CN110991950A - Logistics service data processing method and device and storage medium - Google Patents
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Abstract
The invention discloses a method and a device for processing logistics service data and a storage medium, wherein the method comprises the following steps: calculating the air route logistics data in the collected logistics service basic database to obtain a user portrait, a commodity portrait and user behavior data; comprehensively analyzing user portrait, commodity portrait and user behavior data based on a pre-trained machine learning model, and dynamically adjusting airline logistics service ranking according to the comprehensive analysis result; and recommending the most suitable airline commodity for different users according to the arrangement name of the airline logistics service. By adopting the method and the system, the airline logistics data are statistically analyzed, the airline logistics service is comprehensively ranked by combining the machine learning model, and airline commodities with buying intentions are recommended to the user, so that the loyalty and the user experience of the user can be improved, the quality and the efficiency of shopping decisions of the user are improved, the ordering conversion rate is improved, and the stickiness of the user is enhanced.
Description
Technical Field
The present invention relates to the technical field of logistics services, and in particular, to a method and an apparatus for processing logistics service data, and a storage medium.
Background
The cross-border service industry develops roughly for decades, has obvious market characteristics and obvious industrial pain points, and mainly focuses on the following aspects:
(1) the service roles are multiple, and the service process is long. One-time cross-border service needs dozens of service providers and can be completed by more than ten service types, and particularly in cross-border international shipping service, global airlines and ship companies are various and have strong competition.
(2) The flow of goods and services is blind. The whole service process is relatively original, and at present, the service is almost carried out by adopting modes of telephone, mail, manual operation and the like, so that the low efficiency, the high error rate and the increased cost are caused.
(3) The price is frequently changed and inquired. The whole international freight rate changes every week or even within a shorter time period, so that the price inquiry cost and efficiency of a client are extremely low and tedious. The domestic shipping price inquiry has multi-level agents, such as shipowners, first-level ship generations, second-level ship generations, third-level ship generations and the like, and the information exchange is not timely and opaque. Market behavior between shipyards causes freight rates to fluctuate frequently. The freight rate is unstable due to a plurality of factors such as freight rate change caused by the change of the trade policy of the destination port country. The above reasons directly affect the complexity of the price inquiry action of the client and the uncertainty of the price.
(4) Upstream and downstream lack of reputation severely. The payment is not timely, the triangular debt is serious, the upstream and downstream liquidity is in shortage and cannot be accepted by financial institutions such as banks, and the like, and a transparent and controllable credit voucher does not exist.
Global trade is a worldwide trend and convenience of international trade is an overall trend. Cross-border and cross-border trading services are a huge incremental market from a global and long-term perspective. Meanwhile, the cross-border trade service has high cost and low efficiency due to the extensive development history of the cross-border trade service, backward service modes, the lack of overall high-level practitioners, the lack of technological innovation and the like. According to statistics, the proportion of the trade service cost in the trade amount reaches more than 15%, and the proportion is too high. Therefore, the industry needs to be improved and upgraded with scientific and technological strength.
The cross-border service industry is a traditional industry which is generated along with the cross-border trade industry, is a market which is just needed, has large market scale, and has more than 5 trillions in the Chinese market every year, wherein the weight of international shipping in the whole cross-border service accounts for 70 percent, thereby optimizing the logistics service efficiency, improving the service quality, reducing the cost and providing real valuable service for users.
With the continuous integration of technologies such as internet of things, AI technology, cloud computing and the like into our lives, accumulated data are continuously increased and accumulated in many fields such as internet, communication, finance, commerce and medical treatment, and for massive data in the trade service process, we need to analyze in real time and quickly feed back results, and also need to accurately extract valuable information needed by users hidden therein, and then convert the information obtained by mining into organized knowledge to express the information in modes such as models, so that the analysis models are applied to actual working scenes to improve service efficiency, optimize marketing schemes and the like.
Disclosure of Invention
Embodiments of the present invention provide a method and an apparatus for processing logistics service data, and a storage medium, which can improve user loyalty and user experience, improve quality and efficiency of user shopping decisions, improve ordering conversion rate, and enhance user stickiness.
A first aspect of an embodiment of the present invention provides a method for processing logistics service data, where the method includes:
calculating the air route logistics data in the collected logistics service basic database to obtain a user portrait, a commodity portrait and user behavior data;
comprehensively analyzing user portrait, commodity portrait and user behavior data based on a pre-trained machine learning model, and dynamically adjusting airline logistics service ranking according to the comprehensive analysis result;
and recommending the most suitable airline commodity for different users according to the arrangement name of the airline logistics service.
Further, the method further comprises:
acquiring purchasing behavior data of a user based on recommended airline commodities;
and updating the airline logistics data in the logistics service basic database based on the shopping behavior data.
Further, the method further comprises:
and when the price of the airline commodity corresponding to the target logistics service of the top N in the airline logistics service ranking is changed, sending the price change to the target user, wherein N is a positive integer greater than or equal to 1.
Further, the method further comprises:
and determining the user indicated by the user behavior data of the airline commodity corresponding to the concerned target logistics service as a target user.
Further, the method further comprises:
determining a target route concerned by the user based on the user behavior data;
and when the price of the airline commodity corresponding to the target airline changes, sending the price change to a user paying attention to the target airline.
A second aspect of the embodiments of the present invention provides a logistics service data processing apparatus, which may include:
the data analysis processing module is used for calculating the airline logistics data in the collected logistics service basic database to obtain a user portrait, a commodity portrait and user behavior data;
the service ranking adjusting module is used for comprehensively analyzing the user portrait, the commodity portrait and the user behavior data based on a pre-trained machine learning model and dynamically adjusting the airline logistics service ranking according to the comprehensive analysis result;
and the airline commodity recommending module is used for recommending the most suitable airline commodity for different users according to the logistics service ranking.
Further, the above apparatus further comprises:
the shopping data acquisition module is used for acquiring shopping behavior data of the user based on the recommended airline commodity;
and the data updating module is used for updating the airline logistics data in the logistics service basic database based on the shopping behavior data.
Further, the above apparatus further comprises:
and the information updating and recommending module is used for sending the price change to the target user when the price of the airline commodity corresponding to the target logistics service of the top N in the airline logistics service ranking is changed, wherein N is a positive integer greater than or equal to 1.
Further, the above apparatus further comprises:
and the target user determining module is used for determining the user indicated by the user behavior data of the airline commodity corresponding to the concerned target logistics service as the target user.
Further, the above apparatus further comprises:
the target route determining module is used for determining a target route concerned by the user based on the user behavior data;
and the information updating and recommending module is also used for sending the price change to the user concerning the target air route when the price of the air route commodity corresponding to the target air route is changed.
A third aspect of the embodiments of the present invention provides a computer device, which includes a processor and a memory, where the memory stores at least one instruction, at least one program, a code set, or a set of instructions, and the at least one instruction, the at least one program, the code set, or the set of instructions is loaded and executed by the processor to implement the logistics service data processing method according to the above aspect.
A fourth aspect of the embodiments of the present invention provides a computer storage medium, where at least one instruction, at least one program, a code set, or a set of instructions is stored in the computer storage medium, and the at least one instruction, the at least one program, the code set, or the set of instructions is loaded and executed by a processor to implement the logistics service data processing method according to the above aspect.
In the embodiment of the invention, the airline logistics data is statistically analyzed, the airline logistics service is comprehensively ranked by combining the machine learning model, and airline commodities with buying intentions are recommended to the user, so that the loyalty and the user experience of the user are improved, the quality and the efficiency of shopping decision of the user are improved, the ordering conversion rate is improved, and the user stickiness is enhanced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for processing logistics service data according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a logistics service data processing apparatus according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "including" and "having," and any variations thereof, in the description and claims of this invention and the above-described drawings are intended to cover a non-exclusive inclusion, and the terms "first" and "second" are used for distinguishing designations only and do not denote any order or magnitude of a number. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
It should be noted that the logistics service data processing method provided by the present application can be applied to a scenario of a logistics transportation service in a ship, an air or other manner.
In the embodiment of the invention, the logistics service data processing method can be applied to computer equipment, and the computer equipment can be a computer or a smart phone and can also be other electronic equipment with computing processing capacity.
As shown in fig. 1, the logistics service data processing method may include at least the following steps:
s101, calculating the collected airline logistics data in the logistics service basic database to obtain user portrait, commodity portrait and user behavior data.
It should be noted that the computer device may create a logistics service basic database for storing any data related to the airline logistics, i.e. airline logistics data, such as user data, airline data or commodity data collected by the device and involved in purchasing.
Further, the device may analyze data in the database to form a user representation, a merchandise representation, and user behavior data. The user representation may include enterprise nature of purchased airline goods, location, import and export goods category, year/month/week quantity, goods value, service price sensitivity, trade country/region, trade mode, settlement mode, etc. The merchandise representation may include a country of departure, a port country of destination, a port of destination, a voyage, a shift, a shipper, a box, a hot line, a moving line, general merchandise, hazardous items, and the like, including primarily airlines. The user behavior data may include user route of concern, route query, ordering, click rate, forwarding sharing, progress tracking, logistics tracking, evaluation, and the like.
S102, comprehensively analyzing the user portrait, the commodity portrait and the user behavior data based on the pre-trained machine learning model, and dynamically adjusting the airline logistics service ranking according to the comprehensive analysis result.
It will be appreciated that the un-ranked prior airline logistics services may be cluttered and airline goods of low concern may also be ranked ahead, not conducive to queries and recommendations for airline goods.
In a preferred implementation manner, the equipment may adopt the airline logistics data to train a machine learning model in advance, further, may comprehensively analyze the user portrait, the commodity portrait and the user behavior data based on the model, and dynamically adjust the airline logistics service ranking according to the result of the comprehensive analysis.
It will be appreciated that the ranking of airline logistics services can be a ranking of airline logistics from high to low in terms of quality of service, etc., after a combination of factors such as airline routing, logistics services, after-sales services, etc.
And S103, recommending the most suitable airline commodity for different users according to the airline logistics service arrangement name.
In specific implementation, the equipment can analyze commodities or airlines concerned by different users according to user figures or user behavior data, and then recommend airlines or airlines commodities which are related to the commodities or airlines concerned by the users and are ranked in front in the airline logistics service to the users, so that personalized push is realized, and the commodities recommended to the users can meet the purchase intention of the users most.
Optionally, if a plurality of logistics services corresponding to the ranks exist in the airline logistics of the airline concerned by a user among the logistics services ranked at the top, a plurality of logistics services can be recommended to the user at the same time, or one of the logistics services is selected or the most top logistics service is selected for recommendation. If an airline or commodity concerned by the user is not in the airline logistics service ranked top (for example, no commodity or airline logistics concerned by the user exists in the airline logistics service ranked top 10), the sequential detection is continued based on the ranking until an airline logistics service matched with the airline or airline commodity concerned is found, and then the airline commodity in the corresponding ranked airline logistics service is recommended to the user.
Further, the user can shop according to the airline commodity recommended by the equipment, and shop behavior data for the shop is generated. The equipment can collect the shopping behavior data and then expand or update the airline logistics data in the logistics service basic database based on the data so as to further carry out optimization training on the machine learning model and increase the accuracy of airline logistics service ranking.
In an optional embodiment, the device may monitor whether the price of the airline commodity corresponding to the top-ranked (for example, N is a positive integer greater than or equal to 1) target logistics service in the airline logistics service changes, and when the price changes, send the change of the price to the target user concerned with the airline logistics service. By maintaining airline aerial photographs of airline logistics service rankings, the overall quality of service for top-ranked airlines is guaranteed.
In an optional embodiment, the equipment can calculate a concerned target air route based on user behavior data of any user in a certain time period, further, the equipment can monitor whether the air route commodity price of the target air route changes, and when the price changes, the price changes can be automatically recommended to the user, so that data maintenance of air route logistics service concerned by any user is realized. Optionally, the device may further calculate a ranking of the target route concerned by the user, for example, the ranking of a certain route may be calculated according to route records, query times, concern times, order placement amount, forwarding sharing amount, which are queried by the user in a certain time period. And then judging whether the ranking of the route is close to the front, if so, maintaining the data of the route, otherwise, not processing the ranking, avoiding wasting system resources through selective data maintenance, and simultaneously ensuring that important customers do not lose.
In the embodiment of the invention, the airline logistics data is statistically analyzed, the airline logistics service is comprehensively ranked by combining the machine learning model, and airline commodities with buying intentions are recommended to the user, so that the loyalty and the user experience of the user are improved, the quality and the efficiency of shopping decision of the user are improved, the ordering conversion rate is improved, and the user stickiness is enhanced.
Hereinafter, a logistics service data processing apparatus according to an embodiment of the present invention will be described in detail with reference to fig. 2. It should be noted that, the data processing apparatus shown in fig. 2 is used for executing the method according to the embodiment of the present invention shown in fig. 1, for convenience of description, only the portion related to the embodiment of the present invention is shown, and details of the specific technology are not disclosed, please refer to the embodiment of the present invention shown in fig. 1.
Referring to fig. 2, a schematic structural diagram of a logistics service data processing apparatus according to an embodiment of the present invention is provided. As shown in fig. 2, the data processing apparatus 10 of the embodiment of the present invention may include: the system comprises a data analysis processing module 101, a service ranking adjusting module 102, an airline commodity recommending module 103, a shopping data acquiring module 104, a data updating module 105, an information updating recommending module 106, a target user determining module 107 and a target airline determining module 108.
And the data analysis processing module 101 is used for calculating the air route logistics data in the collected logistics service basic database to obtain a user portrait, a commodity portrait and user behavior data.
And the service ranking adjusting module 102 is used for comprehensively analyzing the user portrait, the commodity portrait and the user behavior data based on a pre-trained machine learning model and dynamically adjusting the airline logistics service ranking according to the comprehensive analysis result.
And the airline commodity recommending module 103 is used for recommending the most suitable airline commodity for different users according to the logistics service ranking.
In an optional embodiment, the shopping data acquiring module 104 is configured to acquire shopping behavior data that occurs based on the recommended airline commodity for the user.
And the data updating module 105 is used for updating the airline logistics data in the logistics service basic database based on the shopping behavior data.
In an optional embodiment, the information update recommending module 106 is configured to send the price change to the target user when the price of the airline commodity corresponding to the top-ranked N target logistics services in the airline logistics service ranking changes, where N is a positive integer greater than or equal to 1.
And the target user determining module 107 is used for determining the user indicated by the user behavior data of the airline commodity corresponding to the concerned target logistics service as the target user.
In an alternative embodiment, the target route determination module 108 is configured to determine a target route of interest to the user based on the user behavior data.
And the information updating and recommending module 106 is further configured to send the price change to a user who focuses on the target airline when the price of the airline commodity corresponding to the target airline changes.
It should be noted that, the execution process of each module in this embodiment may refer to the description in the foregoing method embodiment, and is not described herein again.
In the embodiment of the invention, the airline logistics data is statistically analyzed, the airline logistics service is comprehensively ranked by combining the machine learning model, and airline commodities with buying intentions are recommended to the user, so that the loyalty and the user experience of the user are improved, the quality and the efficiency of shopping decision of the user are improved, the ordering conversion rate is improved, and the user stickiness is enhanced.
An embodiment of the present invention further provides a computer storage medium, where the computer storage medium may store a plurality of instructions, where the instructions are suitable for being loaded by a processor and executing the method steps in the embodiment shown in fig. 1, and a specific execution process may refer to a specific description of the embodiment shown in fig. 1, which is not described herein again.
The embodiment of the application also provides computer equipment. As shown in fig. 3, the computer device 20 may include: the at least one processor 201, e.g., CPU, the at least one network interface 204, the user interface 203, the memory 205, the at least one communication bus 202, and optionally, a display 206. Wherein a communication bus 202 is used to enable the connection communication between these components. The user interface 203 may include a touch screen, a keyboard or a mouse, among others. The network interface 204 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), and a communication connection may be established with the server via the network interface 204. The memory 205 may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory, and the memory 205 includes a flash in the embodiment of the present invention. The memory 205 may optionally be at least one memory system located remotely from the processor 201. As shown in fig. 3, memory 205, which is a type of computer storage medium, may include an operating system, a network communication module, a user interface module, and program instructions.
It should be noted that the network interface 204 may be connected to a receiver, a transmitter or other communication module, and the other communication module may include, but is not limited to, a WiFi module, a bluetooth module, etc., and it is understood that the computer device in the embodiment of the present invention may also include a receiver, a transmitter, other communication module, etc.
calculating the air route logistics data in the collected logistics service basic database to obtain a user portrait, a commodity portrait and user behavior data;
comprehensively analyzing user portrait, commodity portrait and user behavior data based on a pre-trained machine learning model, and dynamically adjusting airline logistics service ranking according to the comprehensive analysis result;
and recommending the most suitable airline commodity for different users according to the arrangement name of the airline logistics service.
In some embodiments, apparatus 20 is further configured to:
acquiring purchasing behavior data of a user based on recommended airline commodities;
and updating the airline logistics data in the logistics service basic database based on the shopping behavior data.
In some embodiments, apparatus 20 is further configured to:
and when the price of the airline commodity corresponding to the target logistics service of the top N in the airline logistics service ranking is changed, sending the price change to the target user, wherein N is a positive integer greater than or equal to 1.
In some embodiments, apparatus 20 is further configured to:
and determining the user indicated by the user behavior data of the airline commodity corresponding to the concerned target logistics service as a target user.
In some embodiments, apparatus 20 is further configured to:
determining a target route concerned by the user based on the user behavior data;
and when the price of the airline commodity corresponding to the target airline changes, sending the price change to a user paying attention to the target airline.
In the embodiment of the invention, the airline logistics data is statistically analyzed, the airline logistics service is comprehensively ranked by combining the machine learning model, and airline commodities with buying intentions are recommended to the user, so that the loyalty and the user experience of the user are improved, the quality and the efficiency of shopping decision of the user are improved, the ordering conversion rate is improved, and the user stickiness is enhanced.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.
Claims (10)
1. A logistics service data processing method is characterized by comprising the following steps:
calculating the air route logistics data in the collected logistics service basic database to obtain a user portrait, a commodity portrait and user behavior data;
comprehensively analyzing the user portrait, the commodity portrait and the user behavior data based on a pre-trained machine learning model, and dynamically adjusting the airline logistics service ranking according to the result of the comprehensive analysis;
and recommending the most suitable airline commodity for different users according to the airline logistics service arrangement name.
2. The method of claim 1, further comprising:
acquiring purchasing behavior data of a user based on recommended airline commodities;
and updating the airline logistics data in the logistics service basic database based on the shopping behavior data.
3. The method of claim 1, further comprising:
and when the price of the airline commodity corresponding to the target logistics service of the top N in the airline logistics service ranking is changed, sending the price change to a target user, wherein N is a positive integer greater than or equal to 1.
4. The method of claim 3, further comprising:
and determining the user indicated by the user behavior data concerning the airline commodity corresponding to the target logistics service as a target user.
5. The method of claim 1, further comprising:
determining a target route of interest to the user based on the user behavior data;
and when the price of the airline commodity corresponding to the target airline changes, sending the price change to a user paying attention to the target airline.
6. A logistics service data processing apparatus, comprising:
the data analysis processing module is used for calculating the airline logistics data in the collected logistics service basic database to obtain a user portrait, a commodity portrait and user behavior data;
the service ranking adjusting module is used for comprehensively analyzing the user portrait, the commodity portrait and the user behavior data based on a pre-trained machine learning model and dynamically adjusting the airline logistics service ranking according to the result of the comprehensive analysis;
and the airline commodity recommending module is used for recommending the most suitable airline commodity for different users according to the logistics service arrangement name.
7. The apparatus of claim 6, further comprising:
the shopping data acquisition module is used for acquiring shopping behavior data of the user based on the recommended airline commodity;
and the data updating module is used for updating the airline logistics data in the logistics service basic database based on the shopping behavior data.
8. The apparatus of claim 6, further comprising:
and the information updating and recommending module is used for sending the price change to the target user when the price of the airline commodity corresponding to the target logistics service of the top N in the airline logistics service ranking is changed, wherein N is a positive integer greater than or equal to 1.
9. The apparatus of claim 8, further comprising:
and the target user determining module is used for determining the user which is concerned about the user behavior data indication of the airline commodity corresponding to the target logistics service as the target user.
10. A computer-readable storage medium, wherein at least one instruction, at least one program, a set of codes, or a set of instructions is stored in the storage medium, and the at least one instruction, the at least one program, the set of codes, or the set of instructions is loaded and executed by a processor to implement the logistics service data processing method of any one of claims 1 to 5.
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