CN116934430A - Information complement method and ERP system - Google Patents
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- G—PHYSICS
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- 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—Lists, e.g. purchase orders, compilation or processing
- G06Q30/0635—Processing of requisition or of purchase orders
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- 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
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
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- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
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Abstract
The application provides an information complement method and an ERP system, wherein the information complement method is used for a financial module of an e-commerce ERP system or an e-commerce platform system, and the information complement method comprises the following steps: acquiring data access rights of an e-commerce platform; acquiring and analyzing real-time user data generated by the e-commerce platform; acquiring and analyzing historical user data generated by the e-commerce platform; acquiring clearing adjustment order information in the historical user data and/or the real-time user data, and searching the clearing order information according to the clearing adjustment order information; calculating and generating supplementary record data according to the clearing order information; and merging the complement data and the real-time user data, and generating target user data. The information complement method and the ERP system have the advantages that missing information in the acquired data can be identified and complement, and therefore timely and accurate store financial information is provided for users.
Description
Technical Field
The application belongs to the technical field of computers, and particularly relates to an information complement method, a financial data matching method and ERP systems corresponding to the methods.
Background
With the rise of global electronic commerce, international retail trade is rapidly developed, a large number of domestic small and medium-sized electronic commerce sellers expand retail business to foreign markets, and a large number of domestic high-quality and low-cost commodities are sold to foreign markets through foreign electronic commerce platforms (such as Amazon, EBay, shopping interest Wish, dried small shrimps Shupe, lazada and the like). With the development of cross-border business, an e-commerce ERP system (which may be simply referred to as "e-commerce ERP", "ERP system" or "system") developed based on ERP software is gradually developed. The electronic commerce ERP system can be deeply connected with the electronic commerce platform, namely, the electronic commerce ERP system accesses and controls shops of the electronic commerce platform through established rules, processes dynamic data of each link of the operation of the shops, further helps domestic electronic commerce sellers to uniformly manage overseas shops, solves the obstacle caused by language difference, can realize that one operator can manage a plurality of electronic commerce shops at the same time, and greatly improves the efficiency of the operation of the shops.
The e-commerce ERP system accesses and controls a store of the e-commerce platform through a set rule, processes dynamic data of each link of store operation, manages numerous and complicated data, and meets the operation convenience requirements of multiple types of users (sellers); therefore, all functional modules of the existing commercial ERP system are in the stage of gradually updating and perfecting functions, the function algorithms and rules formulated by all software enterprises when the software enterprises develop from the ERP system of the household appliances are basically different, and all functional modules continuously develop new versions along with the change of the user demands so as to be compatible with more use scenes.
Taking the amazon platform as an example, the platform would provide financial reports to sellers on a regular basis, but the lag time for such reports is long. In the prior art, an ERP system acquires the management information of a seller shop through an API interface of an Amazon platform, and then identifies and analyzes the acquired data information. The data comprises financial information such as cost, advertising cost, tax, logistics cost, storage cost and the like, and finally the cost information is processed through an ERP system and new financial data is formed for presentation to a user. However, when the ERP system acquires information through the API interface of the Amazon platform, information loss easily occurs, and thus inaccurate financial information presented to the user is caused.
Specifically, in the amazon platform, a portion of the inventory is in stock when it is in stock, and in order to reduce losses or cost, sellers periodically perform a clearing operation on the stock articles in stock; after the completed clearing operation is canceled for some reason (e.g., by a malfunction), a clearing adjustment order is generated. The information returned by the Amazon API interface is missing due to a certain reason of the Amazon platform, so that the existing ERP system cannot effectively identify and process the data, and further the financial information presented to the user by the ERP system is inaccurate.
In addition, the amazon platform may periodically provide sellers with reports of financial information regarding service fees, but the reporting may have a long lag time, and the ERP system may not be able to obtain and generate real-time financial information regarding service fees via the amazon platform's interface.
Specifically, in the amazon platform, service fees (ServiceFee) are a collective term for a large class of fees, and the service fees include a plurality of kinds of fees such as subscription fees, monthly warehouse fees, long-term warehouse fees, destruction fees, removal fees, return warehouse fees, packaging fees, cooperative shipping fees, labeling fees, and the like. However, for some reason on the amazon platform, in service fee (servicebee) data obtained in real time through the amazon API interface, a PostedDate (time of release of the fee, i.e., time of settlement of the fee by the amazon platform) parameter is not given, and the time of occurrence of the fee cannot be determined, i.e., normal calculation and statistics cannot be performed.
Three main solutions to the above problems exist in the industry. The first way is to supplement the expense time by reporting from the subsequent platform, but this way is not timely because the platform has hysteresis in reporting. The second way is to simply estimate the time, which is obviously not accurate, although it has timeliness. The third mode is to combine the first mode and the second mode, simply estimate the occurrence time of the service fee, and use the report to correct the estimated value after the platform reports, and this mode may cause the disadvantage of stability due to the post correction or modification of the data. Because the occurrence time of the expense cannot be determined, the accuracy, timeliness and stability of the financial information generated by the ERP system in real time cannot be guaranteed, and the convenience of using the ERP system is further affected.
Other technical problems related to the present application are further described below. The foregoing is provided to facilitate an understanding of the principles of the application and is not intended to represent all of the prior art.
Disclosure of Invention
The application aims to provide an information complement method and an ERP system, which can identify and complement the missing information in the acquired data, thereby providing timely and accurate store financial information for users. In addition, the application also provides a financial data matching method and an ERP system, which provide accurate and stable real-time financial information for sellers or operators, improve the convenience of using the ERP system, and further help users to better manage or manage electronic stores.
In order to achieve the above purpose, the present application provides an information complement method for a financial module of an e-commerce ERP system or an e-commerce platform system, the financial information complement method comprising the following steps:
s1, acquiring data access rights of an electronic commerce platform;
s2, acquiring and analyzing real-time user data generated by the e-commerce platform;
s3, acquiring and analyzing historical user data generated by the e-commerce platform;
s4, acquiring clearing adjustment order information in the historical user data and/or the real-time user data, and searching the clearing order information according to the clearing adjustment order information;
S5, calculating and generating supplementary record data according to the clearing order information;
s6, merging the complement data and the real-time user data, and generating target user data.
Additional features and technical effects of the present application are set forth in the description that follows. The technical problem solving thought and related product design scheme of the application are as follows:
taking Amazon platform as an example, the operation condition of the store can be reflected by analyzing financial data such as order data, profit data, promotion cost, advertisement cost, warehouse cost, FBA cost, tax, head cost, logistics cost, purchasing cost and the like in the operation process of the store; the financial report provided by the Amazon platform is non-real-time data, and has longer hysteresis, so that the seller or operator can not adjust or implement corresponding operation means in real time according to the operation condition of the store; the data acquired in real time through the Amazon interface is missing due to some reasons of the Amazon platform. Further, after extensive research and analysis, the applicant found that the missing data were as follows:
1. amazon sellers typically open multiple stores, e.g., sellers open stores on netherlands, italian, germany, france, and spanish sites, which all belong to the euro area, and ERP systems cannot distinguish and categorize the data directly returned by amazon platform API interfaces, resulting in the data being non-attributive and thus inaccurate financial data.
2. While some of the items are sold in stock, in order to reduce loss or cost, sellers periodically perform clearing operations on the sold stock items, and after the clearing operations are canceled for some reason, clearing adjustment orders are formed, and the information returned through the amazon API interface is missing for some reason on the amazon platform.
For the first case, the non-attributive Euro data can be matched through currency and mall names, and the sites and related data of the Euro area can be independent through the currency. If the seller opens a plurality of shops in the Euro area, the financial data at the moment is still indistinguishable; for example, if a seller opens a store in germany and france, respectively, data of german sites and france sites acquired through the amazon platform are mixed together, and the data can be divided into two groups by site information in the data to perform preliminary classification on the data.
Meanwhile, since the shops cannot trade at all times, when no order exists, certain acquired data close to the access time (when the data are in the process of the next time) does not contain order information, and at the moment, corresponding site information cannot be acquired from the data, so that the ERP system cannot recognize the data.
Further, applicants have found that data within a site is typically generated time-sequentially from one site to another, and that individual data may be arranged or associated sequentially by the time the data is generated. When the site information cannot be acquired from a certain piece of data (because no order exists), the next data segment is sequentially called, the site information is continuously acquired from the next data segment until the data form a match, so that the data has definite attribution, the ERP system is convenient to acquire and process the relevant information in the data, and real-time and accurate financial data are generated.
For the second case, the amazon platform cannot provide the related information of the clearing adjustment order, and therefore cannot perform verification processing on the clearing order. After a great deal of analysis and research, the applicant finds that the clearing adjustment order ID (order code) can be found from the clearing order information, then the clearing order information can be obtained therefrom, then the historical clearing expense information is obtained according to the clearing order information, and finally the expense amount is obtained according to the historical clearing expense information; if there is only one of the historical clearing cost information, the cost amount is taken as its opposite number. When the historical clearing expense information and the expense amount are multiple, the historical clearing expense information is matched with the expense amount, and then the expense amount is taken as the opposite number. And further, verification and approval processing of the clearing adjustment order is completed, real-time and accurate financial data are generated, and the scheme avoids influencing the accuracy of the generated financial data because corresponding verification and approval processing is not performed.
In summary, the verification process of the clearing adjustment order is performed by acquiring and calling the historical data, and performing verification calculation according to the clearing order information and the expense amount thereof acquired by the historical data. And then combining the generated verification and verification data with the data acquired in real time (namely, the complement processing), thereby generating the required financial data. Due to the fact that missing data can be automatically detected and complement processing is carried out, the user can acquire financial data in real time, meanwhile, the integrity and the accuracy of the financial data are guaranteed, and further, the user can accurately apply various marketing strategies based on the financial data, and the reliability and the convenience of system use and the operation efficiency are improved. Other embodiments and technical effects are set forth below.
Furthermore, the application also provides a financial data matching method for a financial module of an e-commerce ERP system or an e-commerce platform system, which comprises the following steps:
k1, acquiring data access rights of an e-commerce platform and accessing the data access rights;
k2, respectively acquiring a first time parameter and target service charge data according to a first API interface, and acquiring a plurality of pieces of reference service charge data segmented according to a preset time interval according to a second API interface;
K3, assigning the first time parameter to the target service charge data;
k4, matching the target service charge data in the step K3 with the reference service charge data;
if the matching is completed, assigning the time value of the reference service charge data to the target service charge data;
and if the time intervals cannot be matched, sending a request for modifying the time intervals to the second API interface.
Taking amazon platform as an example, in order to meet the user's requirements for use of financial data, there are typically 3 solutions in the prior art: the first scheme is to supplement the expense time through the report sent by the subsequent platform, but the platform has hysteresis, so the method has no timeliness; the second scheme is that the time of the financial cost is simply estimated, and the method has timeliness but obviously has no accuracy; the third scheme combines the first scheme and the second scheme, firstly simply estimates the occurrence time of the service fee, then uses the report to correct the estimated value after the platform reports, and the mode has the advantages of the first scheme and the second scheme, but has the defect of data stability of the service fee caused by the correction (modification) action.
In order to solve the above-mentioned shortcomings, the applicant has found after a great deal of research and analysis that two parameters, namely financialEventGroupstart and financialEventGroupend, can be obtained from the Amazon LittfinancialEventGroups interface, and by means of the two parameters, a time range in which ServiceFee (service fee) occurs can be approximately determined. Second, the start time returned in the amazon platform is generated from the settlement period of the data, that is, the start time returned by the amazon platform is often inconsistent with the start time of the user request. For example, the user requests to obtain ServiceFee data or target service fee data from 8 months 12 days to 8 months 20 days (now), and the returned FinancialEventGroupStart may be 8 months 10 or 8 months 6. Further, the servicefile data of a specified time span (for example, 8 months 12 days to 8 months 20 days (now)) can be obtained through the listfinaiievents interface, that is, all data (refer to service fee data) in the time period can be obtained by taking the time as an incoming parameter. And then matches (target service charge data) among these data, if it can be, it means that the target service charge data is within this time span. Then, the time range of the input parameters is continuously narrowed, namely, the reference service fee data is divided by a dichotomy, and the occurrence time of a corresponding precision range is determined for the corresponding fee in the target service fee data, so that the ERP system can perform corresponding calculation and statistics according to the service fee data of which the occurrence time is determined, and further generate financial information required by the user.
Further, corresponding target service fee data and starting and ending time (determining time range) of the occurrence of the target service fee are respectively obtained through the first interface, the reference service fee data after segmentation is obtained through the second interface by taking time as an input parameter, and then the target service fee is matched with the reference service fee data after segmentation, so that the occurrence time of the fee in the target service fee data can be determined, and the accuracy, timeliness and stability of the data are realized while the data are ensured to be not heavy and not leaked, namely, the ERP system can provide accurate, timely and stable financial information for users. Other embodiments and technical effects are set forth below.
The financial module of the application can be used for an e-commerce ERP system or an e-commerce platform system (such as Amazon, ababa, yi Bei and other e-commerce platform systems) for executing operation instructions contained in each algorithm of the financial module.
Furthermore, the application also provides a computer device, which comprises a memory and a processor, wherein the financial module and the ERP system are stored in the memory, and the processor can run the operation instructions of the ERP system and execute the function implementation method of each function module.
The following is stated: for simplifying the description, the application mainly expresses the related technical scheme based on the Amazon platform, and the description needs to be that the E-commerce platform related to the application can be other E-commerce platforms besides Amazon. In addition, the ERP system comprises one or more of the functional modules of a commodity module, a sales module, a purchasing module, a logistics module, a warehouse module, a financial module, an advertisement module, a customer service module, a tool module, a right management module, a data module and the like, wherein the functional modules can be mutually fused or independently exist, and one functional module can be used as a sub-module of another functional module. Operators of the ERP system of the present application may also be referred to as store managers, sellers, operators, or users, whose identity is not strictly defined unless specifically stated.
Meaning and description of e-commerce field nouns in the application (letters other than English words and field symbols in the application are not case-independent):
1. ERP (Enterprise Resource Planning) is an enterprise resource planning, which is a management platform based on information technology and provides decision operation means for enterprise decision-making layers and staff with systematic management ideas, and can also be used as an abbreviation of ERP systems in the application.
2. API: application Programming Interface, an application programming interface, also commonly referred to as an "interface," is a collection of definitions, programs, and protocols by which computer software can communicate with one another.
3. Servicebee: also called "service fee". It is currently known to contain 32 costs, for example: subscription fees, monthly warehouse fees, long term warehouse fees, destruction fees, removal fees, return warehouse fees, packaging fees, collaborative shipping fees, labeling fees, referred to herein as a class of fees on the Amazon platform.
4. PostedDate: one of the parameters returned by the e-commerce platform interface is the release time of the fee, namely the time for the platform to settle the fee; listfinniaeventgroups: one of the E-commerce platform interfaces can acquire information such as Finanialeventpid and the like in a designated time; listFinanialeventsByGroupID: one of the E-commerce platform interfaces needs to be transmitted into the financial EventGroupID and can return the financial Events; financialEventGroupId: one of the E-commerce platform interfaces is the serial number of a group of financial events; listFinanialeves: one of the interfaces of the (amazon) e-commerce platform can acquire FinancialEvents within a specified time. In the application, the above e-commerce platform is referred to as an amazon e-commerce platform.
5. Financialents: financial events, the e-commerce platform placing fees in a plurality of financial events; serviceeventlist: a financial event returned by the E-commerce platform interface; removalshipsetadvustmenteventlist: a financial event returned by the E-commerce platform interface, through which the cost information of the clearing adjustment order can be obtained; removalShipmentEventList: and the E-commerce platform interface returns a financial event through which the cost information of the clearing order can be acquired. In the application, the above e-commerce platform refers to an amazon e-commerce platform.
6. FinancialEventGroupStart: the start time, also called StartTime, is returned by the listFinanialEventGroups interface; finaliaeventgroup: the end time, also called EndTime, is returned by the listfinniaeventgroups interface.
7. MarketplaceName: mall name. Here, a field returned by the (amazon) e-commerce platform interface is referred to, and the field value is amazon.
8. FeeAmount: clearing fees; derId: order number; currencyAmount: the field returned by the (amazon) e-commerce platform interface is an array containing the currency and the amount.
9. RevenueAdjust: clearing income returns; taxAmountAdjust: clearing tax returns; taxWithheldAdjust: the adjustment and return of the payment tax of the mall payment tax are carried out; revenue: clearing revenue; taxAmount: clearing tax; taxWithheld: the mall for clearing the tax withholds the tax.
10. Clearing the adjustment order: a special order on the (amazon) e-commerce platform. For goods in the FBA silo, a removal clearance may be applied, at which point a clearance order may be generated; when a clearing order is to be withdrawn, a corresponding clearing adjustment order is generated.
Drawings
The accompanying drawings are included to provide a further understanding of the application, and are not to be construed as limiting the application; the content shown in the drawings can be real data of the embodiment, and belongs to the protection scope of the application.
FIG. 1 is a flow chart of a method for repairing financial information according to an embodiment of the application;
FIG. 2 is a schematic diagram of a method for repairing financial information according to an embodiment of the application;
FIG. 3 is a flow chart of a financial data matching method according to an embodiment of the application;
FIG. 4 is a logic diagram of a financial data matching method according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the following detailed description of the embodiments of the present application is given by way of specific examples with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
As shown in fig. 1-2, the present embodiment provides a financial information complement method for a financial module of an e-commerce ERP system or an e-commerce platform system, where the financial module is configured to perform operations in the financial information complement method, and the financial information complement method includes steps S1-S6, which are specifically described below.
S1, acquiring data access rights of an electronic commerce platform; in this embodiment, taking the amazon platform as an example, after the access right or authorization of the amazon platform is obtained, the ERP system can obtain data from the API interface corresponding to the amazon platform.
S2, acquiring and analyzing real-time user data generated by the e-commerce platform; reference herein to "real-time" is a relative concept that refers to the data that is available from the API interface of the amazon platform that is closest to the current time.
S3, acquiring and analyzing historical user data generated by the electronic commerce platform; the "historical user data" refers to data which is periodically generated by the Amazon platform and can be acquired, and also can be data acquired from an ERP system database; of course, these data are generated later in time than the real-time user data.
S4, acquiring clearing adjustment order information in the historical user data and/or the real-time user data, and searching the clearing order information according to the target order information. Further, the financial data for reference and use by the user cannot be directly generated based on the acquired real-time user data, i.e., the real-time user data is missing or incomplete, and the ERP system needs to use the real-time user data and/or the historical user data as support and process the real-time user data to acquire data related to clearing order information.
S5, calculating and generating supplementary record data according to the clearing order information; and correspondingly calculating the clearing order information, carrying out verification calculation of the clearing adjustment order, and generating corresponding data, namely, carrying out complement of the data.
S6, combining the complement data and the real-time user data, and generating target user data, namely financial data which is provided for the user to reference or use. Further, the method also comprises the step of performing data cleaning processing on the target user data.
Acquiring and calling historical data, and performing verification and calculation according to the clearing order information and the expense amount thereof acquired from the historical data, namely performing verification and calculation processing of the clearing adjustment order; and then merging the generated verification and verification data with the data acquired in real time, namely, data complement. Furthermore, due to the fact that missing data can be automatically detected and complement processing is performed, the user can obtain financial data in real time, meanwhile, the integrity and accuracy of the financial data are effectively guaranteed, and further the user can accurately apply various marketing strategies or marketing means based on the financial data, and the reliability and convenience of system use are improved.
In a preferred embodiment, step S2 further includes, if there is no attribution data of the euro areas in the real-time user data, performing a matching process on the no attribution data of the euro areas, so that the no attribution data is associated with a corresponding electronic shop; if not, the operation in step S3 is performed.
Further, an original currency label and an original mall name label in the real-time user data are obtained, and a target currency label and a target mall name label in the non-attribution data are obtained. And then comparing or matching the obtained target currency label and the target mall name label with the original currency label and the original mall name label, and establishing association between non-attribution data and the electronic store if the target currency label and the target mall name label are the same as the original currency label and the original mall name label. In particular, each country uses a respective currency, but multiple countries within the european union hierarchy use the same currency, i.e. euros. Furthermore, the sites of the European Union area or related data can be independently given out through currency, however, sellers often set up a plurality of shops in the European Union area, and the data can not be distinguished at the moment; if the seller opens shops in germany and france respectively, the data of the germany site and the france site acquired by the amazon platform are mixed, so that the data still need to be further matched to be belonged to the corresponding shops. Further, in amazon platform, the MarketplaceName of the netherlands is amazon.nl, the MarketplaceName of the italy is amazon.it, the MarketplaceName of the germany is amazon.de, the MarketplaceName of the france is amazon.fr, the MarketplaceName of the spain is amazon.es, and the MarketplaceName of the belgium is amazon.com.be, so the original market name tag in the real-time user data and the target market name tag in the non-attribution data are continuously compared, and the non-attribution data and the corresponding stores can be formed in a one-to-one correspondence.
In a preferred embodiment, the non-attribution data are associated according to a preset settlement period and form at least one data group; when the target mall name label cannot be acquired from the first non-attribution data in one data group, sequentially calling and acquiring the target mall name labels in other non-attribution data, and when the target currency label and the target mall name label are identical to the original currency label and the original mall name label, establishing the association between all non-attribution data in the data group and the electronic store. Specifically, since the store cannot trade at all times, a scenario is generated in which there is only the currency in the non-attribution data, but since the store trade order is not generated, the target mall name tag in the non-attribution data cannot be acquired, so that the data cannot be associated with the corresponding store. Further, the data generated by the amazon platform are sequentially generated in a segment-by-segment manner, that is, the ending time of the last non-attribution data, such as 15 minutes and 23 seconds at 18 d of 8/month of 2023, then the starting time of the next non-attribution data corresponding to the ending time of 15 minutes and 23 seconds at 18 d of 8/month of 2023 is necessarily required, and according to the corresponding relation, the non-attribution data can be divided into a data group. Further, assuming that the target mall name tag cannot be acquired from the first non-attribution data in this data group and the end time of the non-attribution data is 2023, 8, 9, 18, 15 minutes and 23 seconds, then non-attribution data whose start time is 2023, 8, 9, 18, 15 minutes and 23 seconds is called next, and the target mall name tag is acquired therefrom, and if the target mall name tag cannot still be acquired from the non-attribution data, the next non-attribution data corresponding thereto is called and executed one by one. Further, when the target mall name tag is the same as the original mall name tag, the association between all non-attribution data in the data group and the electronic shops is established, that is, the data in the data group is associated through the time node, so that after knowing which shop the data belongs to through the target trademark name tag, the determination is made as to which shop the non-attribution data in the group belongs to.
In a preferred embodiment, when one non-attribution data within each data group can be associated with any two or more other non-attribution data, processing of the group of data is exited. In particular, there is a special case when the correlation is established for these non-attributive data by means of a time node or settlement period; assuming that one of the non-attribution data ends at 2023, 8, 9, 18 minutes and 23 seconds, while two other non-attribution data starts at 2023, 8, 9, 18 minutes and 23 seconds, one of the data must not belong to the data group, and no destination mall name label can be obtained from the two non-attribution data. That is, the ERP system cannot accurately identify and process the attribution of the data, at this time, the group of data is stored and the processing of the group of data is stopped, and at the same time, the ERP system feeds back the abnormal condition of the group of data to the user. Further, for the ERP system to process abnormal data, the user can select manual matching; for example, the ERP system can obtain the information of the warehouse expense in the abnormal non-attribution data, then the user logs in the Amazon store background to obtain the information of the warehouse expense in a corresponding time period, then the information of the warehouse expense is compared with the abnormal non-attribution data, when the warehouse expense of the information of the abnormal non-attribution data and the information of the warehouse expense are the same, the attribution of the abnormal data can be determined, and then the attribution of the data is manually designated.
In the preferred embodiment, after no attribution data is associated with the corresponding e-commerce store, it is further determined whether the real-time user data generated by the e-commerce platform in step S2 has been completely acquired, and if the real-time user data has not been completely acquired, step S3 is executed after waiting for the real-time user data to be completely acquired. So as to avoid the data information missing caused by the reasons such as the right of store authorization or network service, thereby ensuring the precision of the finally generated financial information.
In a preferred embodiment, the method further comprises determining whether the time span of the real-time data is greater than two years, and if the time span in the real-time data is greater than two years, calling a report generated by a report interface (Reports API) of the Amazon platform or calling a financial event interface (ListFinanialEvents) and supplementing missing parts in the real-time data. Specifically, the seller and amazon platform fail to settle accounts at a later time due to factors such as a small order quantity of the store, illegal operation, etc., that is, the special case that the settlement period exceeds two years when acquiring real-time data. Furthermore, as the settlement period exceeds two years, the Amazon platform does not respond to the request for the data acquisition of the settlement period of more than two years in the process of acquiring the real-time data, and at the moment, the system can know that the settlement period of more than two years appears in the data, that is, the acquired real-time data is missing; then, this part of the data about the settlement period needs to be acquired from elsewhere and fed into the real-time data. Further, if the store of the seller does not belong to the store in the EU area, acquiring missing data from the financial event interface and supplementing the missing data into corresponding real-time data; if the store of the seller belongs to the store in the European Union area, corresponding data are acquired from the Amazon report interface and are fed into the corresponding real-time data, so that the accuracy of the acquired real-time data is further improved, and the accuracy of financial data presented to the user in the later period is further ensured.
In a preferred embodiment, step S5 further comprises steps S51-S56:
s51, acquiring clearing adjustment order information (RemovalShimmAdjust Adjust EventList), and acquiring clearing adjustment order ID (OrderId) according to the clearing adjustment order information; the clearing adjustment order information is a financial event returned by the Amazon electronic commerce platform interface, and the expense information of the clearing adjustment order can be obtained through the financial event, for example, the clearing adjustment order ID or the order number can be found.
S52, acquiring clearing order information (RemovalShismentEventList) according to the clearing adjustment order ID; the clearing order information is a financial event returned by the amazon e-commerce platform interface through which the specific cost of the clearing order can be obtained.
S53, acquiring historical clearing expense information (FeeAmount) according to the clearing order information.
S54, acquiring the historical expense amount (Currency Amount) in the clearing order information according to the historical clearing expense information (FeeAmount); wherein the cost amount is an array which includes the currency in addition to the amount information.
S55, acquiring real-time clearing expense information (FeeAmount) according to the clearing adjustment order information, and acquiring real-time expense amount (Currency Amount) according to the real-time clearing expense information;
S56, judging whether the number of the historical clearing expense information is single or multiple;
if the historical clearing expense information is single, the historical expense amount is given to the real-time expense amount after the opposite number is taken;
if the historical clearing expense information is multiple, after matching of the historical clearing expense information and the real-time clearing expense information is completed, the historical expense amount is obtained by the opposite number and is given to the real-time expense amount.
In a preferred embodiment, if the historical clearance fee information (feeaunt) is plural, in step S56, a clearance income return fee (revueadjustment), a clearance return tax (tax amountadjustment) and an adjustment return fee (tax withholding) for the mall generation of the clearance tax in the clearance adjustment order information are acquired, while a clearance income fee (revue), a clearance tax (tax amount) and a tax payment fee (tax withhold) in the clearance order information are acquired;
the clearing income return fee is matched with the clearing income fee, the clearing tax fee is returned (the clearing tax fee (and the adjustment return fee for paying tax in the mall for clearing tax and the tax fee for paying tax in the mall for clearing tax) and the historical fee amount in the corresponding historical clearing fee information is obtained as the opposite number after the matching is completed, and the real-time fee amount is given.
Specifically, because the information in the clearing adjustment order is missing, corresponding tracing and reverse calculation can be performed through clearing the order information, so that the information missing in the clearing adjustment order is complemented. For example, the composition of a certain clearing order is as follows: the clearing income charge (Revenue) is 100 Euro, the clearing tax (TaxAmount) is 10 Euro, the tax payment by mall (TaxWithheld) is 2 Euro, and the historical charge amount (Currency Amount) is 112 Euro. The composition of the clearing adjustment order is as follows: the clear income charge (revnue) is 100 euros, the clear tax (tax amounts) is 10 euros, the tax payment by the mall (tax withhold) is unknown, the real-time charge amount (currencyamounts) is unknown, and the tax payment by the mall (tax withhold) is unknown, so that the real-time charge amount cannot be calculated. Furthermore, the lost mall tax payment can be known through tracing and matching, and meanwhile, the clearing adjustment order belongs to the verified data, so that the final correct data can be obtained through the inverse calculation of the corresponding expense amount, namely, the complement of the data is realized.
The embodiment also provides an ERP system, which comprises a financial module, wherein the financial module executes the operation in the financial information complement method.
Compared with the prior art, the application has the following main beneficial effects: acquiring and calling historical data, and performing verification and calculation according to the clearing order information and the expense amount thereof acquired from the historical data, namely performing verification and calculation processing of the clearing adjustment order; and then combining the generated verification and verification data with the data acquired in real time, namely, the complement, so as to generate final financial data. Furthermore, due to the fact that missing data can be automatically detected and complement processing is performed, the user can obtain financial data in real time, meanwhile, the integrity and the accuracy of the financial data are effectively guaranteed, and further the user can accurately apply various marketing strategies according to the financial data, and the reliability and the convenience of system use are improved.
As shown in fig. 3-4, the present embodiment provides a financial data matching method for a financial module of an e-commerce ERP system or an e-commerce platform system, the financial data matching method including steps K1-K5.
K1, acquiring data access rights of an e-commerce platform and accessing the data access rights; in this embodiment, a data access right of the amazon e-commerce platform is obtained.
And K2, respectively acquiring a first time parameter and target service charge data according to the first API interface, and acquiring a plurality of pieces of reference service charge data segmented according to a preset time interval according to the second API interface. Taking the amazon platform as an example, the first API interface in this embodiment is listmannialeventgroups, and the first time parameter includes a first start time (FinancialEventGroupStart) and a first end time (financialeventgroupe).
K3, assigning the first time parameter to the target service charge data; the obtained first start time (financialEventGroupstart) and first end time (financialEventGroupe) can be used for preliminarily determining the occurrence time range of target service fee data, then an initial time value can be assigned to related fees, or after the first time parameter is assigned to the target service fee data, the target service fee data is enabled to be specific to a period, and further related fees in the target service fee can be participated in calculation and statistics; that is, giving a time value to the fee can avoid omission of the fee.
K4, matching the target service charge data in the step K3 with the reference service charge data; if the matching is completed, the time value of the reference service charge data is assigned to the target service charge data. Specifically, the target service charge data is matched with the specific charge in the reference service charge data, and the reference service charge data is divided into a plurality of small segments according to time intervals, for example, the reference service charge data is divided into one hour and one segment, and when the charge in the target service charge data is matched with the charge in the small segment of the reference service charge data, the time value in the small segment of the reference service charge data can be assigned to the corresponding charge in the target service charge data.
If the time interval cannot be matched, a request for modifying the time interval is sent to the second API interface; specifically, it is assumed that the fee at a certain time point in the target service fee data is matched with two or more fees in the reference service fee data at the same time, and that the fee occurring in a certain time in the target service fee data can be matched with only one fee in the reference service fee data with respect to the time axis, that is, an uncertainty factor when two or more fees are matched. At this time, the reference service fee data is further divided by a "dichotomy" until the fee in the reference service fee data matches the fee in the target service fee data one by one and the time value is assigned to the corresponding fee in the target service fee data.
And K5, generating final data, further carrying out corresponding statistics and calculation by the ERP system, and finally forming financial information required by the user.
Specifically, corresponding target service fee data and starting and ending time (determining time range) of the occurrence of the target service fee are respectively obtained through the first interface, the reference service fee data after segmentation is obtained through the second interface by taking time as an input parameter, and then the target service fee is matched with the reference service fee data after segmentation, so that the occurrence time of the fee in the target service fee data can be determined, and the accuracy, timeliness and stability of the data are realized while the data are ensured to be not heavy and not leaked, namely, the ERP system can provide accurate, timely and stable financial information for users.
In a preferred embodiment, in step K3, further comprises: and acquiring a second time parameter of the target service charge data, acquiring an intersection of the second time parameter and the first time parameter, and giving an initial time value to the target service charge data. Specifically, assuming that the time of last acquiring the target service fee data is T1, the time of acquiring the target service fee data again is T2, the ERP system can know that the newly added data is generated between the times T1 and T2 after comparing the two acquired data (since the data is repeatedly pushed, that is, the data pushed at the time of T1 is also pushed together with the newly generated data at the time of T2), and this process may be referred to as "increment". Further, when matching is required for the data generated between T1 and T2, the intersection of the "period" and the "increment" is taken, that is, the time period of the target service fee data can be further shortened, so that the amount of calculation of matching can be reduced.
As shown in fig. 4, in the preferred embodiment, in step K2, further includes:
k21, acquiring a first parameter from a first API interface; the first parameter in this embodiment is FinancialEventGroupId, from which the number of financial events of the amazon platform can be further obtained.
K22, acquiring and accessing a third API interface according to the first parameter; the third API interface in this embodiment is listfinnaieventsbegroupid, and after the first parameter is loaded in the third API interface, a corresponding financial event may be further obtained.
K23, acquiring a second parameter from the third API interface; the second parameter in this embodiment is financial events.
K24, acquiring a third parameter according to the second parameter, and acquiring target service charge data from the third parameter; the third parameter in this embodiment is a financial event returned by the serviceeventlist, amazon official interface.
In a preferred embodiment, verification of the final data generated is also included in step K5. Specifically, after the ERP system completes matching of the data, the system compares the generated result data with the initial data to check whether the problem of repetition or missing occurs.
In a preferred embodiment, in step K4, if the target service charge data and the reference service charge data cannot be matched, the reference service charge data is divided (dichotomy) more than once by shortening the time interval until the divided reference service charge data and the target service charge data are matched. The "dichotomy" referred to herein is similar to the mathematical concept that, for a function y=f (x) that is continuous over the interval [ a, b ] and f (a) ·f (b) <0, the two end points of the interval are made to approach the zero point stepwise by continuously dividing the interval where the zero point of the function f (x) is located into two, thereby obtaining a zero point approximation. In the application, the time interval is continuously reduced (cut), and when the divided reference service charge data is matched with the target service charge data, the cut time interval can be used for assigning the target service charge data.
Specifically, the reference service charge data that cannot be matched is divided based on the time interval or by using the "dichotomy" to form the reference service charge data a and the reference service charge data B, respectively. Further, the target service charge data is matched with the reference service charge data a, and if the reference service charge data a matches with the target service charge data, the time value of the reference service charge data a is given to the target service charge number, and the operation of matching the target service charge data with the reference service charge data B is abandoned. Further, if one fee parameter in the target service fee data is the same as two or more fee parameters in the reference service fee data a, the reference service fee data a is divided again based on the time interval or the "dichotomy", and is matched with the target service fee data again, and the operation of matching the target service fee data with the reference service fee data B is abandoned.
Further, the target service charge data is matched with the reference service charge data a, if the reference service charge data a cannot be matched with the target service charge data, the target service charge data is matched with the reference service charge data B, and if the reference service charge data B is matched with the target service charge data, the time value of the reference service charge data B is given to the target service charge number. If one parameter in the target service charge data is the same as two or more parameters in the reference service charge data B, the reference service charge data B is divided based on the time interval, and is matched with the target service charge data again.
In a preferred embodiment, the span of the time interval of the target service fee data is reduced, or the span of the time interval of the reference service fee data is increased, or the span of the time interval of the target service fee data is reduced, and the span of the time interval of the reference service fee data is increased; the time interval of the target service charge data is enabled to fall in the time interval of the reference service charge data, so that the comparison of the target service charge data and the reference service charge data is facilitated. Further, the data is divided using a "dichotomy", which is essentially the division of time intervals, i.e., different data (costs) corresponding to different time intervals.
In another embodiment, in step K4, if the target service fee data and the reference service fee data are completely matched, the reference service fee data is continuously divided more than once, and the time value of the divided and matched reference service fee data is assigned to the target service fee data. If the current time interval of the reference service fee data is two hours, such as the time interval of 2 pm and 3 pm, if the accuracy of the time interval is required, such as 1 hour, to avoid the influence on the statistics and calculation of the financial information due to the time zone crossing, at this time, the reference service fee data may be further divided by using the "dichotomy", for example, if the matched reference service fee data is within the interval of 2 pm, the time value is assigned to the corresponding fee in the target service data, so that the occurrence time of the corresponding fee is accurate to within 1 hour.
The embodiment also provides an ERP system, which comprises a financial module, wherein the financial module performs the operation in the financial data matching method.
The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the scope of the application, and all equivalent modifications made by the present application and the accompanying drawings, or direct/indirect application in other related technical fields are included in the scope of the present application.
Claims (10)
1. The information re-recording method is used for a financial module of an e-commerce ERP system or an e-commerce platform system and is characterized by comprising the following steps of:
s1, acquiring data access rights of an electronic commerce platform;
s2, acquiring and analyzing real-time user data generated by the e-commerce platform;
s3, acquiring and analyzing historical user data generated by the e-commerce platform;
s4, retrieving the clearing adjustment order information in the historical user data and/or the real-time user data, and searching the clearing order information according to the clearing adjustment order information;
s5, calculating and generating supplementary record data according to the clearing order information;
s6, merging the complement data and the real-time user data, and generating target user data.
2. The information supplementary recording method according to claim 1, wherein in step S2, if there is no attribution data in the real-time user data, matching processing is performed on the no attribution data, so that the no attribution data is associated with a corresponding electric shop.
3. The information supplementary recording method according to claim 2, wherein an original currency tag and an original mall name tag in the real-time user data are obtained, and a target currency tag and a target mall name tag in the non-attribution data are obtained;
matching the obtained target currency label and the target mall name label with the original currency label and the original mall name label, enabling the target currency label and the target mall name label to be identical with the original currency label and the original mall name label, and establishing association between the non-attribution data and the electronic store.
4. The information supplementary recording method according to claim 3, wherein the non-attribution data are classified into data large groups according to a target mall name label, and a plurality of the non-attribution data in each data large group are associated according to a preset settlement period and form at least one data small group;
The method comprises the steps of obtaining target currency labels and target mall name labels of the non-attribution data in the data group one by one, matching the obtained target currency labels and target mall name labels with the original currency labels and the original mall name labels, enabling the target currency labels and the target mall name labels to be identical with the original currency labels and the original mall name labels, and establishing association between all non-attribution data in the data group and the electronic commerce.
5. The information supplementary recording method according to claim 3, wherein the non-attribution data is associated according to a preset settlement period and forms at least one data group;
and if the target currency label and the target mall name label are the same as the original currency label and the original mall name label, establishing the association between all non-attribution data in the data group and the electronic commerce store.
6. The method of claim 5, wherein processing of each data group is exited if one non-attribution data within the group is associated with any two or more other non-attribution data.
7. The information supplementary recording method according to any one of claims 2 to 6, wherein after the non-attribution data is associated with the corresponding e-commerce store, it is determined whether the real-time user data generated by the e-commerce platform in step S2 has been completely acquired;
and if the real-time user data is not completely acquired, waiting for the real-time user data to be completely acquired, and executing the step S3.
8. The information complement method according to any one of claims 1-6, characterized in that in step S5 further comprises:
s51, acquiring a clearing adjustment order ID according to the clearing adjustment order information;
s52, acquiring clearing order information according to the clearing adjustment order ID;
s53, acquiring historical clearing expense information according to the clearing order information;
s54, acquiring historical expense amount in the clearing order information according to the historical clearing expense information;
s55, acquiring real-time clearing expense information according to the clearing adjustment order information, and acquiring real-time expense amount according to the real-time clearing expense information;
S56, judging whether the number of the historical clearing expense information is single or multiple;
if the historical clearing expense information is single, the historical expense amount is given to the real-time expense amount after the historical expense amount is the opposite number;
if the historical clearing expense information is multiple, after the matching of the historical clearing expense information and the real-time clearing expense information is completed, the historical expense amount is obtained by the opposite number and is given to the real-time expense amount.
9. The information supplement method according to claim 8, wherein in step S56, if the historical clearance fee information is plural, the clearance income return fee, the clearance return fee, and the adjustment return fee for the mall substitute tax payment of the clearance tax in the clearance adjustment order information are acquired, while the clearance income fee, the clearance tax, and the mall substitute tax payment of the clearance tax in the clearance order information are acquired;
matching the clearing income return fee with the clearing income fee, the clearing return fee with the clearing tax fee and the adjustment return fee of the mall substitute payment of the clearing tax with the mall substitute payment of the clearing tax, and giving the corresponding historical expense amount in the historical clearing expense information the real-time expense amount after the matching is completed.
10. An ERP system, characterized by: the ERP system includes a financial module that performs the information re-entry method of any of claims 1-9.
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