, the corresponding actual booking information is used as a sample label, a large number of samples are constructed, and the samples are used as training data. The cabin booking recommendation model can be a model realized by adopting machine learning, deep learning and other algorithms.
After the item feature information of the item to be transported and the flight feature information of the current candidate flight are input into the booking recommendation model, the booking recommendation model can operate by adopting a pre-learned algorithm or rule based on the item feature information of the item to be transported and the flight feature information of the current candidate flight, a target flight is screened out from the current candidate flights, recommendation booking information is generated for the item to be transported based on the screened target flight, and one or more recommendation booking information can be provided.
The recommended booking information may include a recommended flight number, a recommended booking amount, a recommended class type, a carrier type of the recommended flight, an origin airport, a destination airport, an origin time, an arrival time, etc. Among them, the class types may include policy class, protocol class, and general class according to the service provided and the pricing manner. The policy bunk refers to a signed bunk which is used for preferentially distributing goods on the basis of future strategy or policy cooperation agreement with a voyage or agent; the protocol cabin refers to a priority upper cabin signed by the whole package of a cabin, a package amount, a package plate and the like by a voyage or an agent; a common bunk refers to a bunk signed with a voyage or agency that is typically paid according to the fill weight threshold. From the cost point of view, the policy is usually most favorable, the protocol is second most favorable, and the common cabin is relatively high in price, so the priorities of the three cabin types are generally: policy bunk > protocol bunk > normal bunk.
And 103, displaying the recommended booking information and monitoring the operation on the recommended booking information.
Specifically, the recommendation cabin information may be displayed on a preset page of the electronic device, or may be sent to a personal terminal of a worker (such as an operator or a sorter) to display the recommendation cabin information on a specified page of the personal terminal. The displayed recommended booking information can be operated by a worker, the operation can comprise confirmation operation, adjustment operation and the like, and the electronic equipment can monitor the operation of the worker on the recommended booking information.
And 104, generating target booking information for the to-be-transported goods according to the operation of recommending booking information, and booking the to-be-transported goods according to the target booking information.
For example, when the operation on the recommended cabin information is a confirmation operation, for example, the staff member is stated to recognize that the algorithm recommendation result is accurate, the target cabin information may be generated according to the recommended cabin information (for example, the recommended cabin information is directly confirmed as the target cabin information), and at the same time, the recommended cabin information may be marked with an "accurate" sign. When the operation of recommending the booking information is the adjustment operation, the staff is not authorized to recommend the algorithm, and the algorithm recommendation result is not accurate enough, the target booking information can be generated according to the adjusted recommending booking information, and meanwhile, the recommending booking information can be marked with an inaccurate mark. In a specific implementation, the adjustment operation on the recommendation booking information may include deleting the recommendation booking information, modifying data in the recommendation booking information (such as modifying the recommendation booking type, modifying the recommendation booking amount), replacing the recommendation booking information with other candidate booking information, and the like.
The target booking information may include a flight number, a booking amount, a cabin type, an originating airport, a target airport, an originating time, an arrival time, a carrier type, etc. of a cabin. After the target booking information is generated, a booking application can be initiated to a corresponding batch booking management end according to the target booking information, and after the application is successful, booking of the articles to be transported is completed.
Further, the electronic device can time the marking result of the repeated disc recommended booking information, calculate the recommendation accuracy of the booking recommendation model according to the marking result of the recommended booking information, and initiate updating training of the booking recommendation model if the recommendation accuracy is lower than a certain standard.
According to the scheme provided by the embodiment of the invention, the article characteristic information of the article to be transported and the flight characteristic information of the current candidate flight can be obtained; inputting the article characteristic information of the article to be transported and the flight characteristic information of the current candidate flight into a booking recommendation model to generate recommendation booking information for the article to be transported by using the booking recommendation model; displaying the recommended booking information and monitoring the operation on the recommended booking information; generating booking information for the to-be-transported goods according to the operation of recommending booking information, and booking the to-be-transported goods according to the booking information. In the embodiment of the invention, the booking recommendation model can be used for making booking recommendation for the staff, the recommendation result can be further operated by the staff, the booking is guided by the algorithm recommendation, the booking is completed by combining the algorithm recommendation and the manual operation, the algorithm advantage and the wisdom of the person are integrated, and the booking accuracy and the booking efficiency are improved.
The following describes a training process of the cabin booking recommendation model according to an embodiment of the present invention, which may specifically be shown in fig. 2, and may include the following steps:
step 201, acquiring historical training data, wherein the historical training data comprises article characteristic information of historical transportation articles, flight characteristic information of historical candidate flights and actual booking information of the historical transportation articles.
The historical training data may be created by collecting a large number of historical booking tasks, which may be implemented by manual booking, or recommending booking by using a model or algorithm and by manual confirmation, without limitation. For example, all booking tasks generated by the whole network or a specified range (such as a company) in the past period (such as the past three months, the past half year and the like) can be collected, and data in the booking tasks are analyzed and classified to obtain the item characteristic information of the historical transportation items, the flight characteristic information of the historical candidate flights and the actual booking information of the historical transportation items; the article characteristic information of the historical transportation article and the flight characteristic information of the historical candidate flights can be used as sample data, the corresponding actual booking information is used as a sample label, a large number of samples are constructed, and the samples are used as historical training data.
Wherein, the article characteristic information of the historical transported article and the article characteristic information of the article to be transported can have the same dimension or type of characteristic, but the specific values are different; the flight characteristic information of the historical candidate flight may have the same dimension or type of characteristics as the flight characteristic information of the current candidate flight, but the specific values are different. For example, the characteristic of the dimension, that is, the booking amount, is included in the item characteristic information of the historical transported item and the item characteristic information of the item to be transported, but the specific value of the booking amount is different.
Step 202, taking the characteristic information of the historical transportation goods and the flight characteristic information of the historical candidate flights as training inputs of a preset recommendation model, guiding the training output of the preset recommendation model by using the actual booking information of the historical transportation goods, and training the preset recommendation model to obtain a booking recommendation model.
The preset recommendation model may be a model implemented by using an algorithm such as machine learning and deep learning. Specifically, the item feature information of the historical transportation item and the flight feature information of the historical candidate flight may be input into a preset recommendation model, so as to generate training recommendation information for the historical transportation item by using the preset recommendation model, where the training recommendation information may be cabin booking information recommended for the historical transportation item in the training process; determining training loss according to training recommendation information and actual booking information of each sample, and reversely optimizing model parameters of a preset recommendation model based on the training loss; and (3) training for multiple rounds until a training cut-off condition is met (for example, the number of training rounds reaches a preset number or the training loss does not exceed a preset loss), and stopping training, so that a cabin booking recommendation model is obtained.
In practical application, the cabin booking recommendation model can be updated and trained according to practical requirements. For example, in the process of using the booking recommendation model, the recommendation booking information generated by the model is marked according to the approval condition of staff on the recommendation booking information, the recommendation accuracy of the booking recommendation model is counted in real time according to the marking condition, and when the recommendation accuracy is lower than the preset accuracy, the updating training process of the model can be initiated.
Specifically, when updating and training the booking recommendation model, data generated in the using process of the booking recommendation model can be collected, the data can comprise article characteristic information of articles to be transported, flight characteristic information of current candidate flights and target booking information of the articles to be transported, new samples are constructed by the data, historical training data are updated by the new samples, so that target training data are obtained, and updating and training are carried out on the booking recommendation model by the target training data. The historical training data is updated by using the new sample, or the new sample is directly added into the historical training data, so that the historical training data is expanded.
The training and updating training process of the booking recommendation model can be implemented locally on the electronic device, can be implemented on other devices (such as a server), and when training and updating training of the booking recommendation model are implemented on the other devices, the electronic device can download or acquire the trained booking recommendation model from the other devices.
In the embodiment of the invention, the sample with the label is used for model training, so that the model training efficiency can be improved, and the accuracy of the output result of the model obtained by training can be improved. In addition, the cabin booking recommendation model can be updated and trained according to actual conditions, and accuracy of a model output result can be further improved.
The method for booking a freight flight according to the embodiment of the present invention, as shown in fig. 3, may specifically include the following steps:
step 301, confirming a booking mode, executing step 302 when the booking mode is intelligent booking, and executing step 307 when the booking mode is manual booking.
Namely, the cabin booking system provided by the embodiment of the invention can support different cabin booking modes: intelligent booking and manual booking; the intelligent booking can be a booking method for starting model recommendation, and the manual booking can be a method for booking according to manual experience without starting model recommendation. By being compatible with different booking modes, more choices are provided for the staff, and the booking mode selected by the staff can be determined.
Step 302, acquiring article characteristic information of an article to be transported and flight characteristic information of a current candidate flight.
In practical application, when intelligent booking is selected, two booking modes can be adopted: the real-time booking can be a method for recommending booking by using real-time data, the offline booking can be a method for recommending booking by using offline data, and staff can select a required booking mode according to actual needs.
Specifically, when off-line booking is selected, a worker can enter a booking system to start a booking task, and the booking system can start to execute the started booking task at a designated time; when the booking task is executed, the booking system can predict articles and related information which need to be transported through aviation resources in the future based on the historical transportation information, so that article characteristic information of the articles to be transported is obtained. For example, the booking system may obtain information about items (e.g., item type, traffic, etc.) that were transported by the flight over the past three days at a fixed time per day (e.g., 12:00 a day), thereby predicting information about items that may need to be transported by the flight for three days in the future. In addition, when predicting the articles and related information to be transported through the aviation resource in the future, other data may be referred to, for example, inventory data, sorting data, order data, etc. may also be referred to, so as to improve the prediction accuracy, which is not limited herein. The flight characteristic information of the current candidate flight can be searched or collected by the booking system according to the predicted article characteristic information of the articles to be transported.
Specifically, when selecting real-time booking, a worker can enter a booking system, and configure article characteristic information of the articles to be transported on a preset page, and the booking system acquires the article characteristic information of the articles to be transported which is manually configured. The flight characteristic information of the current candidate flight can be searched or collected by the booking system according to the configured article characteristic information of the articles to be transported. For example, as shown in fig. 4, a real-time booking page of intelligent booking may be configured (input) by a worker on the page with article feature information such as origin sorting place, delivery time, cargo type, origin airport, destination airport, booking volume and the like of articles to be transported, and trigger an "intelligent calculation and update" control of the page; after the intelligent calculation and update control is triggered, the current candidate flights can be collected or searched according to the characteristic information of the articles to be transported, the characteristic information of the articles to be transported and the characteristic information of the flights of the current candidate flights are input into a booking recommendation model, so that booking recommendation information is generated, and the booking recommendation information can be displayed on a corresponding page.
Step 303, inputting the item feature information of the item to be transported and the flight feature information of the current candidate flight into a booking recommendation model, so as to generate recommendation booking information for the item to be transported by using the booking recommendation model.
The booking recommendation model may match the target flight from the current candidate flights based on the learned algorithm or rule and generate recommendation booking information based on the target flight. For example, algorithms or rules learned by the booking recommendation model may include bunk priority, flight timeliness meeting transportation demand, price optimization, etc.
Specifically, when matching the target flight from the current candidate flights according to the item characteristic information and algorithm rules of the item to be transported, the cabin booking recommendation model can adopt two matching modes of accurate matching and fuzzy matching. For example, shipping time, origin airport, destination airport, carrier, etc. may be matched exactly; for example, flights with take-off times within 24 hours of the item delivery time may be strictly matched from the current candidate flights, and flights corresponding to and consistent with the origin airport, destination airport, carrier in the item characteristic information of the item to be transported may be matched from the current candidate flights. The types of the items, the types of the flights and the like can be fuzzy matched; for the type of the goods, if the goods can be accurately matched with the flights, adopting an exact matching, and if the goods cannot be accurately matched with the flights, adopting an spam strategy, for example, only general goods in candidate flights are adopted, and the goods to be transported are fresh, so that the flights corresponding to the general goods are matched; similarly for flight types, if exact matches are not found, the other types are fuzzy matched.
After matching out the target flight, counting the type of the cabin available for the target flight and the cabin booking amount available for the cabin of the corresponding type; the priority of the class type may be as follows: the strategy bunk protocol bunk is capable of giving priority to the booking quantity which can be provided by the bunk type with the highest priority, and if the booking quantity which can be provided by the bunk type with the highest priority is smaller than the booking quantity of the articles to be transported, the booking quantities which can be provided by other bunk types are sequentially considered according to the priority; if all the hold types can provide hold amounts which are added up or smaller than the hold amount of the articles to be transported, be fully booked can use the hold, and then the difference value between the hold amount of the articles to be transported and the available hold amount is marked as a gap amount.
Since the billing of the booking cabin can be rounded, the booking cabin quantity can be rounded according to the preset rule partition to obtain the rounding cargo quantity. For example, when the booking quantity is 0, the rounding quantity is 0; when the cabin booking amount is not more than 50kg, rounding up to a multiple of 10 kg; when the cabin booking amount exceeds 50kg, rounding up to a multiple of 50 kg.
Next, a hold fee is calculated according to the rounded cargo amount, the fee calculation is typically a segment calculation, and the price=max of each segment (the calculated price of the segment by weight, the lowest price of the segment). Finally, selecting an optimal flight from the target flights according to the prices, wherein the optimal flight can be the lowest-priced flight.
Step 304, displaying the recommended booking information, monitoring the operation of the recommended booking information, executing step 305 when the operation is a confirmation operation, and executing step 306 when the operation is an adjustment operation.
Specifically, when the offline booking of the intelligent booking is selected, after the booking system generates the recommended booking information, the recommended booking information can be added into a booking recommendation table, and when a worker needs to consult the recommended booking information in the booking recommendation table, the recommended booking information can be displayed. When the intelligent booking is selected, the booking system generates recommended booking information and then can directly display the recommended booking information.
And 305, generating target booking information according to the recommended booking information.
The recommendation result of the algorithm is accurate, and the recommendation booking information can be directly confirmed to be the target booking information.
And 306, generating target booking information according to the adjusted recommended booking information.
That is, the recommendation result of the algorithm is not accurate enough, the adjusted booking information needs to be acquired, and the adjusted booking information is confirmed to be the target booking information. In a specific implementation, the adjustment operation on the recommendation booking information may include deleting the recommendation booking information, modifying data in the recommendation booking information (such as recommendation booking amount), replacing the recommendation booking information with other candidate booking information, and the like.
Step 307, displaying the flight characteristic information of the current candidate flight, and monitoring the operation of the current candidate flight based on the displayed flight characteristic information of the current candidate flight.
That is, when manual booking is selected, the booking system may collect or search for the current candidate flight based on user input and display flight characteristic information of the current candidate flight, and the operation of the current candidate flight may be a selection operation. For example, as shown in fig. 5, the manual booking page may include a search field and a display field, where the search field may be used for a worker to perform search input, the display field may display a current candidate flight searched based on the search input and flight characteristic information thereof, the displayed current candidate flight may be selected by the worker, the worker may select a desired flight and a cabin type from the displayed current candidate flights and input a corresponding booking amount, and finally trigger "confirm booking" to generate target booking information.
Step 308, generating target booking information for the to-be-transported item according to the operation on the current candidate flight.
Step 309, booking the object to be transported according to the target booking information.
The target booking information may include a flight number, a booking amount, a cabin type, an originating airport, a target airport, an originating time, an arrival time, a carrier type, etc. of a cabin. After the target booking information is generated, a booking application can be initiated to a corresponding batch cabin management end according to the target booking information, and the batch cabin management end is waited for the batch recovery; if the capsule quantity recovered by the batch capsule management end meets the capsule quantity applied for reservation (for example, the capsule quantity recovered is equal to the capsule quantity applied for reservation), successfully reserving capsules for the articles to be transported; if the batch cabin management end does not meet the cabin application quantity (for example, the batch cabin quantity is equal to 0 or is smaller than the cabin application quantity), the staff can continue to select the manual cabin or the intelligent cabin according to the residual cabin.
In practical application, the method can collect the cabin booking results in the manual cabin booking mode and the intelligent cabin booking mode, update the training data based on the collected data, and update and train the cabin booking recommendation model by utilizing the updated training data.
According to the scheme provided by the embodiment of the invention, the article characteristic information of the article to be transported and the flight characteristic information of the current candidate flight can be obtained; inputting the article characteristic information of the article to be transported and the flight characteristic information of the current candidate flight into a booking recommendation model to generate recommendation booking information for the article to be transported by using the booking recommendation model; displaying the recommended booking information and monitoring the operation on the recommended booking information; generating booking information for the to-be-transported goods according to the operation of recommending booking information, and booking the to-be-transported goods according to the booking information. In the embodiment of the invention, the booking recommendation model can be used for making booking recommendation for the staff, the recommendation result can be further operated by the staff, the booking is guided by the algorithm recommendation, the booking is completed by combining the algorithm recommendation and the manual operation, the algorithm advantage and the wisdom of the person are integrated, and the booking accuracy and the booking efficiency are improved.
Fig. 6 is a schematic structural diagram of a cargo flight booking apparatus according to an embodiment of the present invention, and as shown in fig. 6, the apparatus may specifically include:
an acquiring module 501, configured to acquire item feature information of an item to be transported and flight feature information of a current candidate flight;
the recommendation module 502 is configured to input the item feature information of the item to be transported and the flight feature information of the current candidate flight into a booking recommendation model, so as to generate recommendation booking information for the item to be transported by using the booking recommendation model;
the display module 503 is configured to display the recommended booking information and monitor an operation on the recommended booking information;
And the booking module 504 is configured to generate target booking information for the to-be-transported object according to the operation on the recommended booking information, and booking the to-be-transported object according to the target booking information.
In one embodiment, the acquiring module 501 acquires item feature information of an item to be transported, including:
acquiring article characteristic information of the articles to be transported, which are configured on a preset page; or alternatively
And acquiring the article characteristic information of the article to be transported, which is predicted based on the historical transportation information.
In one embodiment, the apparatus further comprises:
The confirmation module is configured to confirm the selected booking mode, and trigger the acquisition module 501 to execute the acquisition of the item feature information of the item to be transported and the flight feature information of the current candidate flight when the booking mode is intelligent booking.
In an embodiment, the display module 503 is further configured to display flight characteristic information of the current candidate flight when the booking mode is manual booking, and monitor an operation on the current candidate flight based on the displayed flight characteristic information of the current candidate flight;
The booking module 504 is further configured to generate target booking information for the to-be-transported item according to the operation on the current candidate flight, and book a cabin for the to-be-transported item according to the target booking information.
In one embodiment, the display module 503 monitors the operation of the recommended booking information, including:
And monitoring a confirmation operation or an adjustment operation of the recommended booking information.
In one embodiment, the apparatus further comprises:
The model training module is used for acquiring historical training data, wherein the historical training data comprises article characteristic information of historical transportation articles, flight characteristic information of historical candidate flights and actual booking information of the historical transportation articles; and training the preset recommendation model by taking the article characteristic information of the historical transportation articles and the flight characteristic information of the historical candidate flights as training inputs of the preset recommendation model and guiding the training output of the preset recommendation model by taking the actual booking information of the historical transportation articles, thereby obtaining the booking recommendation model.
In one embodiment, the model training module is further configured to:
Updating the historical training data according to the item characteristic information of the item to be transported, the flight characteristic information of the current candidate flight and the target booking information of the item to be transported to obtain target training data;
And updating and training the cabin booking recommendation model by utilizing the target training data.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional modules is illustrated, and in practical application, the above-described functional allocation may be performed by different functional modules according to needs, i.e. the internal structure of the apparatus is divided into different functional modules to perform all or part of the functions described above. The specific working process of the functional module described above may refer to the corresponding process in the foregoing method embodiment, and will not be described herein.
The device provided by the embodiment of the invention can acquire the article characteristic information of the article to be transported and the flight characteristic information of the current candidate flight; inputting the article characteristic information of the article to be transported and the flight characteristic information of the current candidate flight into a booking recommendation model to generate recommendation booking information for the article to be transported by using the booking recommendation model; displaying the recommended booking information and monitoring the operation on the recommended booking information; generating target booking information for the to-be-transported goods according to the operation of recommending booking information, and booking the to-be-transported goods according to the target booking information. In the embodiment of the invention, the booking recommendation model can be used for making booking recommendation for the staff, the recommendation result can be further operated by the staff, the booking is guided by the algorithm recommendation, the booking is completed by combining the algorithm recommendation and the manual operation, the algorithm advantage and the wisdom of the person are integrated, and the booking accuracy and the booking efficiency are improved.
The embodiment of the invention also provides electronic equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the cargo flight booking method provided by any one of the embodiments when executing the program.
The embodiment of the invention also provides a computer readable medium, wherein a computer program is stored on the computer readable medium, and the program is executed by a processor to realize the cargo flight booking method provided by any embodiment.
Referring now to FIG. 7, there is illustrated a schematic diagram of a computer system 600 suitable for use in implementing an electronic device of an embodiment of the present invention. The electronic device shown in fig. 7 is only an example and should not impose any limitation on the functionality and scope of use of the present invention.
As shown in fig. 7, the computer system 600 includes a Central Processing Unit (CPU) 601, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the computer system 600 are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other through a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, mouse, etc.; an output portion 607 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The drive 610 is also connected to the I/O interface 605 as needed. Removable media 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed as needed on drive 610 so that a computer program read therefrom is installed as needed into storage section 608.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication portion 609, and/or installed from the removable medium 611. The above-described functions defined in the system of the present invention are performed when the computer program is executed by a Central Processing Unit (CPU) 601.
The computer readable medium shown in the present invention may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules and/or units described in the present invention may be implemented in software or in hardware. The described modules and/or units may also be provided in a processor, e.g., may be described as: a processor includes an acquisition module, a recommendation module, a display module, and a booking module. The names of these modules do not constitute a limitation on the module itself in some cases.
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be present alone without being fitted into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to include: acquiring article characteristic information of an article to be transported and flight characteristic information of a current candidate flight; inputting the article characteristic information of the article to be transported and the flight characteristic information of the current candidate flight into a booking recommendation model to generate recommendation booking information for the article to be transported by using the booking recommendation model; displaying the recommended booking information and monitoring the operation on the recommended booking information; generating target booking information for the to-be-transported object according to the operation of recommending booking information, and booking the to-be-transported object according to the target booking information.
According to the technical scheme, the article characteristic information of the article to be transported and the flight characteristic information of the current candidate flight can be obtained; inputting the article characteristic information of the article to be transported and the flight characteristic information of the current candidate flight into a booking recommendation model to generate recommendation booking information for the article to be transported by using the booking recommendation model; displaying the recommended booking information and monitoring the operation on the recommended booking information; generating target booking information for the to-be-transported goods according to the operation of recommending booking information, and booking the to-be-transported goods according to the target booking information. In the embodiment of the invention, the booking recommendation model can be used for making booking recommendation for the staff, the recommendation result can be further operated by the staff, the booking is guided by the algorithm recommendation, the booking is completed by combining the algorithm recommendation and the manual operation, the algorithm advantage and the wisdom of the person are integrated, and the booking accuracy and the booking efficiency are improved.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
It should be noted that, in the technical solution of the present disclosure, the related aspects of collecting, updating, analyzing, processing, using, transmitting, storing, etc. of the personal information of the user all conform to the rules of the related laws and regulations, and are used for legal purposes without violating the public order colloquial. Necessary measures are taken for the personal information of the user, illegal access to the personal information data of the user is prevented, and the personal information security, network security and national security of the user are maintained.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives can occur depending upon design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method for booking a cargo flight, comprising:
acquiring article characteristic information of an article to be transported and flight characteristic information of a current candidate flight;
Inputting the article characteristic information of the article to be transported and the flight characteristic information of the current candidate flight into a booking recommendation model to generate recommendation booking information for the article to be transported by using the booking recommendation model;
Displaying the recommended booking information and monitoring the operation on the recommended booking information;
generating target booking information for the to-be-transported object according to the operation of recommending booking information, and booking the to-be-transported object according to the target booking information.
2. The method of claim 1, wherein the obtaining the item characteristic information of the item to be transported comprises:
acquiring article characteristic information of the articles to be transported, which are configured on a preset page; or alternatively
And acquiring the article characteristic information of the article to be transported, which is predicted based on the historical transportation information.
3. The method for booking a cargo flight according to claim 1, further comprising, before acquiring the item characteristic information of the item to be transported and the flight characteristic information of the current candidate flight:
confirming the selected booking mode;
and when the booking mode is intelligent booking, triggering and executing the acquisition of the article characteristic information of the article to be transported and the flight characteristic information of the current candidate flight.
4. A method of booking a cargo flight as claimed in claim 3, further comprising, after confirming the selected booking mode:
when the booking mode is manual booking, displaying the flight characteristic information of the current candidate flight, and monitoring the operation of the current candidate flight based on the displayed flight characteristic information of the current candidate flight;
generating target booking information for the to-be-transported object according to the operation on the current candidate flight, and booking the to-be-transported object according to the target booking information.
5. The method of claim 1, wherein the monitoring the recommended booking information comprises:
And monitoring a confirmation operation or an adjustment operation of the recommended booking information.
6. The method of booking a cargo flight according to claim 1, wherein the booking recommendation model is trained by:
Acquiring historical training data, wherein the historical training data comprises article characteristic information of historical transportation articles, flight characteristic information of historical candidate flights and actual booking information of the historical transportation articles;
And training the preset recommendation model by taking the article characteristic information of the historical transportation articles and the flight characteristic information of the historical candidate flights as training inputs of the preset recommendation model and guiding the training output of the preset recommendation model by taking the actual booking information of the historical transportation articles, thereby obtaining the booking recommendation model.
7. The method of booking a cargo flight of claim 6, further comprising:
Updating the historical training data according to the item characteristic information of the item to be transported, the flight characteristic information of the current candidate flight and the target booking information of the item to be transported to obtain target training data;
And updating and training the cabin booking recommendation model by utilizing the target training data.
8. A cargo flight booking apparatus, comprising:
the acquisition module is used for acquiring the article characteristic information of the article to be transported and the flight characteristic information of the current candidate flight;
The recommendation module is used for inputting the article characteristic information of the article to be transported and the flight characteristic information of the current candidate flight into a booking recommendation model so as to generate recommendation booking information for the article to be transported by utilizing the booking recommendation model;
The display module is used for displaying the recommended booking information and monitoring the operation of the recommended booking information;
And the booking module is used for generating target booking information for the to-be-transported object according to the operation of recommending booking information, and booking the to-be-transported object according to the target booking information.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of booking a cargo flight as claimed in any one of claims 1 to 7 when the program is executed by the processor.
10. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements a method of booking a cargo flight as claimed in any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310531259.8A CN118941175A (en) | 2023-05-11 | 2023-05-11 | Cargo flight booking method, device, electronic device and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310531259.8A CN118941175A (en) | 2023-05-11 | 2023-05-11 | Cargo flight booking method, device, electronic device and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN118941175A true CN118941175A (en) | 2024-11-12 |
Family
ID=93355169
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310531259.8A Pending CN118941175A (en) | 2023-05-11 | 2023-05-11 | Cargo flight booking method, device, electronic device and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118941175A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119204926A (en) * | 2024-11-26 | 2024-12-27 | 贵州空港智能科技有限公司 | An intelligent information release and management system for air cargo terminal inbound and outbound business |
-
2023
- 2023-05-11 CN CN202310531259.8A patent/CN118941175A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119204926A (en) * | 2024-11-26 | 2024-12-27 | 贵州空港智能科技有限公司 | An intelligent information release and management system for air cargo terminal inbound and outbound business |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20150120600A1 (en) | Time and location based delivery optimization | |
| WO2017048697A1 (en) | Method and apparatus for processing transmission information | |
| CN110135815B (en) | Travel order monitoring method and device, computer equipment and storage medium | |
| US20200057982A1 (en) | System and method for fuel storage tank inventory management | |
| Zhou et al. | A data-driven business intelligence system for large-scale semi-automated logistics facilities | |
| CN111192090A (en) | Seat allocation method and device for flight, storage medium and electronic equipment | |
| US11810061B2 (en) | Pre-trip inspection prediction and PTI reduction systems, processes and methods of use | |
| US12482049B2 (en) | System and method for dynamically generating collection routes based on predicted accumulation of industrial waste and recyclables | |
| Erdoğan et al. | Solving a large-scale crew pairing problem | |
| Meisel et al. | The design of Make-to-Order supply networks under uncertainties using simulation and optimisation | |
| Jiang et al. | A scheme for determining vehicle routes based on Arc-based service network design | |
| US20200219049A1 (en) | Delivery load management method and system | |
| US20240338619A1 (en) | Automatically scheduling and route planning for service providers | |
| CN117453994A (en) | Information push methods, devices, equipment and storage media | |
| CN118941175A (en) | Cargo flight booking method, device, electronic device and storage medium | |
| Yan et al. | A customer selection and vehicle scheduling model for moving companies | |
| CN113537853A (en) | Order distribution method, order distribution device, readable storage medium and electronic equipment | |
| CN113393171B (en) | Car rental scheduling method, system, device and storage medium based on inventory compensation | |
| CN109345341A (en) | Commodity screening technique, device, medium and electronic equipment | |
| Wasesa et al. | Relocating a multiple-tenants logistics center: Lesson learned from an air cargo terminal relocation project | |
| CN118780510A (en) | Vehicle dispatching method and device | |
| CN115375237A (en) | Cold-chain logistics intelligent management method, system, equipment and storage medium | |
| US20250190930A1 (en) | Systems and methods for load recommendation using profitability scores in a freight management platform | |
| Heinrich | Hand luggage overflow prediction at KLM | |
| CN112308344A (en) | A method, device and electronic device for predicting reservation value of non-departing flights |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |