CN111523691A - Merchant data processing method and system, cloud server system and storage medium - Google Patents

Abstract

The application discloses a merchant data processing method and system, a cloud server system and a storage medium. The method comprises the following steps: determining a geographical area where the target merchant is located based on the position information of the target merchant contained in the merchant list; determining at least one type of reference data for reflecting data changes of at least one type of reference merchant according to historical data of at least one type of reference merchant in a geographic area; generating anchor point data of the target merchant according to the at least one type of reference data and the historical data of the target merchant; the anchor point data is used for the target merchant to adjust the current setting data into display data which accords with the data change rule corresponding to at least one type of reference data. According to the method and the device, anchor point data are generated to guide the target merchant to adjust the current setting data of the target merchant, so that display data which can be displayed on a user side are saved unconsciously by the target merchant; and confirming whether the current setting data provided by the target merchant and used as the display data is abnormal data or not by utilizing the anchor data.

Description

Merchant data processing method and system, cloud server system and storage medium

Technical Field

The application relates to the field of computer data processing, in particular to a merchant data processing method and system, a cloud server system and a storage medium.

Background

The online booking platform provides more channels for acquiring information of the same industry for merchants, and compared with the traditional offline sales, the online sales realizes a cross-region consumption mode. However, the user can realize a cross-region shopping mode through online shopping, which is only one of online shopping modes, and in real life work, a shopping mode of taking goods offline through online payment also becomes a life mode on which the user depends. This type of lifestyle makes businesses around the place where users live and work, even if not seen, a dependent business relationship that is both concerned and competitive with each other.

As more and more merchants provide online services through online booking platforms, merchants who make abnormal sales with platform vulnerabilities, for example, exaggerating the effect of goods, lowering/raising the price of goods, etc., become difficult to find by online booking platform providers.

Disclosure of Invention

In view of the above drawbacks of the related art, an object of the present application is to provide a method, a system, a cloud server system, and a storage medium for processing merchant data, so as to overcome the technical problem that it is difficult for an online booking platform to check abnormal data displayed by merchants depending on regional sales in the related art.

To achieve the above and other related objects, a first aspect of the disclosure provides a merchant data processing method, including the steps of: determining a geographical area where a target merchant is located based on the position information of the target merchant contained in the merchant list; determining at least one type of reference data for reflecting data changes of at least one type of reference merchant according to historical data of the at least one type of reference merchant in the geographic area; generating anchor point data of the target merchant according to the at least one type of reference data and the historical data of the target merchant; the anchor point data is used for the target merchant to adjust the current setting data into display data which accords with the data change rule corresponding to the at least one type of reference data.

A second aspect of the present disclosure provides a merchant data processing method, including: determining a geographical area where a target merchant is located based on the position information of the target merchant contained in the merchant list; determining reference data for reflecting data changes of at least one brand of allied merchant in the geographic area according to historical data of the at least one brand of allied merchant; generating anchor data of the target merchant according to the reference data of the at least one brand and the historical data of the target merchant; the anchor point data is used for the target merchant to adjust the current setting data into display data which accords with the data change rule corresponding to the at least one reference data.

A third aspect of the present disclosure provides a merchant data processing method, including: determining a geographical area where each target merchant is located based on each position information of the target merchants contained in the merchant list; selecting a benchmarking merchant of the target merchant from a location geographic area by referring to the historical data of the target merchant; determining reference data for reflecting data changes of corresponding bidding merchants according to the historical data of the bidding merchants; generating anchor point data of the target merchant according to the reference data of the benchmarking merchant and the historical data of the target merchant; the anchor point data is used for the target merchant to adjust the current setting data into display data which accords with the data change rule corresponding to the reference data.

A fourth aspect of the present disclosure provides a merchant data processing system, comprising: the business circle processing module is used for determining the geographic area where the target merchant is located based on the position information of the target merchant contained in the merchant list; the merchant data processing module is used for determining at least one type of reference data for reflecting data changes of at least one type of reference merchant according to historical data of the at least one type of reference merchant in the geographic area; generating anchor point data of the target merchant according to the at least one type of reference data and the historical data of the target merchant; the anchor point data is used for the target merchant to adjust the current setting data into display data which accords with the data change rule corresponding to the at least one type of reference data.

A fifth aspect of the present disclosure provides a merchant data processing system, comprising: the business circle processing module is used for determining the geographic area where the target merchant is located based on the position information of the target merchant contained in the merchant list; the merchant data processing module is used for determining reference data for reflecting data changes of at least one brand of alliance type merchants in the geographic area according to the historical data of the at least one brand of alliance type merchants; generating anchor data of the target merchant according to the reference data of the at least one brand and the historical data of the target merchant; the anchor point data is used for the target merchant to adjust the current setting data into display data which accords with the data change rule corresponding to the at least one reference data.

A sixth aspect of the present disclosure provides a merchant data processing system, comprising: the business circle processing module is used for determining the geographic area where the target merchant is located based on the position information of the target merchant contained in the merchant list; the bidding merchant determining module is used for selecting the bidding merchant of the target merchant from the location geographic area according to the historical data of the target merchant; determining reference data for reflecting data changes of corresponding bidding merchants according to the historical data of the bidding merchants; generating anchor point data of the target merchant according to the reference data of the benchmarking merchant and the historical data of the target merchant; the anchor point data is used for the target merchant to adjust the current setting data into display data which accords with the data change rule corresponding to the reference data.

A seventh aspect of the present disclosure provides a cloud server system, including: at least one storage device for storing at least one program; at least one processing device, connected to the storage device, for executing and implementing the merchant data processing method according to any one of the first to third aspects when the at least one program is run.

An eighth aspect of the present disclosure provides a computer-readable storage medium storing at least one program which, when executed by a processor, performs and implements a merchant data processing method as described in any one of the first to third aspects.

In summary, according to the merchant data processing method, the merchant data processing system, the cloud server system, and the storage medium provided by the present application, at least one type of reference merchant is selected according to a geographic area where a target merchant is located, reference data reflecting a change trend of historical data of merchants in the geographic area is obtained by using historical data of the selected reference merchant, and anchor data for the target merchant or for a certain product of the target merchant is generated by using the reference data, so that the target merchant fills current setting data according to the anchor data to comply with the data change trend of the geographic area. Therefore, the current setting data are filled by guiding the target merchant to refer to the anchor point data, so that the display data which can be displayed at the user side and is unconsciously stored by the target merchant, namely the current setting data stored in the database, are reduced; and confirming whether the current setting data provided by the target merchant and used as the display data is abnormal data or not by utilizing the anchor data.

Other aspects and advantages of the present application will be readily apparent to those skilled in the art from the following detailed description. Only exemplary embodiments of the present application have been shown and described in the following detailed description. As those skilled in the art will recognize, the disclosure of the present application enables those skilled in the art to make changes to the specific embodiments disclosed without departing from the spirit and scope of the invention as it is directed to the present application. Accordingly, the descriptions in the drawings and the specification of the present application are illustrative only and not limiting.

Drawings

The specific features of the invention to which this application relates are set forth in the appended claims. The features and advantages of the invention to which this application relates will be better understood by reference to the exemplary embodiments described in detail below and the accompanying drawings. The brief description of the drawings is as follows:

fig. 1 is a flowchart of a merchant data processing method according to an embodiment of the present disclosure.

FIG. 2 is a flow chart of a merchant data processing method according to another embodiment of the present application.

FIG. 3 is a flow chart of a merchant data processing method according to another embodiment of the present application.

FIG. 4 is a flow chart of a merchant data processing method according to another embodiment of the present application.

FIG. 5 is a flow chart of a merchant data processing method according to another embodiment of the present application.

FIG. 6 is a block diagram showing the components of a merchant data processing system according to an embodiment of the present application.

Fig. 7 is a block diagram illustrating a structural configuration of a cloud server system according to an embodiment of the present invention.

Detailed Description

The following description of the embodiments of the present application is provided for illustrative purposes, and other advantages and capabilities of the present application will become apparent to those skilled in the art from the present disclosure.

In the following description, reference is made to the accompanying drawings that describe several embodiments of the application. It is to be understood that other embodiments may be utilized and that changes in the module or unit composition, electrical, and operation may be made without departing from the spirit and scope of the present disclosure. The following detailed description is not to be taken in a limiting sense, and the scope of embodiments of the present application is defined only by the claims of the issued patent. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

Also, as used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes" and/or "including," when used in this specification, specify the presence of stated features, steps, operations, elements, components, items, species, and/or groups, but do not preclude the presence, or addition of one or more other features, steps, operations, elements, components, species, and/or groups thereof. The terms "or" and/or "as used herein are to be construed as inclusive or meaning any one or any combination. Thus, "A, B or C" or "A, B and/or C" means "any of the following: a; b; c; a and B; a and C; b and C; A. b and C ". An exception to this definition will occur only when a combination of elements, functions, steps or operations are inherently mutually exclusive in some way.

The merchants with strong regional dependence displayed by the online booking platform, such as catering merchants, scenic spot merchants, retail merchants, accommodation merchants and the like, enter commodity data through the online booking platform and display the data at the user side. The merchants provide corresponding offline services by utilizing online payment processes provided by the online booking platform. For non-chained, unaffiliated small and medium merchants, the information uploaded to the online booking platform may disturb the content of the search list, merchant details, presented to the user side by the online booking platform. Taking the lodging merchants as an example, when the user searches for a lodging merchant near the target lodging place by using the online booking platform, if the searched lodging merchant contains too low/too high price set by the merchant, the corresponding price can influence the selection of the lodging merchant by the user, thereby being not beneficial to the normal sale of lodging services of other lodging merchants. The online booking platform side is not easy to perceive the behavior that merchants show corresponding information to the user side through the online booking platform intentionally or unintentionally. In order to guide the information uploaded by the merchant and not set deliberately to be in proportion to the products/services pushed on the online booking platform, and to enable the online booking platform side to be more easily aware of the information uploaded by the merchant and set deliberately, the application provides a merchant data processing method.

The merchant data processing method provided by the application is mainly executed by a merchant data processing system. The merchant data processing system comprises software and hardware configured on the cloud server system side, and at least shares data in a database with the online booking platform. For example, the cloud server system is also configured with the online booking platform and the database. The merchant data processing system also provides data for entering the data for the user side to display to the merchant side.

The merchant data processing system is used for uploading, viewing and modifying various merchant data by a merchant side through a merchant terminal so as to form products/services which can be recommended or sold on an online booking platform. The merchant terminal accesses the merchant data processing system by using the merchant account, which is, for example, an electronic device used by an operator of the merchant, such as a personal computer, a mobile phone, a tablet computer, and the like.

The online booking platform is used for the user to check various information provided by the merchant through the user terminal so as to select to purchase corresponding products or services. And the online booking platform is provided with a program for pushing a corresponding display interface and a data processing result to the user terminal based on various types of data of various merchants stored in the database. The user terminal is provided with an application program for an online booking platform, the application program is, for example, a beauty team APP, a carry away APP, a live APP, a piglet APP, a flying pig APP, a donkey mother APP, a where to go APP, a hornet APP, a cattle en route APP, an art dragon travel APP, an Airbnb APP, a Yelp APP, or a tripadevir APP, or the application program is, for example, a beauty team applet, a carry away applet, a live applet, a piglet applet, a flying pig applet, a donkey mother applet, a go to baby applet, a horse cell applet, a cattle en route applet, an art dragon travel applet, an Airbnb applet, a Yelp applet, or a tripadevir applet, or the like.

The merchant data processing system is in communication connection with the merchant terminal, and the online booking platform is in communication connection with the user terminal through a network. The network may be the internet, a mobile network, a Local Area Network (LAN), a wide area network (WLAN), a Storage Area Network (SAN), one or more intranets, etc., or a suitable combination thereof, and the embodiments of the present application do not limit the types of clients (e.g., merchant terminals or user terminals), services (e.g., merchant data processing systems or online booking platforms), or the types or protocols of communication networks between publisher terminals (e.g., merchant terminals or user terminals) and servers (e.g., merchant data processing systems or online booking platforms), responder terminals (e.g., merchant terminals or user terminals) and the servers.

Such as an electronic device loaded with an APP application or having web/website access capabilities, including components such as memory, memory controller, one or more processing units (CPUs), peripheral interfaces, RF circuitry, audio circuitry, speakers, microphones, input/output (I/O) subsystems, display screens, other output or control devices, and external ports, which communicate via one or more communication buses or signal lines. The electronic device includes, but is not limited to, personal computers such as desktop computers, notebook computers, tablet computers, smart phones, smart televisions, and the like. The electronic device can also be an electronic device consisting of a host with a plurality of virtual machines and a human-computer interaction device (such as a touch display screen, a keyboard and a mouse) corresponding to each virtual machine.

In some embodiments of the present application, the cloud server system may be arranged on one or more entity servers according to various factors such as function, load, and the like. When distributed in a plurality of entity servers, the server may be composed of servers based on a cloud architecture. For example, a Cloud-based server includes a Public Cloud (Public Cloud) server and a Private Cloud (Private Cloud) server, wherein the Public or Private Cloud server includes Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS), Infrastructure as a Service (IaaS), and Infrastructure as a Service (IaaS). The private cloud service end is used for example for a Mei Tuo cloud computing service platform, an Array cloud computing service platform, an Amazon cloud computing service platform, a Baidu cloud computing platform, a Tencent cloud computing platform and the like. The server may also be formed by a distributed or centralized cluster of servers. For example, the server cluster is composed of at least one entity server. Each entity server is provided with a plurality of virtual servers, each virtual server runs at least one functional module in the catering merchant information management server, and the virtual servers are communicated with each other through a network.

In order to execute the merchant data processing method provided by the application, the merchant data processing system can at least obtain data of merchants stored in the database. The acquired data is at least used for checking the data which are displayed or to be displayed to the user side by the target merchant, and also comprises the historical data of the corresponding merchant. The data obtained is related to the objectives being investigated by the merchant data processing system. For example, if the purpose to be investigated by the merchant data processing system is the price of the merchant, then the data obtained by the merchant data processing system is related to the price. For another example, the purpose to be investigated by the merchant data processing system is related to the search of the merchant, and the data acquired by the merchant data processing system is related to the search keyword.

Because the data displayed to the user side by the merchant has higher relevance with other merchants in the geographic area, the merchant data processing system firstly obtains a merchant list from the database during the checking. The merchant list is obtained by the merchant data processing system according to the information of each merchant in the database. The merchants included in the merchant list represent merchant information of the corresponding entity merchant, such as unique identification information of the merchant, merchant addresses, business scope, merchant abbreviation and the like. In order to facilitate the data provided by the same merchant to be analyzed by the merchant data processing system, the merchants included in the merchant list are selected according to the merchant operation range. For example, each merchant included in the merchant list is selected according to the operation range of the types of accommodation hotels, and the merchant list includes merchant information of the types of accommodation hotels, such as merchant information of five-star (or four-star) hotels, merchant information of chain (or franchise) hotels, merchant information of medium and small hotels, and the like. For another example, each merchant included in the merchant list is selected according to the operation range of the types of the catering restaurants, and the merchant list includes merchant information of the types of the catering restaurants, such as merchant information of a michelin samsung (or two-star) restaurant, merchant information of a chain (or franchised) type restaurant, merchant information of a medium-sized restaurant, and the like. The merchant data processing system obtains data related to the checked purpose by using the incidence relation constructed according to the merchant information in the database.

Referring to FIG. 1, a flow chart of one embodiment of a merchant data processing method is shown. The merchant data processing system executes at least the following steps S110 to S130 to provide the target merchant with anchor data, where the anchor data is used for the target merchant to upload non-abnormal data, in other words, the merchant data processing system facilitates guiding the target merchant to upload services/products meeting the expected quality of the user by displaying the anchor data to the merchant side. Therefore, the situation that the target merchant sets abnormal data under the non-intentional situation is solved, and the situation that the display data displayed to the user side by the target merchant is abnormal data and the like can be easily checked by the merchant data processing system according to the anchor data.

In some specific examples, when the merchant data processing system receives current setting data of a merchant terminal for a certain product/service (or corresponding merchant), steps S110-S130 are performed to determine whether the current setting data is abnormal data. In other specific examples, the merchant data processing system executes steps S110-S130 according to the execution cycle, and when receiving current setting data of the merchant terminal about a certain product/service (or corresponding merchant), feeds anchor point data obtained by executing the above steps back to the merchant terminal of the corresponding target merchant, thereby facilitating to guide the target merchant to adjust the current setting data. Here, the current setting data is data entered by a merchant, such as a commodity price, a per-capita consumption price, a keyword convenient to search, and the like. The anchor point data is prediction data which is generated by the merchant data processing system according to historical data of similar merchants in the geographic area and is close to the actual operation capacity of the target merchant, such as predicted commodity price, predicted per-capita consumption price, predicted keywords and the like.

In order to determine target merchants to be checked, such as small and medium merchants, individual merchants and the like, the merchant data processing system performs screening processing on all merchants in the acquired merchant list. Here, taking an example of an untagged target merchant in the database as an example, the merchant data processing method further includes step S100 (not shown), to obtain each target merchant in the geographic area to be determined in the merchant list by filtering attribute data of each merchant in the merchant list. Wherein the attribute data comprises at least one of: the first attribute data is used for reflecting the characteristics of the merchant, the second attribute data is used for reflecting the characteristics of the commodity displayed by the merchant, and the like. Wherein the first attribute data includes at least one of: a merchant linkage/affiliation attribute, a merchant rank attribute, a merchant participation in a ranking activity, etc. The rank attribute of the merchant is, for example, a star rank of the hotel merchant, or a star rank of the hotel merchant. Examples of the second attribute data include at least one of: attributes set based on item price/ranking, etc. Examples of the attribute set based on the price of the article include an attribute of the article participating in an online booking platform (or offline) promotional program. Examples of the attribute set based on the commodity rank include an attribute of the commodity participating in a search top-up activity or an attribute of the commodity participating in a geographic area evaluation rank. The target merchants obtained by utilizing the attribute data comprise: target merchants who do not participate in the promotion, and/or target merchants screened based on preset grade conditions, and the like. The ranking condition is, for example, the ranking attribute.

And the merchant data processing system screens the attribute information of each merchant in the merchant list according to a preset screening condition. Wherein the screening condition may include a condition of rejecting merchants and/or a condition of reserving merchants. If the conditions for removing the merchants and the conditions for retaining the merchants are mutually exclusive conditions, the merchant data processing system obtains each target merchant according to one of the screening conditions; if the conditions for removing the merchants and the conditions for retaining the merchants are not mutually exclusive, the screening conditions preset by the merchant data processing system set the conditions for retaining the merchants and the conditions for removing the merchants according to the logical AND, logical OR or equivalent relationship, and the merchant data processing system obtains the intersection or union of each merchant obtained according to the conditions for retaining the merchants and each target merchant obtained according to the conditions for removing the merchants. Taking the screening condition as the condition for rejecting the merchants as an example, the screening condition includes at least one of the following: the merchant data processing system reserves each target merchant which does not meet the screening condition from the merchant list according to the screening condition. The screening condition can also be set as a condition of reserving the merchant, and is not repeated here.

The merchant data processing system obtains at least one target merchant in the merchant list through screening, and in order to determine whether display data displayed to the user side by each target merchant is abnormal, the geographical area where each target merchant is located needs to be referred to. Wherein the geographic area is a geographic range related to the products/services exhibited by the target merchant on the online booking platform. For example, the three target merchants are lodging merchants, two of the three target merchants are located near the Tanjin walking street in Beijing, the other one is located near the Wangfu well shopping mall, and the difference between the guest room accommodation prices respectively displayed on the online booking platform by the two lodging merchants located near the Tanjin walking street is within 50 yuan, and the difference between the guest room accommodation prices and the guest room accommodation prices of the lodging merchants located near the Wangfu well shopping mall is more than 100 yuan. It can be seen that the display data (e.g., the guest room accommodation price) displayed by the target merchant is related to the consumption characteristics of the geographic region.

Correspondingly, the merchant data processing system executes step S110 to determine the geographic area where each target merchant is located.

In step S110, a geographic area where the target merchant is located is determined based on the location information of the target merchant included in the merchant list. And the geographic area and the target merchant are not in one-to-one correspondence. One or more target merchants may be covered within a geographic area based on the target merchants' concentration, screening criteria, etc.

In some examples, the geographic area may be based on the range radiated by the center of the physical business district. The center of the entity business district is, for example, the position of a mall building, or the position of a traffic platform such as a subway; the range radiated is in a geographic range of a preset radius around the center of the physical business circle, the preset radius being related to the size of the physical business circle. Correspondingly, the merchant data processing system determines the preset geographic area where the corresponding target merchant is located according to the preset coverage relation between at least one geographic area and the position information of each target merchant. Wherein the preset geographic area is a geographic range radiated based on the center of an entity business district. If the number of the preset geographic areas is multiple, the geographic areas are not usually set as areas having an intersection with each other. And the merchant data processing system determines the geographical area of each target merchant in the merchant list according to the single coverage relation between each preset geographical area and the position information of each target merchant. And if the geographical areas have intersection, the merchant data processing system corresponds each target merchant to one of the preset geographical areas according to the distance between the preset central position of each geographical area and the position information of the covered target merchants so as to determine the geographical area where each target merchant is located in the merchant list.

In other examples, the target merchants in the merchant list are not within the geographic area corresponding to the entity, and the merchant data processing system generates the geographic area in which each target merchant in the merchant list is located according to the location distance between each merchant in the merchant list. The merchant list includes the target merchant and other merchants (i.e., non-target merchants). And the merchant data processing system generates a corresponding geographic area where the target merchant is located according to the preset position distance between the target merchant and other merchants. In some specific examples, the merchant data processing system performs clustering processing according to the location information of each merchant in the merchant list, performs merchant area division processing on the location of each merchant according to the density of each merchant in the location or the distance between any two clustered merchants, thereby generating at least one geographic area, and a target merchant in the merchant list determines the geographic area where the target merchant is located according to the generated geographic area.

In still other examples, for a target merchant in the physical geographic area, the merchant data processing system determines a portion of the target merchants in the merchant list in the physical geographic area; and for target merchants not in the physical geographic area, the merchant data processing system determines another portion of the target merchants in the merchant list based on the generated geographic area.

It should be noted that, by using the target merchants and the non-target merchants covered by the geographic area, the merchant data processing system executes steps S120-S130 to perform data prediction on a certain target merchant in the same geographic area by using the historical data of at least one type of reference merchant selected from the geographic area, so as to obtain anchor point data that conforms to the business environment and the surrounding market environment of each merchant in the corresponding geographic area.

In step S120, at least one type of reference data for reflecting data changes of at least one type of reference merchant is determined according to historical data of the at least one type of reference merchant in the geographic area. Wherein the reference merchant is obtained from a merchant in the same geographic area as the target merchant. The reference merchants are at least one type of merchant selected by the merchant data processing system for calculating anchor data for the target merchant. The type of reference merchant is related to the manner in which anchor data is generated and/or is comparable to the product quality/service capabilities of the target merchant, as opposed to the merchant's business scope contained in the merchant list. To this end, the reference merchant type may be predetermined and/or selected from a list of merchants based on historical data for each merchant in the geographic area.

Taking as an example that the type of reference merchant comprises a preset and computationally selected plurality of types, the at least one type of reference merchant comprises at least one of: a federation-type merchant of the same brand, the target merchant itself, or a counterpoint merchant of the target merchant in the geographic region. The alliance type merchants and the target merchants of the same brand are both of preset types, and the alliance type merchants and the target merchants are obtained through calculation and selection. The alliance-type merchants with the same brand comprise chain-type merchants with the same brand and alliance-type merchants, for example, chain hotels such as family, light living, Hanting family and the like, and alliance hotels such as shang Guo and the like. And the target merchant is selected from the location geographic area according to the historical data of the target merchant. In order to distinguish the different types of reference merchants on the one hand and enable the historical data among the reference merchants to reflect the data change rules of the respective types on the other hand, the different types of reference merchants are selected without complete overlapping or even overlapping.

Here, the benchmarking hotel is a benchmarking merchant that selects the target merchant from a geographic area with reference to the target merchant's historical data. The historical data of the target merchant for reference comprises at least one of historical data of historical synchronization, historical data determined based on holidays and historical data of a preset time interval. Wherein, the historical data of the historical synchronization comprises: historical data for the same period of time across months/years, such as historical data for the last year today, or historical data for the last month for up to 7 days. Examples of the historical data determined based on holidays include: historical data from the first 7 days of one or more holidays until the end date of the respective holiday. Examples of the historical data of the preset time interval include: historical data for the last 7 days (or 14 days), etc. The historical data can also be selected in combination, for example, the historical data of a time interval containing holidays in the last month. The historical data can be obtained by data mining of at least one of historical data of historical synchronization, historical data determined based on holidays or historical data of a preset time interval. For example, the historical data is a string, and the merchant data processing system maps the corresponding string to a numerical value using statistics of usage of the string over a period of time to facilitate performance of subsequent steps. And the merchant data processing system respectively selects the historical data of the same period from the target merchant and all merchants in the geographic area according to the date, respectively analyzes the historical data, and selects the benchmarking merchants from the geographic area within preset selection conditions by taking the analysis result of the target merchant as a reference. The selection condition is, for example, an interval divided based on the analysis result of the target merchant. Wherein, the analysis mode includes but not limited to at least one of the following: and performing calculation processing such as weighted average, use frequency, probability distribution and the like on the historical data. For example, the historical data is numerical data (such as the check-in price), the merchant data processing system obtains an average value of the contemporaneous historical data of each merchant through average value calculation, a mean value interval is defined according to the average value of the target merchant, and the merchant with the mean value falling into the mean value interval is used as the benchmarking merchant. For another example, the historical data is character string data (such as keywords), the merchant data processing system calculates the frequency of use to obtain the probability distribution of the contemporaneous historical data of each merchant, and defines a probability interval according to the peak value of the probability of the target merchant, and takes the merchant with the probability peak value falling into the probability interval as the benchmarking merchant.

Taking the historical data of the target merchants for reference as the actual purchase and consumption price of the user with the current month of 7 days as an example, the merchant data processing system extracts the historical data of the previous month of 7 days of all the merchants in the merchant list, calculates the mean value of the historical data, and calculates the mean value of the target merchants

Set up a fluctuation △ p-element for the reference, select the mean at

Each merchant in the interval is used as a bidding merchant. As can be seen from the above example, the targeted merchants selected by the merchant data processing system for data processing at different times are not completely consistent according to the selected historical data and the setting of the selection condition. For example, the bidding merchant selected by the merchant data processing system in executing the scheme in the previous month is not completely consistent with the bidding merchant selected by the merchant in executing the scheme in the current month.

In fact, all merchants actually contained in the geographic area do not necessarily contain all types of reference merchants, and the merchant data processing system determines the reference merchants and the types thereof required in the data processing according to the types of the preset reference merchants and the merchants that can be contained in the actual merchant list. For example, if the merchants within the geographic area do not include federation-type merchants, then the reference merchants selected by the merchant data processing system include the target merchant itself and the target merchant. As another example, the reference merchant selected by the merchant data processing system may include the target merchant itself, as determined by the selection calculation that the merchants within the geographic area do not include the target merchant and the affiliated merchant.

It should be noted that the number of reference merchants of the same type that are selected may be one or more. If the number is multiple, the merchant data processing system selects the historical data of multiple reference merchants of the type to perform step S120.

The merchant data processing system performs data processing on historical data of different types of reference merchants by adopting the same or different data processing modes to determine at least one type of reference data reflecting data changes of various types of reference merchants. Wherein the merchant data processing system selects the reference merchant's historical data associated with the date attribute of the target merchant to determine corresponding reference data. Examples of the date attribute include at least one of: such as holidays, etc., and such as the own business activities of the target merchants, or cultural and literature activities in the geographic area, etc. For example, the anchor point data to be generated corresponds to the predicted price of the target merchant in the festival, and the historical data of the reference merchant selected by the merchant data processing system includes the historical data of the recent festival interval, the historical data of the same festival (or similar festival) in the past year, and the like. The reference merchant's historical data includes data that has been generated on the current day and data that has been generated prior to the current day. The historical data of the reference merchant comprises at least one of historical data of historical synchronization, historical data determined based on holidays, and historical data of a preset duration interval. Here, the historical data of the reference merchant is selected in the same or similar way as the historical data of the target merchant, and is not described in detail here. The reference data is exemplified by a proportionality coefficient and/or a deviation scale for reflecting data changes of various reference merchants based on the same-ratio/ring-ratio statistics.

In an embodiment, the merchant data processing system adopts a same-ratio statistic/ring-ratio statistic method to obtain historical data of the reference merchant so as to obtain first reference data for reflecting the data change proportion of the reference merchant; and/or calculating a data change difference value between the historical data of the reference merchant to obtain second reference data reflecting the data change deviation of the reference merchant. Here, the first reference data and the second reference data both belong to the above-mentioned reference data, and when one reference data is calculated by the above-mentioned one manner, the reference data corresponds to the first reference data or the second reference data.

In some examples, the merchant data processing system performs data processing on historical data of different types of reference merchants by using a ring ratio statistical calculation mode to obtain corresponding types of first reference data. Taking the calculation of the ring ratio as WOW (week-to-ring ratio) and the historical data as the actual consumption price as an example, the merchant data processing system calculates the first reference data of at least one type of reference merchant by using the calculation mode of the average price of the reference merchant today/the original price of the reference merchant in the near 7 days ago. For example, the merchant data processing system uses at least one alliance type merchant of the same brand as a reference merchant, and is configured to calculate a present average price of the reference merchant as a mean value of prices of commodities generated by each alliance merchant of the corresponding brand today, and calculate a previous original price of the reference merchant up to 7 days as a mean value of prices of commodities corresponding to each alliance merchant of the corresponding brand today in one week, so as to obtain first reference data of each reference merchant of the brand. When the brand is multiple, the obtained types of the first reference data are multiple, and the number of the first reference data corresponding to each type of the reference merchant may be one or more. For another example, the merchant data processing system uses the selected bidding merchant as a reference merchant, the today average price of the reference merchant for calculation is a mean value of commodity prices generated by each bidding merchant today, and the near 7-day previous original price of the reference merchant for calculation is a mean value of commodity prices corresponding to the week before one week of each bidding merchant, so as to obtain the first reference data of the type of reference merchant. Due to the comprehensive consideration of the prices of all commodities of all bidding merchants, the number of the first reference data obtained is one even if the number of the bidding merchants is large. For another example, the merchant data processing system uses the target merchant itself as a reference merchant, the today average price of the reference merchant for calculation is an average value of the yesterday prices of the target merchant, and the nearly 7 days ago original price of the reference merchant for calculation is an average value of the yesterday weeks worth of the target merchant, so as to obtain the first reference data of the reference merchant of the type.

In still other examples, the merchant data processing system performs data processing on historical data of different types of reference merchants by using a geometric statistic calculation mode to obtain corresponding types of first reference data. Taking a reference merchant as a target merchant as an example, a merchant data processing system performs comparably statistics on cross-year contemporaneous historical data of the target merchant and contemporaneous historical data derived from the current day (or the previous day) to obtain first reference data for reflecting the data change proportion of the target merchant. Taking the reference merchant as the target merchant itself as an example, the merchant data processing system performs geometric statistics by using the average consumption data of the target merchant in the previous day and the average consumption data of the target merchant in the natural week in the same day in the last year to obtain the first reference data.

In still other examples, the merchant data processing system calculates a data change difference for the historical data of the reference merchant to obtain reference data reflecting a data change bias for the reference merchant. Still taking the reference merchant as the target merchant itself as an example, the merchant data processing system uses the difference value of the cross-year synchronization historical data as the second reference data of the merchant itself taking the reference merchant as the target. For example, the merchant data processing system calculates a difference between the last year and this day actual consumption price and the last year and yesterday actual consumption price to obtain the second reference data. Still taking the reference merchant as the target merchant itself as an example, the merchant data processing system performs comparably statistics on the average consumption data of the target merchant in the previous day and the average consumption data of the target merchant in the natural week in the same day in the last year to obtain first reference data; and performing difference calculation by using the per-capita consumption data of the target merchant in the previous day and the per-capita consumption data of the target merchant in the same day of the last year to obtain second reference data.

In other examples, the merchant data processing system utilizes the examples described above to calculate the first reference data and the second reference data for use in subsequent steps. Still taking the reference merchant as the target merchant itself as an example, the merchant data processing system calculates the first reference data by using the actual consumption price of the current 1 day (or the previous 7 days) of the year/the actual consumption price of the last 1 day (or the previous 7 days) of the year; and calculating the difference value between the actual consumption price of the last year and the current day and the actual consumption price of the last year and the current day to obtain second reference data. The other reference merchants calculate in the same or similar manner as the target merchant itself and are not described in detail herein.

It should be noted that the manners provided in the above examples are not limitations on the data selection manner and the reference data generation manner of the reference merchants, and various reference merchants may calculate the reference data by using one or more of the above examples.

The merchant data processing system obtains various types of reference data corresponding to the types of reference merchants according to the calculation of the examples, and executes step S130 to calculate anchor point data of the target merchant.

In step S130, anchor point data of the target merchant is generated according to the at least one type of reference data and the history data of the target merchant.

The anchor point data is used for the target merchant to adjust the current setting data into display data which accords with the data change rule corresponding to the at least one type of reference data. The data change rule refers to that the change of information displayed to the user by the target merchants and the reference merchants through the online booking platform in the geographic area, which have similar operation types, is matched with the change of actual conditions of the target merchants and the reference merchants in the online booking platform, such as the operation range, the commodity cost ratio and the like. The anchor data is prediction data or a prediction data interval obtained according to at least one reference data of at least one type of reference merchant, and comprises at least one of the following: the current prediction average value of the target merchant, the current prediction average interval of the target merchant, the current prediction anchor point value of the corresponding service type in the target merchant, or the current prediction anchor point interval of the corresponding service type in the target merchant. The anchor point data can represent the current per capita consumption value and/or the current per capita consumption interval predicted by the merchant data processing system; the target merchant is an accommodation merchant, and the anchor point data can represent at least one of a current accommodation consumption value of the accommodation merchant, a current accommodation consumption interval of the accommodation merchant, a current accommodation consumption value of each type of accommodation merchant and a current accommodation consumption interval of each type of accommodation merchant predicted by the merchant data processing system.

Here, according to the number of types of reference merchants obtained by the merchant data processing system and the number of reference merchants in each type, the merchant data processing system may provide at least one type of reference data corresponding to the type of the reference merchant and the number of each type of reference data is at least one by performing step S120. For example, the type of reference merchant comprises a plurality of brands of league-type merchants, and the resulting reference data comprises reference data corresponding to each brand of league-type merchant.

In some examples, the merchant data processing system generates anchor data using the derived class of reference data. Taking the number of the obtained reference data as an example, if the reference data is used for reflecting the data change proportion of the reference merchant, the merchant data processing system multiplies a plurality of recent historical data of the target merchant by the first reference data respectively, and performs average processing on the multiplication result, wherein the obtained calculation result (i.e. average value) corresponds to the anchor point data of the target merchant. Taking a target merchant as a catering merchant as an example, the recent historical data of the target merchant are price data, the merchant data processing system multiplies the price data by first reference data respectively, and performs mean value processing on the multiplication result, the obtained mean value corresponds to anchor point data of the target merchant, and the anchor point data represents a current per capita consumption mean value of the target merchant, which is predicted according to a price change rule provided by the first reference data. Still taking a target merchant as a catering merchant as an example, the recent historical data of the target merchant are statistical data corresponding to certain keyword data, the merchant data processing system multiplies the statistical data by the first reference data respectively, and performs mean processing on the product results, the obtained mean corresponds to anchor point data of the target merchant, and represents a current statistical result of the keyword data of the target merchant predicted according to a search change rule provided by the first reference data.

Taking the obtained reference data as an example that the obtained reference data comprises first reference data and second reference data corresponding to a type of reference merchant, if the reference data comprises first reference data used for reflecting the data change proportion of the reference merchant and second reference data used for reflecting the data change deviation of the reference merchant, calculating the historical data of the target merchant by taking the first reference data as a proportionality coefficient and the second reference data as an offset, wherein the obtained calculation result corresponds to the anchor point data of the target merchant. For example, the merchant data processing system utilizes p with reference to the merchant as the targeted merchant itself_today＝p_{l_t}×con₁-Δcon₂Obtaining a calculation result of the target merchant, wherein the calculation result corresponds to anchor point data; wherein p is_todayFor the anchor data, p_{l_t}For the target merchant's own historical data on a certain day, con₁As a first parameter,. DELTA.con₂Is the second parameter. Wherein p is_{l_t}Historical data that may be selected based on date attributes of anchor data, such as historical data of the last year and this day, and the like. Here, each history data in the present example may correspond to price data, statistical data of keyword data, and the like in the foregoing example, which is not exemplified here.

Taking the obtained reference data corresponding to a type of reference merchant and the number of the reference data being multiple as an example, if each reference data is a proportionality coefficient, the merchant data processing system calculates the historical data of the target merchant by using each reference data as a weight, and performs an averaging process on the obtained multiple calculation results, wherein the obtained calculation results (i.e. the average values) correspond to the anchor data of the target merchant.

In other examples, the obtained reference data corresponds to multiple types of reference merchants, and similar to the example where the number of the reference data is multiple, the merchant data processing system performs individual calculation on the historical data of the target merchant by using each reference data, and then performs weighted mean (or weighted mean) processing to obtain a calculation result of the anchor point data corresponding to the target merchant.

Here, the merchant data processing system converts the calculation into anchor data in accordance with the description of the examples above. In some examples, the resulting computation is anchor point data. Taking the target merchant as a catering merchant, taking the obtained calculation result as the predicted current per-person consumption price of the target merchant as an example, wherein the current per-person consumption price is the anchor point data of the target merchant, or taking the current per-person consumption price as a center by the merchant data processing system to float up and down for a preset deviation, and obtaining the anchor point data of the target merchant as a current per-person consumption interval. Taking a target merchant as an accommodation merchant and taking the obtained calculation result as the predicted current per capita accommodation price of the target merchant as an example, wherein the current per capita accommodation price is the anchor point data of the target merchant, or setting different coefficients by a merchant data processing system according to house types and calculating the coefficients and the current per capita accommodation price to obtain the current predicted accommodation price of various house types of the target merchant; on the basis, the merchant data processing system takes the current per capita lodging price of each house type as a center to float up and down for a preset deviation, and anchor point data of each house type of the target merchant is obtained and is taken as a current per capita lodging price interval.

And the merchant data processing system sends the obtained anchor data to the corresponding target merchant so that the target merchant can refer to the prediction information provided by the anchor data when inputting the current set data. When the target merchant does not intend to input the current setting data deviating from the prediction information, the merchant terminal displays the anchor point data provided by the merchant data processing system on the interface, so that the target merchant is prompted to adjust the input current setting data, and the merchant data processing system stores the received current setting data as display data and displays the display data on the user side. When the target merchant intentionally inputs the current setting data deviating from the prediction information, the merchant data processing system also determines that the presentation data of the corresponding target merchant is abnormal when the current setting data is saved as the presentation data.

The current setting data is, for example, data that is input by the target merchant but has not yet been entered, for example, the current setting data is an accommodation price that is expected to be charged by an accommodation type entered by the accommodation merchant within a new week. The display data is, for example, data submitted to the merchant data processing system by the target merchant and entered into the database, and when the user views the corresponding target merchant, the display data is displayed on the interface of the user terminal. For example, the lodging price of the user consumption in the new week of a certain type of room saved by the lodging merchant is shown.

According to the description of the foregoing example, the merchant data processing system sends the anchor data to the target merchant so that the target merchant can fill in the current setting data according to the anchor data and submit the current setting data, and thus, the merchant data processing system saves the current setting data set based on the anchor data as the presentation data in the database and presents the presentation data on the user side. If the target merchant does not fill in and submit the current setting data according to the anchor data, the merchant data processing system determines that the corresponding target merchant intentionally provides abnormal data according to the non-coincidence relation between the generated anchor data and the stored current setting data (i.e., the display data).

In some practical processes, in order to reduce the fact that the historical data of some types of reference merchants among the multi-type reference merchants have large errors, after the merchant data processing system obtains the multi-type reference merchants, the merchant data processing system evaluates the prediction accuracy of the various types of reference merchants by using the historical data of the various types of reference merchants so as to select the reference merchants capable of calculating the reference data or the anchor data. In other words, the merchant data processing system further executes step S140 (not shown) to evaluate the prediction accuracy of each type of reference merchant by using the historical data of the multiple types of reference merchants in the geographic area, so as to select to determine the reference data of the corresponding type of reference merchant by using the historical data of the reference merchant with higher prediction accuracy, or select to generate the anchor data by using the reference data of the reference merchant with higher prediction accuracy.

Wherein the historical data used by the merchant data processing system in making the prediction is related to the historical data used in calculating the reference data. For example, the merchant data processing system utilizes historical data that includes all or part of the historical data used in calculating the reference data for the calculation of prediction accuracy.

Examples of the prediction accuracy used by the merchant data processing system include, but are not limited to: at least one of RMSE (root mean square error), MSE (mean square error), MAE (mean absolute error), MAPE (mean absolute percentage error), SMAPE (symmetric mean absolute percentage error) is calculated using historical data of the reference merchant. Taking the prediction error algorithm as MAPE for example, the merchant data processing system utilizes formula (1):

obtaining a prediction error, wherein n is the total amount of historical data, observed, of the same time interval of all reference merchants of the same type_kPredicted for the kth history data_kIs the prediction data corresponding to the kth history data. Wherein the kth predicted data is predicted_kFor example, observed_kAverage value of historical data for a predetermined time interval before the corresponding time, e.g. predicted data_kAs historical data observed_kThe average of historical data several days before the corresponding date (e.g., the previous 7 days).

In some specific examples, the reference merchant comprises at least one brand of league-type merchant, wherein the number of league-type merchants per brand is one or more. Taking the number of the brand of the reference merchant as one and the number of the alliance type merchants of the same brand as a plurality of examples, the merchant data processing system takes the historical data of all the alliance type merchants of the same brand in the same period as the whole historical data and inputs the historical data into a formula (1) to obtain a prediction error.

Taking the number of brands in the geographic area of the target merchant as an example, the merchant data processing system calculates the prediction error of each brand by using the formula (1). In some more specific examples, the merchant data processing system also evaluates whether the prediction error for each brand is less than the prediction reference and selects the brand of league-type merchants and their prediction errors that are less than the prediction reference. In yet other more specific examples, the merchant data processing system evaluates the brand that has the least deviation from the prediction reference and its prediction error for each brand. The prediction reference may be a fixed value, or may be represented by a prediction error obtained according to formula (1) by using historical data of all merchants in the geographic area.

The reference merchant comprises the target merchant itself, and in some more specific examples, the merchant data processing system calculates a prediction error for the reference merchant using equation (1) above, and determines the reference data using historical data for the reference merchant by comparing whether the prediction error is less than a prediction benchmark, and if so, determining the reference data. In some more specific examples, the merchant data processing system calculates the first reference data and the second reference data by using the historical data of the target merchant, predicts at least one candidate calculation result by using the historical data of the target merchant, and takes each candidate calculation result as the updated_kAnd calculating each prediction error corresponding to each candidate calculation result by using the formula (1), and selecting the prediction error with the minimum deviation from the prediction reference.

The reference merchant comprises at least one benchmarking merchant, and the merchant data processing system calculates the prediction error of the reference merchant using equation (1) above.

In some specific examples, if the reference merchant obtained by the merchant data processing system is of any of the above categories, steps S120-S130 are performed using historical data of the corresponding category of reference merchant.

In some specific examples, the type of the reference merchant subjected to prediction accuracy evaluation includes at least two types, and in order to further select an optimal type of reference merchant for calculation of the reference data or the anchor data, the merchant data processing system further performs step S150 (not shown) to evaluate prediction errors to determine prediction accuracy of historical data of each reference merchant. Specifically, the merchant data processing system ranks prediction errors of various reference merchants to represent a reference merchant of a type corresponding to the prediction error with the highest accuracy as a reference merchant for calculating reference data or anchor data. Taking the calculation mode of the prediction error as MAPE as an example, the merchant data processing system selects the minimum value of the prediction error from the prediction errors of various reference merchants to obtain the reference merchant of the type corresponding to the minimum value of the prediction error, and therefore the historical data of the selected reference merchant of the type is used for calculating the reference data or the anchor point data.

In other practical processes, the merchant data processing system uses the anchor point data obtained in steps S110-S130 as a price data, and the merchant data processing system further executes step S160 (not shown) to prompt the corresponding target merchant whether the current setting data belongs to the abnormal data or not based on the preset pricing/price adjusting condition.

Wherein the preset pricing conditions comprise: a fixed pricing condition threshold, or an anomalous pricing condition threshold set based on changes in the target merchant's historical data. Here, the history data is the same as or similar to the history data describing prices in the foregoing examples, and examples thereof include: historical data of the target merchant for a number of days in the near future, historical data of the target merchant for a number of days in the past year, and the like. Examples of the conditional threshold of the anomalous pricing set according to the change of the historical data based on the target merchant include: the condition threshold is determined by the average of the target merchant's historical data and a coefficient of anomalous pricing, such as 2 or 3. For example, the condition threshold is the average of the historical data of the target merchant in the last 7 days multiplied by 3.

For example, the accommodation merchant Hotel _1 inputs the current setting data of the type A house as P1 element through the merchant terminal, and the conditional threshold value is set according to the fixed pricing

The merchant data processing system determines the current setting data of the type A house set by the lodging merchant Hotel _1

The current setting data P1 is abnormal data, and the current setting data is displayed in the merchant terminal in the prompt color of red, highlight, etc. for the lodging merchant Hotel _1 to view.

For another example, the lodging merchant Hotel _2 inputs the current setting data of the type B house as P2 element through the merchant terminal, and the merchant data processing system uses the average value of the historical data of the lodging merchant Hotel _2 for about 7 days (or the same period of month/year) as the pricing condition threshold value

The merchant data processing system according to

Determining that the current setting data P2 of the B-type house type set by the accommodation merchant Hotel _2 is not obviously abnormal price, continuously determining whether the current setting data P2 is abnormal price or not by executing anchor point data of the B-type house type corresponding to the accommodation merchant Hotel _2 obtained in the step S110-S130, if the current setting data is abnormal price, displaying the current setting data on the merchant terminal in prompting colors of red, highlight and the like for the accommodation merchant Hotel _2 to view, and otherwise, displaying no prompting color on the merchant terminal.

Wherein the price adjustment condition comprises a condition threshold of fixed price adjustment or a condition threshold of abnormal price adjustment set based on the change of the historical data of the target merchant. Here, the history data is the same as or similar to the history data describing prices in the foregoing examples, and examples thereof include: historical data of the target merchant for a number of days in the near future, historical data of the target merchant for a number of days in the past year, and the like. Examples of the conditional threshold of abnormal price adjustment set according to the change of the historical data of the target merchant include: and taking the difference value between the minimum value and the maximum value of the historical data of the target merchant as a floating interval and taking the average value of the historical data of the target merchant as a central value, thereby determining the condition threshold value. For example, the condition threshold is a price interval after price adjustment formed by a price peak value (such as a holiday price) and a price valley value (such as a working day price) in the historical data of the last year.

For example, the lodging merchant Hotel _1 inputs the current setting data of the type A house as P1 yuan through the merchant terminal, and if the merchant data processing system determines that the current setting data P1 of the type A house set by the lodging merchant Hotel _1 is abnormal data according to the condition threshold, the current setting data is displayed on the merchant terminal in the prompting colors of red, highlight and the like for the lodging merchant Hotel _1 to view; otherwise, whether the current set data is an abnormal price is further determined through the steps of S110-S130, if the current set data is the abnormal price, the current set data is displayed on the merchant terminal in a prompting color of red, highlight and the like for the lodging merchant Hotel _2 to check, and if not, the prompting color is not displayed on the merchant terminal.

The merchant data processing method provided by the application selects at least one type of reference merchant according to the geographic area where the target merchant is located, obtains reference data reflecting the change trend of historical data of merchants in the geographic area by using the historical data of the selected reference merchant, and generates anchor point data aiming at the target merchant or aiming at a certain commodity of the target merchant by using the reference data so that the target merchant fills current set data according to the anchor point data to follow the data change trend of the geographic area. Therefore, the current setting data filled by the target merchant is guided to reduce the display data which can be displayed at the user side and is unconsciously stored by the target merchant, namely the current setting data stored in the database; and confirming whether the current setting data provided by the target merchant and used as the display data is abnormal data or not by utilizing the anchor data.

In accordance with the above description of the examples, an embodiment of a merchant data processing method is described with reference to a merchant including a target merchant itself, please refer to fig. 2, which is a flowchart of an example of a merchant data processing method.

In step S210, a geographic area where the target merchant is located is determined based on the location information of the target merchant included in the merchant list.

Here, the execution process of step S210 is the same as or similar to the execution process of step S110, and is not described in detail here. For example, the merchant data processing system matches the location information of the target merchant with the coverage of a preset geographic area to determine the geographic area where the target merchant is located.

In step S220, whether the current setting data belongs to the abnormal data is prompted to the corresponding target merchant based on the preset pricing/price adjusting condition.

Here, the step S220 is the same as or similar to the execution process of the step S160, and is not described in detail here. For example, the merchant data processing system obtains current setting data (for example, pricing price of house type a) filled by the target merchant, the preset pricing condition is abnormal pricing if the preset pricing condition exceeds a element, the merchant data processing system judges whether the current setting data filled by the target merchant is abnormal pricing according to the pricing condition, and if so, the merchant data processing system feeds back the merchant terminal of the target merchant to remind the target merchant of the abnormal pricing price. For another example, the merchant data processing system obtains current setting data (for example, a pricing price of house type a) filled by the target merchant, the preset price adjusting condition is that a difference between a price peak value and a price valley value exceeding the past year is an abnormal price adjusting condition, the merchant data processing system judges whether a maximum value of a price difference between the current setting data filled by the target merchant and the previous day (or the previous 7 days) exceeds the price adjusting condition according to the price adjusting condition, and if so, the merchant data processing system feeds back a merchant terminal of the target merchant to remind the target merchant of the pricing price being the abnormal data.

In step S230, the target merchant itself is used as a reference merchant, and at least one reference datum for reflecting data change of the reference merchant is determined according to the historical data of the reference merchant.

Here, the execution process of step S230 is the same as or similar to the execution process of step S120. Taking an example that a merchant data processing system counts historical data of the reference merchant based on a homonymy to obtain first reference data for reflecting a data change proportion of the reference merchant, and calculates a data change difference value of the historical data of the reference merchant to obtain second reference data for reflecting a data change deviation of the reference merchant, wherein a target merchant is an accommodation merchant, and the merchant data processing system respectively performs homonymy statistics on per-person consumption data of the target merchant in the previous 7 days and per-person consumption data of the same-period natural week in the last year to obtain a plurality of first reference data; and performing difference calculation by using the per-person consumption data of the target merchant in the previous 7 days and the per-person consumption data of the same period of the last year in nature to obtain a plurality of corresponding second reference data.

In step S240, a prediction calculation is performed according to the at least one reference data and the historical data of the target merchant. Taking the target merchant in the step S230 as an example of an accommodation merchant, the merchant data processing system performs predictive computation on yesterday per capita consumption data of the target merchant by using each first reference data corresponding to a date as a weight and using a corresponding second reference data as an offset, so as to obtain a plurality of computation results of the target merchant; the calculation results are used for generating anchor point data of each house type of the target merchant.

And step S250, evaluating the prediction accuracy of the reference merchant, that is, determining whether the reference merchant is evaluated by the prediction accuracy of the reference merchant, if so, executing step S260, otherwise, reselecting the historical data of the reference merchant, and executing step S240 again.

Here, the merchant data processing system bases the prediction reference MAPE on historical data for all merchants within the geographic area_baseFor prediction error MAPE_refPerforming prediction accuracy evaluation if prediction error MAPE_refIf the prediction error is less than the prediction reference, step S260 is executed, otherwise, the historical data of the reference merchant is reselected, and step S240 is executed again until the prediction error MAPE obtained by using different historical data_refLess than the prediction reference. Here, the prediction reference may also be a fixed value.

In step S260, anchor point data for the target merchant is generated using the plurality of calculations. Taking the target merchant as an accommodation merchant as an example, the merchant data processing system takes each calculation result as a plurality of pieces of current per-capita consumption data of the target merchant, and performs weighted calculation on the average value of the plurality of pieces of predicted current per-capita consumption data by using a coefficient related to the house type to obtain anchor point data of the corresponding house type of the target merchant. Wherein the coefficient is exemplified by the average of recent historical consumption data of the same house type of the target merchant/the average of recent historical consumption data of all merchants in the geographic area.

An embodiment of a method for merchant data processing is described with reference to merchants comprising federation-type merchants of the same brand, please refer to fig. 3, which is a flow chart of a method for merchant data processing in yet another example.

In step S310, a geographic area where the target merchant is located is determined based on the location information of the target merchant contained in the merchant list. Here, the execution process of step S310 is the same as or similar to the execution process of step S110, and is not described in detail here. For example, the merchant data processing system matches the location information of the target merchant with the coverage of a preset geographic area to determine the geographic area where the target merchant is located.

In step S320, whether the current setting data belongs to the abnormal data is prompted to the corresponding target merchant based on the preset pricing/price adjusting condition. Here, the step S320 is the same as or similar to the execution process of the steps S160 and S220, and is not described in detail here.

In step S330, determining alliance-type merchants of each brand in the geographic area where the target merchant is located according to a plurality of preset chain brands, and determining whether the obtained number of brands is multiple, if so, performing step S340, otherwise, performing step S350.

In step S340, the prediction accuracy of each brand is evaluated using historical data of a plurality of brands of federated merchants within a geographic area.

Here, the execution process of step S340 is similar to the execution process described in step S140. In an embodiment, the merchant data processing system calculates a prediction error for each brand based on historical data for each brand of allied merchants; calculating a prediction benchmark of the geographic area according to historical data of all merchants in the geographic area; and evaluating the prediction accuracy of each brand according to the deviation between the prediction reference and the prediction error of each brand. Taking the prediction error obtained by using the formula (1) as an example, the merchant data processing system determines that the prediction accuracy of the brand corresponding to the minimum deviation value is highest.

Taking the implementation of each prediction error through the formula (1) as an example, the merchant data processing system calculates a plurality of prediction errors of corresponding brands by using the historical data of the alliance type merchants of each brand; calculating a calculation prediction benchmark of the geographic area according to historical data of all merchants in the geographic area; and the merchant data processing system selects the prediction error lower than the threshold value by taking the prediction reference as the threshold value, and determines the alliance merchants of the corresponding brands according to the prediction error. In some examples, the merchant data processing system selects a league merchant for a brand that is below the prediction error of the threshold and for which the minimum prediction error value corresponds.

In step S350, reference data for reflecting data changes of at least one brand of federation-type merchants in the geographic area is determined according to the historical data of the at least one brand of federation-type merchants.

Here, the execution process of step S350 is the same as or similar to the execution process of step S120. Taking the federated merchants of one brand determined in step S340 as an example, the merchant data processing system calculates the reference data from the contemporaneous historical data of all federated merchants of the brand in the geographic area. Taking the determined federated merchants of multiple brands in step S340 as an example, the merchant data processing system calculates respective reference data from contemporaneous historical data of all federated merchants of different brands within the geographic area.

Taking the example that the merchant data processing system counts the historical data of the reference merchants based on the same proportion to obtain the reference data for reflecting the data change proportion of the reference merchants, the selected reference merchants are accommodation merchants, the historical data is the per-capita consumption price of the historical dates of the reference merchants, and the merchant data processing system selects the historical data from the historical data of all alliance type merchants of the same brand in the geographic area by utilizing the calculation mode of the current average price of the reference merchants/the previous original price of the reference merchants of about 7 days to obtain one reference data. Wherein the reference merchant's average today represents the average of the average consumption prices incurred by all league-type merchants of the brand in the geographic area on the current day (or previous day); the reference merchant's original price near 7 days ago represents the average of the consumption prices of all league-type merchants of the brand in the geographic area from the current day (or previous day) to the 7 day past. The consumption price of each person refers to the original accommodation price of each house type but not the activity price. The merchant data processing system obtains a corresponding amount of reference data based on the determined brand amount.

In step S360, anchor data of the target merchant is generated according to the reference data of the at least one brand and the history data of the target merchant.

Here, the execution process of step S360 is the same as or similar to the execution process of step S130 described above. Taking the reference data obtained according to the step S350 as an example, the target merchant is a lodging merchant, the merchant data processing system takes the reference data as a weight, performs predictive calculation on the per-capita consumption price of the target merchant in the previous 7 days to obtain at least one predicted calculation result, and determines the per-capita consumption price interval of the target merchant according to the maximum and minimum values of the calculation results; and calculating the boundary value of the per-capita consumption price interval by using the coefficient related to the house type as the weight to obtain the current price interval (namely anchor point data) of each house type of the predicted target merchant. Wherein, the coefficient related to the house type is, for example, the average of the recent historical consumption data of the house type of the target merchant/the average of the recent historical consumption data of all merchants in the geographic area. Taking the multiple reference data obtained according to step S350 as an example, the merchant data processing system respectively performs the predictive calculation with each reference data as a weight to obtain the anchor point data of the target merchant, which is not described in detail herein.

In yet another embodiment, an embodiment of a merchant data processing method is described with reference to a merchant comprising a tender merchant, please refer to fig. 4, which shows a flow chart of the merchant data processing method in another example.

In step S410, a geographic area where each target merchant is located is determined based on each location information of the target merchants included in the merchant list. Here, the execution process of step S410 is the same as or similar to the execution process of step S110, and is not described in detail here. For example, the merchant data processing system matches the location information of the target merchant with the coverage of a preset geographic area to determine the geographic area where the target merchant is located.

In step S420, whether the current setting data belongs to the abnormal data is prompted to the corresponding target merchant based on the preset pricing/price adjusting condition. Here, the step S420 is the same as or similar to the execution process of the steps S160, S220 and S320, and is not described in detail here.

In step S430, a bidding merchant of the target merchant is selected from the location geographic area with reference to the historical data of the target merchant. Here, the execution process of step S430 is the same as or similar to the execution process of the corresponding part in step S120. In an embodiment, the merchant data processing system determines the targeted merchant of the targeted merchant according to the statistical deviation between the historical data of the targeted merchant and other merchants in the geographic area in the same time period.

In an embodiment, the merchant data processing system selects the historical data of the same period from the target merchant and all merchants in the geographic area according to dates, analyzes the historical data respectively, and selects the benchmarking merchant from the geographic area within a preset selection condition on the basis of the analysis result of the target merchant. The selection condition is, for example, an interval divided based on the analysis result of the target merchant. Wherein, the analysis mode includes but not limited to at least one of the following: and performing calculation processing such as weighted average, use frequency, probability distribution and the like on the historical data. For example, the historical data is numerical data (such as the check-in price), the merchant data processing system obtains an average value of the contemporaneous historical data of each merchant through average value calculation, a mean value interval is defined according to the average value of the target merchant, and the merchant with the mean value falling into the mean value interval is used as the benchmarking merchant. For another example, the merchant data processing system respectively performs mean calculation on historical data of the target merchant and other merchants in the geographic area in the last month to obtain a mean value of the historical data of each merchant in the last month; and taking the historical data mean of the target merchant as a reference, obtaining a historical data mean interval according to the preset up-down floating deviation, and taking other merchants of which the historical data mean corresponding to other merchants falls into the historical data mean interval as benchmarking merchants.

In step S440, reference data reflecting data changes of the corresponding bidding merchant is determined according to the historical data of the bidding merchant. Here, the execution process of step S440 is the same as or similar to the execution process of step S120. For example, the merchant data processing system counts the historical data of all the bidding merchants based on the same ratio to obtain the reference data for reflecting the data change proportion of all the bidding merchants.

In step S450, anchor point data of the target merchant is generated according to the reference data of the targeting merchant and the history data of the target merchant. Here, the execution process of step S450 is the same as or similar to the execution process of step S130. Taking a target merchant as an accommodation merchant as an example, the merchant data processing system takes reference data as weight, carries out prediction calculation on the per-capita consumption price of the target merchant in the previous 7 days to obtain at least one predicted calculation result, and takes the average value of the calculation results as the per-capita consumption price of the target merchant; and calculating the mean value of the calculation result by using the coefficient related to the house type as the weight to obtain the current price data of each house type of the prediction target merchant. Wherein, the coefficient related to the house type is, for example, the average of the recent historical consumption data of the house type of the target merchant/the average of the recent historical consumption data of all merchants in the geographic area.

For example, referring to fig. 5, a flow diagram of a merchant data processing system in yet another example is shown, with reference to at least two of the merchants including a federation-type merchant of the same brand, the target merchant itself, and a target merchant's partner merchant in the geographic area.

In step S510, a geographic area where each target merchant is located is determined based on each location information of the target merchants included in the merchant list. Here, the execution process of step S510 is the same as or similar to the execution process of step S110, and is not described in detail here. For example, the merchant data processing system matches the location information of the target merchant with the coverage of a preset geographic area to determine the geographic area where the target merchant is located.

In step S520, whether the current setting data belongs to the abnormal data is prompted to the corresponding target merchant based on the preset pricing/price adjusting condition. Here, the step S520 is the same as or similar to the execution process of the steps S160, S220, and S320, and is not described in detail here.

In step S530, the prediction accuracy of each type of reference merchant is evaluated by using the historical data of the plurality of types of reference merchants in the geographic area.

Here, the execution process of step S530 is similar to the execution process of step S140. In the embodiment, the merchant data processing system calculates prediction errors of various reference merchants according to historical data of various reference merchants; each prediction error is evaluated to determine the prediction accuracy of the historical data for each reference merchant. The historical data of the calculated prediction error can be selected from the historical data of the last month, so that the reference merchants reflecting the data change of the corresponding period can be conveniently selected by the merchant data processing system when the scheme is executed at different periods, and the selected reference merchants can provide the reference data with higher prediction accuracy or the anchor data.

Taking the example of respectively calculating the prediction errors of various reference merchants by using the formula (1), the merchant data processing system calculates the prediction errors of various reference merchants, selects the reference merchant type corresponding to the minimum value of the prediction errors, and executes the steps S540-S550 by using the historical data of the reference merchants of the corresponding type.

In step S540, at least one type of reference data for reflecting data changes of at least one type of reference merchant is determined according to historical data of the at least one type of reference merchant in the geographic area.

In step S550, anchor point data of the target merchant is generated according to the at least one type of reference data and the history data of the target merchant. The steps S540 and S550 correspond to the steps S120 and S130, and are not described in detail herein.

When the reference merchant selected in the step S530 is the target merchant itself, the merchant data processing system may specifically execute the steps S230-S260; when the reference merchant selected in the step S530 is a coalition-type merchant, the merchant data processing system may specifically perform steps S350-S360; when the reference merchant selected in said step S530 is the bidding merchant, the merchant data processing system may specifically perform steps S440-S450. And will not be described in detail herein.

It should be noted that the calculation manner of each anchor point data illustrated in fig. 2 to 5 is only an example and not a limitation. When a plurality of parameter data or historical data of a plurality of target merchants are used for calculation, the merchant data processing system can generate a predicted value or a predicted interval according to the calculation result, and the corresponding predicted value or the predicted interval can be used as anchor point data.

It should be further noted that each anchor point data obtained according to the above fig. 2 to 5 is also sent to the merchant terminal of the corresponding target merchant, so that the target merchant can adjust the current setting data filled by the anchor point data, and submit the corresponding current setting data after the completion of the filling is determined, and the anchor point data is stored in the database as the display data by the merchant data processing system. When the user terminal at the user side requests to display the detail information/overview information of the target merchant, the display data is displayed in the interface of the user terminal.

Please refer to fig. 6, which is a schematic diagram of an architecture of a merchant data processing system according to the present application. The merchantdata processing system 6 comprises: a businesscircle processing module 61 and a business data processing module 62. Wherein, the execution process of the businesscircle processing module 61 corresponds to the execution process of the step S110; the execution of the merchant data processing module 62 corresponds to the execution of steps S120-S160 as shown in fig. 1, steps S220-S260 as shown in fig. 2, steps S320-S360 as shown in fig. 3, steps S420-450 as shown in fig. 4, or steps S520-S560 as shown in fig. 5. And will not be described in detail herein.

The application also provides a cloud server system. Referring to fig. 7, which is a schematic structural diagram of the cloud server system according to an embodiment of the present application, as shown in the drawing, thecloud server system 7 according to the present application includes: at least onememory device 71, at least oneprocessing device 72, and at least oneinterface device 73.

In an embodiment, the cloud server system is, for example, a computer device loaded with an application program or having a web page/website access capability.

The at least one storage device is used for storing at least one program; in an embodiment, the storage device includes high speed random access memory, and may also include non-volatile memory, such as one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid state storage devices. In certain embodiments, the storage device may also include a storage server remote from the one or more processors, such as a network-attached storage accessed via RF circuitry or external ports and a communications network, which may be the internet, one or more intranets, local area networks, wide area networks, storage area networks, and the like, or suitable combinations thereof. The memory controller may control access to the memory by other components of the device, such as the CPU and peripheral interfaces.

In an embodiment, the at least one processing device is communicatively connected to the at least one storage device, and is configured to execute and implement at least one embodiment described in the above merchant data processing method when running the at least one program, such as the embodiments described in fig. 1 to 5. In an embodiment, the processing device is operatively coupled with a memory and/or a non-volatile storage device. More specifically, the processing device may execute instructions stored in the storage device and/or the non-volatile storage device to perform operations in the computing device, such as generating and/or transmitting anchor data to the merchant terminal. As such, the processor may include one or more general purpose microprocessors, one or more special purpose processors, one or more field programmable logic arrays, or any combination thereof.

The at least one interface device is used for being in communication connection with merchant terminals of all target merchants, and includes but is not limited to: a network module including a network card, a wireless network module for transmitting data by using a mobile network, and the like.

The present application also provides a computer-readable-writable storage medium storing a computer program that, when executed, implements at least one embodiment described above for a merchant data processing method, such as the embodiment described in any of fig. 1-5.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application.

In the embodiments provided herein, the computer-readable and writable storage medium may include read-only memory, random-access memory, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, flash memory, a USB flash drive, a removable hard disk, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection is properly termed a computer-readable medium. For example, if the instructions are transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. It should be understood, however, that computer-readable-writable storage media and data storage media do not include connections, carrier waves, signals, or other transitory media, but are intended to be non-transitory, tangible storage media. Disk and disc, as used in this application, includes Compact Disc (CD), laser disc, optical disc, Digital Versatile Disc (DVD), floppy disk and blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers.

In one or more exemplary aspects, the functions described in the computer program of the methods described herein may be implemented in hardware, software, firmware, or any combination thereof. When implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. The steps of a method or algorithm disclosed herein may be embodied in a processor-executable software module, which may be located on a tangible, non-transitory computer-readable and/or writable storage medium. Tangible, non-transitory computer readable and writable storage media may be any available media that can be accessed by a computer.

The flowcharts and block diagrams in the figures described above of the present application illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The above embodiments are merely illustrative of the principles and utilities of the present application and are not intended to limit the application. Any person skilled in the art can modify or change the above-described embodiments without departing from the spirit and scope of the present application. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical concepts disclosed in the present application shall be covered by the claims of the present application.

Claims (37)

1. A merchant data processing method is characterized by comprising the following steps:

determining a geographical area where a target merchant is located based on the position information of the target merchant contained in the merchant list;

determining at least one type of reference data for reflecting data changes of at least one type of reference merchant according to historical data of the at least one type of reference merchant in the geographic area;

generating anchor point data of the target merchant according to the at least one type of reference data and the historical data of the target merchant; the anchor point data is used for the target merchant to adjust the current setting data into display data which accords with the data change rule corresponding to the at least one type of reference data.

2. The merchant data processing method of claim 1, further comprising: and screening the attribute data of each merchant in the merchant list to obtain each target merchant in the geographical area to be determined in the merchant list.

3. The merchant data processing method of claim 2, wherein each of the obtained targeted merchants comprises: and screening out the target merchants based on preset grade conditions.

4. The merchant data processing method of claim 1, further comprising: and prompting whether the current set data belongs to abnormal data or not to the corresponding target merchant based on the preset pricing/price adjusting condition.

5. The merchant data processing method of claim 1, wherein the step of determining the geographic area in which the target merchant is located based on the location information of the target merchant contained in the merchant list comprises at least one of:

determining a preset geographical area where the corresponding target merchant is located according to the coverage relation between at least one preset geographical area and the position information of each target merchant; or

And generating the geographical area of each target merchant in the merchant list according to the position distance between each merchant in the merchant list.

6. The merchant data processing method of claim 1, wherein the at least one type of reference merchant comprises at least one of: a federation-type merchant of the same brand, the target merchant itself, or a counterpoint merchant of the target merchant in the geographic region.

7. The merchant data processing method of claim 1, wherein the step of determining reference data reflecting data changes of reference merchants within the geographic area based on historical data of the reference merchants comprises:

carrying out geometric statistics or ring ratio statistics on the historical data of the reference merchant to obtain first reference data for reflecting the data change proportion of the reference merchant; and/or

And calculating a data change difference value of the historical data of the reference merchant to obtain second reference data for reflecting the data change deviation of the reference merchant.

8. The merchant data processing method of claim 7, wherein the step of generating anchor data for the target merchant based on at least one type of reference data and historical data for the target merchant comprises:

and calculating the historical data of the target merchant by taking the first reference data as a proportionality coefficient and the second reference data as an offset to generate anchor data of the target merchant.

9. The merchant data processing method of claim 1, wherein the step of determining at least one type of reference data reflecting data changes of at least one type of reference merchant from historical data of the at least one type of reference merchant within the geographic area comprises:

determining reference data reflecting data changes of the reference merchant by using the historical data of the reference merchant related to the date attribute of the target merchant.

10. The merchant data processing method of claim 1, wherein the historical data comprises at least one of: historical data of a cross-year or cross-month synchronization, historical data determined based on holidays, and historical data of a preset duration interval.

11. The merchant data processing method of claim 1, further comprising: and evaluating the prediction accuracy of various reference merchants by utilizing the historical data of the various reference merchants in the geographic area.

12. The merchant data processing method of claim 11, wherein the step of using historical data of a plurality of types of reference merchants within the geographic area to evaluate the accuracy of the predictions of the plurality of types of reference merchants comprises:

calculating prediction errors of various reference merchants according to historical data of the various reference merchants;

each prediction error is evaluated to determine the prediction accuracy of the historical data for each reference merchant.

13. The merchant data processing method of claim 12, wherein the prediction error is based on a MAPE algorithm.

14. The merchant data processing method of claim 1, wherein the anchor data comprises at least one of: the current prediction average value of the target merchant, the current prediction average interval of the target merchant, the current prediction anchor point value of the corresponding service type in the target merchant, or the current prediction anchor point interval of the corresponding service type in the target merchant.

15. A merchant data processing method is characterized by comprising the following steps:

determining a geographical area where a target merchant is located based on the position information of the target merchant contained in the merchant list;

determining reference data for reflecting data changes of at least one brand of allied merchant in the geographic area according to historical data of the at least one brand of allied merchant;

generating anchor data of the target merchant according to the reference data of the at least one brand and the historical data of the target merchant; the anchor point data is used for the target merchant to adjust the current setting data into display data which accords with the data change rule corresponding to the at least one reference data.

16. The merchant data processing method of claim 15, further comprising: and screening the display data of each merchant in the merchant list to obtain each target merchant in the geographical area to be determined in the merchant list.

17. The merchant data processing method of claim 16, wherein each of the obtained targeted merchants comprises: and screening out the target merchants based on preset grade conditions.

18. The merchant data processing method of claim 15, further comprising: and prompting whether the current set data belongs to abnormal data or not to the corresponding target merchant based on the preset pricing/price adjusting condition.

19. The merchant data processing method of claim 15, wherein the step of determining the geographic area in which the target merchant is located based on the location information of the target merchant contained in the merchant list comprises:

determining a preset geographical area where the corresponding target merchant is located according to the coverage relation between at least one preset geographical area and the position information of each target merchant; or

And generating the geographical area of each target merchant in the merchant list according to the position distance between each merchant in the merchant list.

20. The merchant data processing method of claim 15, further comprising:

and evaluating the prediction accuracy of each brand by utilizing historical data of a plurality of brands of union type merchants in the geographic area.

21. The merchant data processing method of claim 20, wherein the step of evaluating the accuracy of the predictions for each brand using historical data for a plurality of brands of federated merchants within the geographic area comprises:

calculating the prediction error of each brand according to the historical data of the alliance type merchants of each brand;

and evaluating the prediction accuracy of the historical data of each brand according to the deviation degree between the preset prediction reference and each prediction error.

22. The merchant data processing method of claim 21, wherein the prediction error is based on a MAPE algorithm.

23. The merchant data processing method of claim 20, wherein the step of determining reference data reflecting data changes for at least one brand of federated merchants within the geographic area based on historical data for the respective brand of federated merchants comprises: determining reference data reflecting data changes of the alliance type merchants of the corresponding brand by utilizing historical data of the alliance type merchants of the selected brand related to the date attribute of the target merchant.

24. The merchant data processing method of claim 15, wherein the anchor data comprises at least one of: the current prediction average value of the target merchant, the current prediction average interval of the target merchant, the current prediction anchor point value of the corresponding service type in the target merchant, or the current prediction anchor point interval of the corresponding service type in the target merchant.

25. A merchant data processing method is characterized by comprising the following steps:

determining a geographical area where each target merchant is located based on each position information of the target merchants contained in the merchant list;

selecting a benchmarking merchant of the target merchant from a location geographic area by referring to the historical data of the target merchant;

determining reference data for reflecting data changes of corresponding bidding merchants according to the historical data of the bidding merchants;

generating anchor point data of the target merchant according to the reference data of the benchmarking merchant and the historical data of the target merchant; the anchor point data is used for the target merchant to adjust the current setting data into display data which accords with the data change rule corresponding to the reference data.

26. The merchant data processing method of claim 25, further comprising: and screening the display data of each merchant in the merchant list to obtain each target merchant in the geographical area to be determined in the merchant list.

27. The merchant data processing method of claim 26, wherein each of the obtained targeted merchants comprises: and screening out the target merchants based on preset grade conditions.

28. The merchant data processing method of claim 25, further comprising: and prompting whether the current set data belongs to abnormal data or not to the corresponding target merchant based on the preset pricing/price adjusting condition.

29. The merchant data processing method of claim 25, wherein the step of determining the geographic area in which the target merchant is located based on the location information of the target merchant contained in the merchant list comprises:

determining a preset geographical area where the corresponding target merchant is located according to the coverage relation between at least one preset geographical area and the position information of each target merchant; or

And generating the geographical area of each target merchant in the merchant list according to the position distance between each merchant in the merchant list.

30. The merchant data processing method of claim 25, wherein the step of selecting the targeted merchant's targeted merchant from a location geographic area based on the targeted merchant's historical data comprises:

and determining the target merchant of the target merchant according to the statistical deviation between the historical data of the target merchant and other merchants in the geographic area in the same time period.

31. The merchant data processing method of claim 25, wherein the step of determining reference data reflecting changes in data of the corresponding bidding merchant based on the historical data of the bidding merchant comprises:

and determining corresponding reference data by utilizing historical data of the targeted merchant related to the date attribute of the targeted merchant.

32. The merchant data processing method of claim 25, wherein the anchor data comprises at least one of: the service type prediction method comprises the steps of predicting average values of target merchants, predicting average intervals of the target merchants, predicting service anchor point values of corresponding service types in the target merchants, or predicting service anchor point intervals of corresponding service types in the target merchants.

33. A merchant data processing system, comprising:

the business circle processing module is used for determining the geographic area where the target merchant is located based on the position information of the target merchant contained in the merchant list;

the merchant data processing module is used for determining at least one type of reference data for reflecting data changes of at least one type of reference merchant according to historical data of the at least one type of reference merchant in the geographic area; generating anchor point data of the target merchant according to the at least one type of reference data and the historical data of the target merchant;

the anchor point data is used for the target merchant to adjust the current setting data into display data which accords with the data change rule corresponding to the at least one type of reference data.

34. A merchant data processing system, comprising:

the business circle processing module is used for determining the geographic area where the target merchant is located based on the position information of the target merchant contained in the merchant list;

the merchant data processing module is used for determining reference data for reflecting data changes of at least one brand of alliance type merchants in the geographic area according to the historical data of the at least one brand of alliance type merchants; generating anchor data of the target merchant according to the reference data of the at least one brand and the historical data of the target merchant;

the anchor point data is used for the target merchant to adjust the current setting data into display data which accords with the data change rule corresponding to the at least one reference data.

35. A merchant data processing system, comprising:

the business circle processing module is used for determining the geographic area where the target merchant is located based on the position information of the target merchant contained in the merchant list;

the bidding merchant determining module is used for selecting the bidding merchant of the target merchant from the location geographic area according to the historical data of the target merchant; determining reference data for reflecting data changes of corresponding bidding merchants according to the historical data of the bidding merchants; generating anchor point data of the target merchant according to the reference data of the benchmarking merchant and the historical data of the target merchant;

the anchor point data is used for the target merchant to adjust the current setting data into display data which accords with the data change rule corresponding to the reference data.

36. A cloud server system, comprising:

at least one storage device for storing at least one program;

at least one processing device coupled to the storage device for executing and implementing the merchant data processing method of any of claims 1 to 14, 15-24, or 25-32 when executing the at least one program.

37. A computer-readable storage medium, characterized by at least one program stored thereon which, when executed by a processor, performs and implements a merchant data processing method as claimed in any one of claims 1 to 14, 15-24, or 25-32.

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