CN116383592A

CN116383592A - Real-time computing and analyzing system and method based on Amazon finance

Info

Publication number: CN116383592A
Application number: CN202310306862.6A
Authority: CN
Inventors: 张能
Original assignee: Shenzhen Lingxing Network Technology Co ltd
Current assignee: Shenzhen Lingxing Network Technology Co ltd
Priority date: 2023-03-27
Filing date: 2023-03-27
Publication date: 2023-07-04

Abstract

The application relates to the technical field of data processing, and particularly discloses a real-time computing and analyzing system and method based on Amazon finance, wherein the system comprises the following components: the data acquisition module is used for acquiring Amazon bill data in real time; the settlement center is used for generating real-time profit data according to the collected bill data and updating the data according to the bill data according to preset time frequency; the profit statistics module is used for acquiring profit data in real time, and carrying out multidimensional calculation statistics and analysis according to the profit data so as to generate corresponding data sets; the message queue module is used for sending a message to inform the profit statistics module to update the data after the data of the clearing house is updated; the data storage module is used for storing the bill data and the corresponding data set in a preset database. The bill data is obtained in real time, the instant platform profit statistics is realized according to the bill data, and simultaneously, the profit data is subjected to multi-dimensional accounting and analysis, so that financial checking and decision making are facilitated.

Description

Real-time computing and analyzing system and method based on Amazon finance

Technical Field

The application relates to the field of data processing, in particular to a real-time computing and analyzing system and method based on Amazon finance.

Background

The period, links, chains and the like related to the cross-border electronic commerce are far more complex than those of the domestic electronic commerce, and the management and control of funds, goods and personnel are challenged. Where profit accounting is directly related to revenue, it is of particular importance. In the whole financial system, the cross-border electronic commerce involves a lot of and miscellaneous expenses, is dispersed in each report, and needs to calculate clear profit conditions, and the cost is shared to specific products, so that a large amount of manpower is needed to be input, and the unclear data calculation can directly influence the fund flow, even slow down the production efficiency of enterprises. The whole Amazon financial computing system is used for realizing accurate profit accounting and providing data support for the whole transaction flow.

Traditional financial systems are mainly used for acquiring and secondarily processing amazon store settlement data, and the data source is consistent with transactions by relying on amazon financial API. The captured data is analyzed and calculated, the profit situation can be checked according to the day, the time range can be freely selected, and the profit situation of a certain commodity can be checked in a certain period.

Although this set of procedures is relatively easy to implement, it has some drawbacks. For example, because of the limitation of amazon data sources, the overall dependence on settlement reports has a delay in data calculation, and some data which is not shared in commodity dimensions, such as adjustment fees, FBA warehouse service fees, and the like, are inconsistent in data viewed in each dimension, and are easy to misunderstand.

Disclosure of Invention

The purpose of the application is to provide a real-time computing and analyzing system and method based on Amazon finance, so as to obtain the finance data of sellers in real time and conduct more accurate profit accounting according to the finance data.

In a first aspect, the present application provides a real-time computing and analysis system based on amazon finances, comprising:

the data acquisition module is used for acquiring Amazon bill data in real time through the SP-API interface;

the settlement center is used for integrating and controlling the collected bill data to generate real-time profit data, and updating the data according to the real-time bill data according to preset time frequency;

the profit statistics module is used for immediately acquiring profit data of the settlement center and carrying out multidimensional calculation statistics and analysis according to the real-time profit data so as to generate corresponding data sets;

the message queue module is used for notifying a settlement center to carry out data accounting after bill data are acquired, and sending a message to notify the profit statistics module to carry out data updating after the data updating of the settlement center is completed;

and the data storage module is used for storing the bill data and the corresponding data set in a preset database.

Through the technical scheme, the settlement center and the profit statistics module are added on the existing financial system, amazon bill data can be obtained in real time, profit statistics is carried out in real time through the settlement center according to the latest bill data, so that the profit updating and checking of the hour-level instant platform are realized, and in addition, more accurate profit accounting and effective risk assessment can be realized through multidimensional statistics and analysis on the profit data.

Optionally, the bill data comprises a refund data and a billing month bill, the clearing house comprises a checking module, a bill inquiring module, a checking module, a data analyzing module and a data updating module,

the accounting module is used for accounting according to the bill data to generate corresponding profit data;

the bill inquiry module is used for inquiring the amazon bill which is not returned;

the account checking module is used for checking a summary month bill;

the data analysis module is used for carrying out trend analysis on the refund data and effectively evaluating the fund risk;

the data processing module is used for updating the profit data in real time and storing the updated profit data in a preset database.

Optionally, the data processing module comprises a message triggering unit, a message transmission unit, a data updating unit and a data storage unit,

the message triggering unit is used for monitoring the change setting of the bill data, and when the bill data is changed, the data updating module is informed to count the latest profit and update the data;

the message transmission unit is used for notifying the profit statistics module to perform corresponding data processing after generating profit data each time;

the data updating unit is used for updating profit data according to newly acquired bill data;

the data storage unit is used for storing the bill data and the profit data in a preset database.

Optionally, the bill data comprises a Summary form order, the profit statistics module comprises an instant data acquisition unit, a multidimensional accounting unit, a data synchronization unit,

the instant data acquisition unit is used for receiving the message of the settlement center and acquiring corresponding profit data in real time;

the multidimensional accounting unit is used for respectively carrying out data accounting from commodity dimensions and store dimensions so as to support viewing profit data according to dates from commodity dimensions and store dimensions and quickly grasp the profit situation of the day;

the data synchronization unit is used for carrying out profit statistics of the data dimension according to the Summary table order.

Optionally, the billing data further includes European station fees and advertising fees, the profit statistics module further includes an automatic apportionment unit and a custom apportionment unit,

the automatic allocation unit is used for carrying out differentiated accounting on the European station fees according to each station of the European station and automatically allocating the advertising fees to the commodities;

the custom allocation unit is used for carrying out custom allocation on indexes which are not allocated to commodity dimensions, and aggregating all dimension data upwards from MSKU dimensions so as to keep the same total cost count of all dimensions consistent.

Optionally, the message queue module comprises a data receiving module and a message transmission module,

the data receiving module is used for receiving the message notification from the corresponding module and storing the message notification into a preset message queue;

the data transmission module is used for extracting message notification from a preset message queue and carrying out corresponding operation processing according to the message notification.

In a second aspect, the present application provides a real-time computing and analyzing method based on amazon finance, comprising the steps of:

and acquiring the required Amazon bill data in real time through a data acquisition module.

Accounting is performed through the clearing house based on amazon billing data to obtain profit data.

And storing the bill data and the corresponding profit data in a preset database, and sending a notification message to the profit statistics module.

When a notification message of a clearing house is received, the profit statistics module performs multidimensional statistics and accounting based on the bill data and the profit data to obtain corresponding data sets.

And storing the corresponding data set in a preset database.

Optionally, the profit statistics module performs multidimensional statistics and accounting based on the bill data and the profit data to obtain corresponding data sets, and then includes:

acquiring product replenishment data under different dimensions according to the corresponding data sets;

and summarizing the MSKU dimension according to the product replenishment data in different dimensions, and generating a corresponding purchase order and a delivery plan.

Optionally, after storing the corresponding data set in a preset database, the method includes:

corresponding data encoding is carried out on the corresponding data set through a preset method so as to obtain serialized data;

based on the serialized data, carrying out data analysis and prediction through a preset data model, and obtaining a data analysis result;

performing risk assessment according to the data analysis result, and acquiring a corresponding risk assessment index;

judging whether the risk assessment index is in a preset threshold range or not;

if yes, outputting first prompt information, wherein the first prompt information indicates that the current scheme is in an expected state and can be continuously used;

if not, outputting second prompt information, wherein the second prompt information indicates that the risk index of the current scheme is higher, and corresponding scheme adjustment is suggested.

In a third aspect, the present application provides a computer readable storage medium storing a computer program capable of being loaded by a processor and executing one of the above-described amazon-based real-time computing and analysis systems.

In summary, the present application interfaces with amazon financial data through the SP-API interface first, and may obtain the required billing data in real time, and then update and manage the billing data through the clearing house. In addition, the European station fees are subjected to station distinction through profit statistics, all dimension data are aggregated upwards from MSKU dimensions, the same total cost count of all dimensions is kept consistent, and the profit accounting accuracy is improved. In addition, through an event-driven data processing mode, the accounting center and profit statistics are associated in a message queue mode, so that the bill data can be updated in real time, multi-latitude accounting and statistics on the profit data can be carried out in real time in a distributed mode, the high-efficiency circulation of the data is improved, the instantaneity of the data is guaranteed, and the accuracy of the data is guaranteed.

Drawings

FIG. 1 is a schematic diagram of a real-time computing and analysis system based on Amazon finance provided in an embodiment of the present application;

FIG. 2 is a schematic diagram of a clearing house provided in an embodiment of the present application;

FIG. 3 is a schematic diagram of a profit statistics module provided by an embodiment of the present application;

FIG. 4 is a flow chart of a real-time computing and analysis method based on Amazon finance provided in an embodiment of the present application;

FIG. 5 is a flow chart of analytical evaluation based on historical data provided by an embodiment of the present application.

Detailed Description

The present application is described in further detail below with reference to fig. 1-5.

The application provides a real-time computing and analyzing system based on Amazon finance, which is shown in FIG. 1 and comprises a data acquisition module 10, a settlement center 11, a profit statistics module 12, a message queue module 13 and a data storage module 14.

The data acquisition module 10 is configured to acquire amazon bill data in real time through an SP-API interface.

The clearing house 11 is used for integrating and controlling the collected bill data to generate real-time profit data, and updating the data according to the real-time bill data according to a preset time frequency.

The profit statistics module 12 is used for obtaining profit data of the clearing house in real time and carrying out multidimensional calculation statistics and analysis according to the real-time profit data so as to generate corresponding data sets.

The message queue module 13 is used for notifying the clearing house to perform data accounting after collecting bill data, and sending a message to notify the profit statistics module to perform data updating after the clearing house data updating is completed.

The data storage module 14 is configured to store billing data and corresponding data sets in a preset database.

In the embodiment of the present application, the data acquisition module 10 is configured to acquire amazon bill data in real time through the SP-API interface.

The SP-API is a REST-based API that helps Amazon sales partners programmatically access their order, shipping, payment, etc. Applications using the SP-API may increase sales efficiency, reduce labor requirements, and shorten response time to customers, thereby helping sales partners develop business. REST (Representational State Transfer, also known as stateful transport) is a web software architecture style, in which the purpose is to facilitate the transfer of messages between different software or programs across a network.

The bill data is amazon bill data, and is specifically all financial data recorded by the amazon clearing house, and relates to various financial reports.

Because the traditional financial system mainly acquires and secondarily processes the amazon store settlement data, the data source is relatively dependent on the amazon financial API (which belongs to an open type general financial interface) and is consistent with transactions, and because the amazon data is numerous and miscellaneous, the user quantity is extremely large, so that the amazon store user wants to pull the required bill data from the amazon background to carry out financial accounting, and the time required for acquiring the data is long, which is unfavorable for real-time inventory and analysis of the data.

Therefore, the data acquisition module 10 is used for opening a data interface with Amazon to synchronize Amazon bill data, automatically extract the latest data in Amazon reports such as Amazon clearing house and Summary, and integrate with the local latest data such as purchasing, warehousing and logistics. In this way, the data acquisition module 10 may request the financial data of the seller in real time through the provided data interface, that is, the amazon shop user may directly acquire the related data through the provided data interface, without pulling the data through the amazon development background.

Therefore, in the embodiment of the application, the data acquisition module 10 can enable the amazon shop user to directly throw away the amazon background operation and acquire the real-time required bill data in real time.

In this embodiment of the present application, the clearing house 11 is configured to integrate and manage the collected billing data, so as to generate real-time profit data, and update data according to the real-time billing data according to a preset time frequency.

The profit data represents profit data obtained by integrating and statistically accounting according to the bill data.

Since the bill data is all financial data recorded by the Amazon background, and relates to various financial reports, when the Amazon store user obtains the required financial data, profit accounting is required or specific data analysis is performed on sales conditions of all products, so as to be used as a reference for subsequent planning. At this time, corresponding processing is necessarily performed on the bill data, and data statistics and counting are performed according to the required indexes. The amount of work required is also not trivial when the amount of data is relatively large.

Therefore, in the embodiment of the present application, besides providing a corresponding data interface so that a user can obtain required bill data in real time, the accounting center 11 can also perform corresponding integration on the bill data to generate profit data, and when new bill data is obtained, corresponding profit accounting can also be directly performed, so that updating and viewing of the instant platform profit data are realized.

In addition, in addition to profit accounting, data can be correspondingly rearranged so as to help users to extract effective information more quickly and analyze the data.

Thus, in the embodiment of the present application, referring to fig. 2, the clearing house 11 specifically includes an accounting module 110, a bill inquiry module 111, a reconciliation module 112, a data analysis module 113, and a data processing module 114.

Wherein, the accounting module 110 is used for accounting according to the bill data to generate corresponding profit data.

The bill query module 111 is configured to query the amazon bill that is not returned.

The reconciliation module 112 is used to reconcile the summary month bill.

The data analysis module 113 is used for performing trend analysis on the refund data, and effectively evaluating the fund risk.

The data processing module 114 is configured to update profit data in real time, and store the updated profit data in a preset database.

Wherein, amazon returns record has three parts: the sales details include product name, sales quantity, sales price, product period, invoice number, buyer contact, payer name, refund account number, etc.; the credit account includes the registrant's name, account number, email address, credit card number, payer's name, refund account number, payer's address, etc.; the account information includes account name, login password, payer name, refund account number, payer address, and the like. Amazon rebate records are records of all sales and transactions processed by amazon from which it can be seen that amazon payouts and payouts, and amazon's various actions on sellers and buyers.

In the embodiment of the present application, after the bill data is obtained, profit accounting is performed on the bill data by the accounting module 110 to obtain specific profit data.

In addition, the bill data which is not returned can be obtained in advance from the obtained bill data, refund order marking is carried out from the corresponding order list, and the settlement, refund and account checking states of each order can be marked in real time, so that the refund monitoring of order level is realized, and the balance condition of receivables is controlled in real time.

Due to the fact that data acquisition is delayed, the reconciliation time of financial staff is correspondingly delayed, and due to the fact that data are too much and chaotic, the work efficiency of reconciliation is greatly reduced.

Therefore, in the embodiment of the present application, the clearing house 11 not only can acquire the bill data and generate the real-time profit data, but also provides a checking function for checking the Summary monthly bill, and the checking module 112 is used for implementing the checking of the API data and the Summary data, so that when the difference occurs between the two end data, the disclosure is automatically highlighted, thereby helping the financial staff to quickly locate the problem and reducing the financial checking difficulty. When all the account checking items have no difference, the data can be stored, the data of the next month after the data are stored can not be updated, and the account checking state can be returned to the settlement summary and the transaction detail.

In the embodiment of the present application, a data analysis module 113 is further provided for performing trend analysis on the refund data, so as to effectively evaluate the fund risk. For example, taking a month as a period, checking can be performed by aiming at the change of the duty ratio of the monthly return data, and even data modeling can be performed by adopting a large data platform, so that the subsequent return data is correspondingly predicted and analyzed, and further, the effective evaluation of the fund risk is realized.

In addition, corresponding data processing is performed between the acquisition of the latest profit data, such as saving the latest profit data, and corresponding statistics and analysis are performed based on the latest profit data by the profit statistics module 12. Thus, in the embodiment of the present application, a data processing module 114 is also included.

Specifically, the data processing module 114 includes a message triggering unit 1140, a message transmitting unit 1141, a data updating unit 1142, and a data storing unit 1143.

The message triggering unit 1140 is used for monitoring the change setting of the bill data, and when the bill data changes, notifying the data updating module to count the latest profit and update the data.

The message transmission unit 1141 is configured to notify the profit statistics module to perform corresponding data processing after generating profit data each time.

The data updating unit 1142 is configured to update profit data according to newly acquired billing data.

The data storage unit 1143 is configured to store billing data and profit data in a preset database.

In this embodiment of the present application, an event-driven data processing mode is adopted, and data completing processing is performed through a message queue, that is, after the data acquisition module 10 acquires the latest bill data, a corresponding message notification is generated and stored in a preset message queue, and then the message notification is extracted from the preset message queue without affecting the operation of the accounting center 11 itself, and then the latest bill data is acquired.

Therefore, the data processing module 114 is further provided with a corresponding message triggering unit 1140, that is, monitor the change setting of the bill data, and when the bill data changes, the data updating unit 1142 updates the profit data according to the latest bill data.

In addition, since the profit data needs to be counted and analyzed accordingly after the latest profit data is generated, the profit statistics module 12 is informed to perform corresponding data processing by the message transmission unit 1141 after the profit data is generated.

In addition, the newly acquired bill data and the profit data corresponding to the bill data are stored in a preset database through the data storage unit 1143, so that the data checking can be conveniently performed at any time later, and the data analysis and prediction can be performed through big data modeling.

In the embodiment of the present application, the profit statistics module 12 is configured to obtain the profit data generated by the clearing house 11 in real time, and perform multidimensional calculation statistics and analysis according to the real-time profit data to generate a corresponding data set.

Since amazon merchants want to acquire profit data in real time, they may need to aggregate the billing data from multiple dimensions to generate various reports in a targeted manner for corresponding data analysis.

In the embodiment of the present application, referring to fig. 3, the profit statistics module 12 specifically includes an immediate data acquisition unit 120, a multidimensional accounting unit 121, and a data synchronization unit 122.

The instant data obtaining unit 120 is configured to receive a message from a clearing house, and obtain corresponding profit data in real time.

The multidimensional accounting unit 121 is used for performing data accounting from the commodity dimension and the store dimension respectively, so as to support viewing profit data from the commodity dimension and the store dimension according to the date and quickly grasp the profit situation of the day.

The data synchronization unit 122 is used for profit statistics of data dimension according to the Summary table order.

In the embodiment of the present application, after the accounting center 11 generates profit data from billing data, a message is sent to inform the profit statistics module 12, and then the profit statistics module 12 performs multidimensional statistics and analysis on the profit data.

Therefore, the notification message transmitted from the clearing house 11 is received through the data unit 120, and the latest profit data is acquired. The profit data is then multi-dimensionally counted by the multi-dimensional accounting unit 121. The commodity dimension and the store dimension can be classified from the large direction, and the store dimension is counted from the perspective of the whole store, for example, the total commodity sales, refund amount, reimbursement amount, advertising fee, renting fee, service fee, pure profit and the like in the current time interval.

The commodity dimension is sales detailed data of a certain class of commodity or a specific commodity, and the commodity dimension can be subdivided into ASIN and father ASIN. The ASIN here refers to an ASIN code produced by the Amazon system by adding the commodity to the merchant, which corresponds to the coded identification of the commodity. By parent ASIN is meant a concept of a shop, such as a shirt, which represents a class of goods and cannot represent a specific product, whereas ASIN is understood as a child ASIN, i.e. a specific product of the class of goods, such as an orange L-code shirt. So the parent ASIN is not directly available for purchase, and the main function of the parent ASIN is to categorize all child ASIN into the same item detail page, which is convenient for the user to browse and for the background management. The sub-ASIN is a real commodity and can be purchased by a user.

Therefore, when profit is calculated, according to the dimension of the commodity, the dimension of ASIN and the dimension of father ASIN can be respectively counted and calculated to calculate profit, so that the specific selling details of the commodity can be more clearly known, and the corresponding data analysis is convenient.

In addition, since the bill data includes the Summary table order, statistics of the multidimensional data is realized by the data synchronization unit 122 based on the Summary table order, so that all header items in the profit statistics uniquely correspond to each order of the background Summary table, and the seller can confirm the authenticity and validity of the data without going to the background for repeated comparison.

When the profit is calculated according to the commodity dimension, the fees such as advertising fees, renting fees and the like need to be shared so as to calculate the profit of the specific commodity. However, if there is cost data that cannot be shared to commodity dimensions, the data that is viewed in each dimension is inconsistent, resulting in deviation in accounting for profit data.

Thus, in the present embodiment, referring to FIG. 3, profit statistics module 12 also includes an automatic apportionment unit 123 and a custom apportionment unit 124.

Wherein the automatic allocation unit 123 is used for performing a differentiated accounting for the European station fees according to the respective stations of the European station, and automatically allocating the advertising fees to the goods.

The custom allocation unit 124 is configured to perform custom allocation on the index that is not allocated to the commodity dimension, and aggregate all dimension data upward from the MSKU dimension, so as to keep the same total cost count of all dimensions consistent.

In the embodiment of the application, since the profit data relates to a relatively large number of items, especially for sellers at the european stations, different stations have different fees, and when the commodity dimension is allocated, the fees of the european stations need to be summarized and allocated, which is not beneficial to accounting the profit data of the stations besides consuming manpower.

Thus, the automatic allocation unit 123 is configured to distinguish the european station fees by the individual sites of the european station, and then, when accounting for commodity dimensions is performed, the corresponding european station fees are displayed, and then, the distinction accounting is performed with the fees of the corresponding sites. In addition, the advertising fees are automatically distributed on the commodities, for example, corresponding advertising fees are automatically distributed according to sales of the commodities.

Since consideration of data not allocated to commodity dimensions may make the total of costs for each dimension non-uniform, for example, if the costs of overseas rentals are not allocated to commodity dimensions, the rentals in the commodity dimensions are not consistent with the rentals in the store dimensions.

Therefore, all dimensions in the profit statistics are aggregated upward by the custom apportionment unit 124, where MSKU is a vendor stock keeping unit, i.e., a way for the vendor to manage the stock, and the vendor adds an effective and personalized identifier, i.e., an MSKU tag, to each item in the stock. The MSKU and ASIN are in a one-to-one relationship, and by establishing this relationship, the seller is better able to manage its own inventory.

The method has the advantages that each cost can be allocated and calculated to the MSKU dimension through the custom allocation rule, so that the data counted according to the MSKU dimension is similar to the statistics according to the shop dimension, such as platform income, platform expenditure (cost to be allocated), profit and the like, in addition, the MSKU corresponds to the ASIN one by one, the statistic data can be easily converted into the data calculated according to the commodity dimension, the total cost count of the same dimension is kept consistent, and sellers can easily compare the statistic data of different dimensions.

In this embodiment of the present application, the message queue module 13 is configured to notify the clearing house to perform data accounting after collecting the bill data, and send a message to notify the profit statistics module to perform data updating after the clearing house data updating is completed.

Since event-driven processing models are employed so that the modules can still cooperate with each other while being partitioned from each other, message delivery and message response driving can be performed in the form of message queues. The reception and transmission of messages will be effected by the message queue module 13.

Specifically, the message queue module 13 includes a data receiving module 131 and a message transmitting module 132.

The data receiving module 131 is configured to receive a message notification from a corresponding module, and store the message notification in a preset message queue.

The data transmission module 132 is configured to extract a message notification from a preset message queue, and perform corresponding operation processing according to the message notification.

In this embodiment of the present application, after the data acquisition module 10 acquires the required bill data, the accounting center needs to make profit statistics according to the bill data, so the data acquisition module 10 will be used as a data producer to produce corresponding notification messages, at this time, the data receiving module 131 will receive the notification messages and write the notification messages into the message queue, and then the settlement center 11 will be used as a data consumer to extract the notification messages from the message queue when necessary, and then start to complete its own work. That is, the data receiving module 132 will extract notification messages from the message queue according to the demand state of the clearing house 11 and feed back to the clearing house 11.

Similarly, after the accounting center 11 completes accounting of the profit data, the profit statistics module 12 writes corresponding notification messages into the message queue, and the profit statistics module 12 obtains the notification messages through the message queue, and starts multidimensional accounting and accounting according to the billing data and the profit data.

In the embodiment of the present application, the data storage module 14 is configured to store the billing data and the corresponding data set in a preset database.

Every time the latest bill data is obtained, corresponding accounting is carried out according to the latest bill data, profit data and various analysis data are updated, and at the moment, the updated data and the latest bill data are stored into a preset database through the data storage module 14, so that the store merchant can conveniently and randomly check and count the data.

The embodiment of the application also provides a real-time computing and analyzing method based on Amazon finance, which is shown in FIG. 4 and specifically comprises the following steps:

s100, acquiring required Amazon bill data in real time through a data acquisition module.

S200, based on Amazon bill data, accounting is carried out through a settlement center to obtain profit data.

And S300, storing the bill data and the corresponding profit data in a preset database, and sending a notification message to the profit statistics module.

And S400, when receiving the notification message of the settlement center, the profit statistics module performs multidimensional statistics and accounting based on the bill data and the profit data so as to acquire a corresponding data set.

S500, storing the corresponding data set in a preset database.

In the embodiment of the application, the data acquisition module 10 can open a channel with Amazon background data, so that the Amazon background data and Amazon bill data are synchronized, and a user of an Amazon shop can acquire required bill data in real time.

After the billing data is obtained, in order to facilitate the amazon store user to better perform profit accounting and multidimensional data statistics and analysis on the billing data, the accounting center 11 also performs profit accounting in real time according to the latest billing data to generate the latest profit data.

The latest profit data and billing data are then counted by the profit statistics module 12 according to the store dimension, the commodity dimension and the merchant user-defined dimension, respectively. And generates corresponding data sets, namely corresponding data reports and the like. And finally, storing the corresponding data set in a preset database so as to facilitate subsequent inventory and data analysis.

Since the multi-dimensional data statistics set generated by the profit statistics module 12 can reflect profit of a certain time interval and also has sales details of corresponding commodities, corresponding replenishment processing can be performed according to the sales data of the commodities.

Therefore, after the profit statistics module performs multidimensional statistics and accounting based on the billing data and profit data to obtain corresponding data sets, the method further comprises the steps of:

s410, acquiring product replenishment data under different dimensions according to the corresponding data sets.

And S420, summarizing the MSKU dimension according to the product replenishment data in different dimensions and generating a corresponding purchase order and a delivery plan.

In this embodiment of the present application, the data set generated by the profit statistics module 12 may obtain sales conditions of products in a plurality of different dimensions, and then, by combining with a preset sales plan, specific data of the need for replenishment may be known.

And then, according to the replenishment data of the product, a corresponding purchase order can be produced, and according to the circulation condition of the product, a corresponding product delivery plan can be generated. Therefore, for Amazon store users, the selling condition and the profit of each commodity can be clearly known through data statistics under different dimensions, and the user can conveniently and timely adjust the replenishment data, the purchasing plan and the delivery plan in combination with the expected planning.

In addition, since the latest bill data is obtained every time with updating of profit data and multidimensional data statistics sets, updated data is written into a preset database, and when the data amount in the database reaches a certain degree, analysis and modeling of the data can be performed based on big data, so that users can be better helped to perform data prediction and risk assessment, and thus management schemes and strategies can be adjusted.

Thus, after storing the corresponding data sets in the preset database, see fig. 5, the method further comprises the steps of:

s610, carrying out data coding on the corresponding data set by a preset method so as to obtain the serialized data.

S620, based on the serialized data, carrying out data analysis and prediction through a preset data model, and obtaining a data analysis result.

And S630, performing risk assessment according to the data analysis result, and acquiring a corresponding risk assessment index.

S640, judging whether the risk assessment index is in a preset threshold range.

And S650, if so, outputting first prompt information, wherein the first prompt information represents that the current scheme is in an expected state and can be continuously used.

And S660, if not, outputting second prompt information, wherein the second prompt information indicates that the risk index of the current scheme is higher, and corresponding scheme adjustment is suggested.

In the embodiment of the application, the multi-dimensional data statistics set generated by the profit statistics module can be extracted from a preset database, based on the data sets, the data can be classified accordingly, and a series of data analysis indexes, such as data analysis and prediction for a certain type of commodity, the ratio of the whole commodity category to all commodities in the next quarter, and the like, are formulated.

For the corresponding data analysis index, the relevant data can be summarized and encoded, for example, transform is used for encoding, so as to generate serialized data, then training is carried out through a preset algorithm or analysis and prediction are carried out through a trained data model, corresponding data analysis and prediction results can be obtained, and according to the corresponding analysis and prediction results, a corresponding risk assessment index can be generated.

The specific planning and strategy layout of the subsequent scheme can be determined according to the risk assessment index, if the risk assessment index is within the preset threshold range, the current risk index is within the expected range, namely, the current scheme is in an expected state and can be continuously used, and therefore the first prompt information can be output. If the risk assessment index is not in the preset threshold range, namely exceeds the expected risk index, namely the risk index of the current scheme is higher, the corresponding scheme adjustment is suggested, and at the moment, a second prompt message is output.

Embodiments of the present application also provide a computer readable storage medium storing a computer program capable of being loaded by a processor and executing any one of the above-described amazon-based real-time computing and analysis systems.

The embodiments of the present invention are all preferred embodiments of the present application, and are not intended to limit the scope of the present application in this way, therefore: all equivalent changes according to the principles of this application should be covered by the protection scope of this application.

Claims

1. A real-time computing and analysis system based on amazon finances, comprising:

2. The Amazon finance-based real-time computing and analyzing system of claim 1, wherein the billing data comprises refund data and billing month, the billing center comprises a accounting module, a billing query module, a billing module, a data analysis module and a data update module,

the account checking module is used for checking a summary month bill;

the data updating module is used for updating the profit data in real time and storing the updated profit data in a preset database.

3. The amazon-based real-time computing and analysis system of claim 2, wherein the data processing module comprises a message triggering unit, a message transmission unit, a data updating unit, and a data storage unit,

4. The amazon finance-based real-time computing and analyzing system of claim 1, wherein the billing data comprises a Summary form order, the profit statistics module comprises an instant data acquisition unit, a multidimensional accounting unit, a data synchronization unit,

the multidimensional accounting unit is used for respectively carrying out data accounting from commodity dimensions and store dimensions so as to support viewing profit data from commodity dimensions and store dimensions according to dates;

5. The amazon-based real-time computing and analysis system of claim 4, wherein the billing data further comprises european station costs and advertising costs, the profit statistics module further comprises an automatic allocation unit and a custom allocation unit,

6. The amazon-based real-time computing and analysis system of claim 1, wherein the message queue module comprises a data receiving module and a message transmitting module,

the data receiving module is used for receiving message notifications from other modules and storing the message notifications into a preset message queue;

7. The real-time computing and analyzing method based on Amazon finance is characterized by comprising the following steps:

acquiring required Amazon bill data in real time through a data acquisition module;

accounting is carried out through a settlement center based on Amazon bill data so as to obtain profit data;

storing the bill data and the corresponding profit data in a preset database, and sending a notification message to a profit statistics module;

when receiving a notification message of a clearing house, the profit statistics module performs multidimensional statistics and accounting based on bill data and profit data to obtain a corresponding data set;

and storing the corresponding data set in a preset database.

8. The amazon-based real-time computing and analysis method of claim 7, wherein the profit statistics module performs multidimensional statistics and accounting based on the billing data and the profit data to obtain corresponding data sets, comprising:

9. The method of real-time amazon-based computing and analysis according to claim 7, wherein after storing the corresponding data sets in a predetermined database, comprising:

10. A computer readable storage medium storing a computer program capable of being loaded by a processor and executing a real-time amazon-based computing and analysis method according to any one of claims 7 to 9.