CN112488748B - Data identification matching method and device, storage medium and computing equipment - Google Patents
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Abstract
A data identification matching method and device, a storage medium and a computing device, wherein the method comprises the following steps: acquiring transaction flow data and advertisement putting data, wherein the transaction flow data comprises transaction time, transaction merchant information and transaction user identification, and the advertisement putting data comprises putting time, equipment position information and equipment identification of advertisement putting; determining the trade business circle of each trade flow record according to the trade business information; determining a delivery business circle of each advertisement delivery record according to the equipment position information; and according to the matching relationship between the transaction business circles and the delivery business circles and between the transaction time and the delivery time, the transaction flow data and the advertisement delivery data are analyzed to determine the matching relationship between the transaction user identification and the equipment identification. By the technical scheme of the invention, different data with mutually independent sources can be associated or matched.
Description
Technical Field
The invention relates to the technical field of big data analysis, in particular to a data identification matching method and device, a storage medium and computing equipment.
Background
In the field of big data analysis at present, massive amounts of data are collected. In practical applications, however, data collection is often performed on multiple data sources that are independent of each other, which results in data that often cannot be matched or correlated due to the existence of data isolation. For example, data from different data sources are isolated from each other, and even data representing the same user or the same object often cannot be correlated or matched due to different data sources, which makes subsequent data analysis difficult. For example, when the consumption conversion situation of advertisement delivery is analyzed by using advertisement delivery data and transaction flow data, how many users of users delivered with advertisements are required to be analyzed to perform consumption related to the delivered advertisements, but because the advertisement delivery data generated by advertisement delivery of merchants and the transaction flow data generated by user consumption are usually generated on different platforms, the data sources are different, so that the transaction flow data and the advertisement delivery data cannot be directly related or matched, and thus the consumption conversion situation of obtaining advertisement delivery cannot be performed.
There is a need for a data identity matching method that can correlate or match different data that has data isolation for subsequent data analysis.
Disclosure of Invention
The invention solves the technical problem of how to associate or match different data with mutually independent sources.
In order to solve the above technical problems, an embodiment of the present invention provides a data identifier matching method, where the method includes: acquiring transaction flow data and advertisement delivery data, wherein the transaction flow data comprises a plurality of transaction flow records, the transaction flow records comprise transaction time, transaction merchant information and transaction user identification, the advertisement delivery data comprises a plurality of advertisement delivery records, and the advertisement delivery records comprise the delivery time of advertisement delivery, the equipment position information and the equipment identification of user equipment to which the advertisement is delivered; determining the trade business circle of each trade flow record according to the trade business information; determining a delivery business circle of each advertisement delivery record according to the equipment position information, wherein the delivery business circle is a business circle to which user equipment for delivering advertisements in the advertisement delivery records belongs; and analyzing the transaction flow data and the advertisement delivery data according to the matching relationship between the transaction business circles and the delivery business circles and the transaction time and the delivery time so as to determine the matching relationship between the transaction user identification and the equipment identification.
Optionally, the determining the transaction business circle of each transaction flow record according to the transaction business information includes: acquiring preset business circle data, wherein the preset business circle data comprises business information of a plurality of business circles; for each transaction flow record, reading transaction merchant information of the transaction flow record; searching merchant information which is the same as the transaction merchant information in the preset business district data; and marking the business circle to which the found merchant information belongs as the transaction business circle of the transaction flow record.
Optionally, before acquiring the preset business district data, the method further includes: acquiring a preset non-transaction time period; and deleting the transaction flow records of which the transaction time falls into the preset non-transaction time period in the transaction flow data.
Optionally, determining the delivery business circles of each advertisement delivery record according to the device position information includes: acquiring preset business district data, wherein the preset business district data comprises position information of a plurality of business districts; for each advertisement putting record, reading the equipment position information of the user equipment to which the advertisement is put; searching a business district with a distance smaller than or equal to a preset distance from the preset business district data according to the equipment position information of the user equipment to be advertised and the position information of the business district; and marking the found business circles as the advertisement putting business circles of the advertisement putting records.
Optionally, according to a matching relationship between the transaction business circles and the delivery business circles, and between the transaction time and the delivery time, analyzing the transaction flow data and the advertisement delivery data to determine the matching relationship between the transaction user identifier and the device identifier includes: processing the transaction flow data according to the transaction business turn to obtain first processing data, wherein each data item in the first processing data comprises a corresponding transaction business turn, transaction time and transaction user identification of the transaction flow record; processing the advertisement delivery data according to the delivery business circle to obtain second processing data, wherein each data item in the second processing data comprises a delivery business circle, delivery time and equipment identifier of a corresponding advertisement delivery record; and analyzing the first processing data and the second processing data, and if the transaction business circle of the transaction flow record is the same as the delivery business circle of the advertisement delivery record and the time difference between the transaction time of the transaction flow record and the delivery time of the advertisement delivery record is less than or equal to a preset time difference, determining that the transaction user identification of the transaction flow record is matched with the equipment identification of the advertisement delivery record.
Optionally, the analyzing the first processing data and the second processing data includes: step A, setting a matching list, wherein the matching list comprises a plurality of sub-tables, and the sub-tables are in one-to-one correspondence with business circles; step B, each data item in the first processing data or the second processing data is read in time sequence, wherein the data item comprises time, a business circle and an identifier, the time is transaction time of transaction or the putting time of advertisement putting, the business circle is transaction business circle of transaction or the putting business circle of advertisement putting, and the identifier is transaction user identifier of transaction or equipment identifier of advertisement putting; step C, determining a sub-table to be added into the data item according to a business district in the data item; step D, calculating the time difference between the time of the currently read data item and the time of the current first data item in the sub-table; e, if the time difference is smaller than or equal to the preset time difference, adding the time and the mark in the data item into the sub-table, and returning to the step B; and F, if the time difference is larger than the preset time difference, determining that the transaction user identification and the equipment identification of each data item in the sub-table are mutually matched, emptying the sub-table, adding the time and the identification in the data item into the sub-table, and returning to the step B.
Optionally, the method further comprises: and counting the matching times of each transaction user identifier and each device identifier.
Optionally, the method further comprises: analyzing the transaction flow data to determine a plurality of transaction users, each transaction user having a corresponding transaction user identification; determining the equipment of the transaction user according to the matching relation between the transaction user identification and the equipment identification; and putting advertisements to the equipment of the transaction user according to the equipment identification of the transaction user.
The embodiment of the invention also provides a data identifier matching device, which comprises: the receiving module is used for acquiring transaction flow data and advertisement delivery data, wherein the transaction flow data comprises a plurality of transaction flow records, the transaction flow records comprise transaction time, transaction merchant information and transaction user identification, the advertisement delivery data comprises a plurality of advertisement delivery records, and the advertisement delivery records comprise the delivery time of advertisement delivery, the equipment position information and the equipment identification of user equipment to which the advertisement is delivered; the first determining module is used for determining the transaction business circles of each transaction flow record according to the transaction business information; the second determining module is used for determining a delivery business circle of each advertisement delivery record according to the equipment position information, wherein the delivery business circle is a delivery business circle to which the user equipment for delivering advertisements in the advertisement delivery records belongs; and the matching module is used for analyzing the transaction flow data and the advertisement delivery data according to the matching relation between the transaction business circles and the delivery business circles and the transaction time and the delivery time so as to determine the matching relation between the transaction user identification and the equipment identification.
The embodiment of the invention also provides a storage medium, on which a computer program is stored, which when being executed by a processor performs the steps of the above-mentioned data identification matching method.
The embodiment of the invention also provides a computing device, which comprises a memory and a processor, wherein the memory stores a computer program capable of running on the processor, and the processor executes the steps of the data identification matching method when running the computer program.
Compared with the prior art, the technical scheme of the embodiment of the invention has the following beneficial effects:
the embodiment of the invention provides a data identification matching method, which comprises the following steps: acquiring transaction flow data and advertisement delivery data, wherein the transaction flow data comprises a plurality of transaction flow records, the transaction flow records comprise transaction time, transaction merchant information and transaction user identification, the advertisement delivery data comprises a plurality of advertisement delivery records, and the advertisement delivery records comprise the delivery time of advertisement delivery, the equipment position information and the equipment identification of user equipment to which the advertisement is delivered; determining a transaction business circle of the transaction flow record according to the transaction business information; determining a delivery business circle of the advertisement delivery record according to the equipment position information, wherein the delivery business circle is a business circle to which user equipment for delivering advertisements in the advertisement delivery record belongs; and analyzing the transaction flow record and the advertisement delivery record according to the matching relationship between the transaction business circles and the delivery business circles and between the transaction time and the delivery time so as to determine the matching relationship between the transaction user identification of the transaction flow record and the equipment identification of the advertisement delivery record. According to the scheme provided by the embodiment of the invention, the third party platform can analyze the transaction flow record generated by user consumption and the advertisement delivery record generated by advertisement delivery of the commercial, obtain the business circle of the transaction user when the transaction flow record is generated, namely the transaction business circle, through the transaction commercial information in the transaction flow record, and obtain the business circle of the user equipment for delivering the advertisement when the advertisement delivery record is generated, namely the delivery business circle through the equipment position information in the advertisement delivery record, and if the transaction business circle of the transaction is matched with the delivery business circle of the advertisement delivery, and the transaction time is matched with the delivery time, the corresponding transaction user identification and the equipment identification can be considered to be matched. Through multiple matching operations of transaction user identifiers of multiple transaction flow records and device identifiers of multiple advertisement delivery records, the reliability of the obtained matching relationship between the transaction user identifiers and the device identifiers is higher and higher, so that the matching relationship can be close to a real matching relationship.
Further, in the embodiment of the present invention, a matching list may be set, where the matching list includes multiple sub-tables, the sub-tables correspond to business circles one by one, data items of the first processing data or the second processing data are sequentially read according to time, the business circle in the data items determines that the sub-table to which the data item is to be added, so that it may be determined that a transaction user identifier and a device identifier in each sub-table correspond to the same business circle, if a time difference between a time of a currently read data item and a time of a currently first data item in the sub-table is greater than the preset time difference, it may be determined that transaction user identifiers and device identifiers of all data items in the sub-tables are mutually matched, so that a matching relationship between the transaction user identifier and the device identifier in the current sub-table may be obtained, then the sub-table is emptied, the time and identifiers in the data items are added into the sub-table, and then a next data item is read, and a continuous cycle is performed, thereby, it may be determined that a correspondence between the transaction user identifiers of a plurality of transaction flow records and advertisement delivery identifiers of a plurality of advertisement records may be determined. This way of determination is efficient and takes less time.
Further, as the matching relationship between the transaction user identifier and the device identifier is obtained in the scheme of the embodiment of the invention, a specific transaction user to which the advertisement is put can be determined by analyzing transaction flow data before the advertisement is put, and then the device identifier of the transaction user is determined according to the transaction user identifier of the transaction user, so that the advertisement can be put to the transaction user.
Drawings
Fig. 1 is a flowchart of a data identifier matching method in an embodiment of the present invention.
FIG. 2 is a schematic diagram of a method for determining a geohash encoded coordinate set for a business turn in accordance with an embodiment of the present invention.
Fig. 3 is a flowchart of an embodiment of step S104 in fig. 1.
Fig. 4 is a flowchart of an embodiment of step S303 in fig. 3.
Fig. 5 is a schematic structural diagram of a data identifier matching device in an embodiment of the present invention.
Detailed Description
As noted in the background, in the field of big data analysis at present, massive amounts of data are collected. In practical applications, however, data collection is often performed on multiple data sources that are independent of each other, which results in data that often cannot be matched or correlated due to the existence of data isolation. For example, data from different data sources are isolated from each other, and even data representing the same user or the same object often cannot be correlated or matched due to different data sources, which makes subsequent data analysis difficult. For example, when the consumption conversion situation of advertisement delivery is analyzed by using advertisement delivery data and transaction flow data, how many users of users delivered with advertisements are required to be analyzed to perform consumption related to the delivered advertisements, but because the advertisement delivery data generated by advertisement delivery of merchants and the transaction flow data generated by user consumption are usually generated on different platforms, the data sources are different, so that the transaction flow data and the advertisement delivery data cannot be directly related or matched, and thus the consumption conversion situation of obtaining advertisement delivery cannot be performed.
In order to solve the above technical problems, an embodiment of the present invention provides a data identifier matching method, where the method includes: acquiring transaction flow data and advertisement delivery data, wherein the transaction flow data comprises a plurality of transaction flow records, the transaction flow records comprise transaction time, transaction merchant information and transaction user identification, the advertisement delivery data comprises a plurality of advertisement delivery records, and the advertisement delivery records comprise the delivery time of advertisement delivery, the equipment position information and the equipment identification of user equipment to which the advertisement is delivered; determining a transaction business circle of the transaction flow record according to the transaction business information; determining a delivery business circle of the advertisement delivery record according to the equipment position information, wherein the delivery business circle is a business circle to which user equipment for delivering advertisements in the advertisement delivery record belongs; and analyzing the transaction flow record and the advertisement delivery record according to the matching relationship between the transaction business circles and the delivery business circles and between the transaction time and the delivery time so as to determine the matching relationship between the transaction user identification of the transaction flow record and the equipment identification of the advertisement delivery record. According to the scheme provided by the embodiment of the invention, the third party platform can analyze the transaction flow record generated by user consumption and the advertisement delivery record generated by advertisement delivery of the commercial, obtain the business circle of the transaction user when the transaction flow record is generated, namely the transaction business circle, through the transaction commercial information in the transaction flow record, and obtain the business circle of the user equipment for delivering the advertisement when the advertisement delivery record is generated, namely the delivery business circle through the equipment position information in the advertisement delivery record, and if the transaction business circle of the transaction is matched with the delivery business circle of the advertisement delivery, and the transaction time is matched with the delivery time, the corresponding transaction user identification and the equipment identification can be considered to be matched. Through multiple matching operations of transaction user identifiers of multiple transaction flow records and device identifiers of multiple advertisement delivery records, the reliability of the obtained matching relationship between the transaction user identifiers and the device identifiers is higher and higher, so that the matching relationship can be close to a real matching relationship.
In order to make the above objects, features and advantages of the present invention more comprehensible, embodiments accompanied with figures are described in detail below.
Referring to fig. 1, fig. 1 is a flowchart of a data identification matching method that may be performed by a third party platform coupled to a transaction server and an advertisement delivery server in an embodiment of the present invention. The transaction server is used for providing transaction-related settlement service and recording transaction-related data, and the advertisement putting server is used for providing advertisement putting data service and recording advertisement putting related data. The third party platform can acquire transaction flow data from the transaction server and can acquire advertisement delivery data from the advertisement delivery server.
The data identification matching method described in fig. 1 may include the following steps:
step S101, transaction flow data and advertisement delivery data are obtained, wherein the transaction flow data comprise a plurality of transaction flow records, the transaction flow records comprise transaction time, transaction merchant information and transaction user identification, the advertisement delivery data comprise a plurality of advertisement delivery records, and the advertisement delivery records comprise the delivery time of advertisement delivery, the equipment position information and the equipment identification of user equipment to which the advertisement is delivered;
Step S102, determining the trade business circles of each trade flow record according to the trade business information;
step S103, determining a putting business circle of each advertisement putting record according to the equipment position information, wherein the putting business circle is a business circle to which user equipment to be put with advertisements in the advertisement putting records belongs;
and step S104, analyzing the transaction flow data and the advertisement delivery data according to the matching relation between the transaction business circles and the delivery business circles and the transaction time and the delivery time so as to determine the matching relation between the transaction user identification and the equipment identification.
In the implementation of step S101, transaction flow data and advertisement delivery data are obtained, where the transaction flow data includes a plurality of transaction flow records, the transaction flow records include transaction time, transaction merchant information and transaction user identification, and the advertisement delivery data includes a plurality of advertisement delivery records, and the advertisement delivery records include delivery time of advertisement delivery, device location information of user devices to which advertisements are delivered, and device identification.
In particular, the third party platform may obtain transaction flow data that includes transaction flow records between a plurality of transaction merchants (e.g., stores, restaurants, etc.) and a plurality of transaction users (e.g., card-holding individuals, etc.) that are generated as a result of transaction activity. The transaction flow data can be stored in the third party platform or can be stored outside the third party platform, and the third party platform can be obtained through network access and the like. Typically, a transaction merchant and a transaction user make a transaction flow record, but not limited thereto.
The third party platform may obtain specific content for each transaction by reading each transaction flow record, including but not limited to: transaction time, transaction merchant information, transaction user identification.
The transaction time may be a time at which the transaction merchant and the transaction user conduct a transaction. The transaction merchant information can be merchant identification, merchant terminal identification and merchant name, or other information which can uniquely determine the transaction merchant. The transaction user identification can be a bank account number of the transaction user, or can be other information which can uniquely determine the transaction user.
The third party platform can also acquire advertisement delivery data, wherein the advertisement delivery data comprises advertisement delivery records generated by advertisement delivery behaviors between a plurality of commercial tenants delivering advertisements and a plurality of users delivering advertisements. The advertisement putting record can be stored in the third party platform, can also be stored outside the third party platform, and can be obtained by network access and the like. Typically, a merchant will make an advertisement delivery record to a user device, but is not limited thereto.
The third party platform may obtain specific content for each advertisement delivery by reading each advertisement delivery record, including but not limited to: the time of putting the advertisement, the equipment position information and the equipment identification of the user equipment to which the advertisement is put.
The advertisement delivery time may be a time when the merchant delivers the advertisement to the user device. The device location information of the user device to which the advertisement is placed may be information of a location where the user device is located when the merchant places the advertisement to the user device, and the location information may be coordinate information or other information capable of determining the location where the user device is located. The device identifier may be a device ID of the user device to which the advertisement is to be placed, or may be other information that may uniquely identify the user device to which the advertisement is to be placed.
In the implementation of step S102, a transaction business turn of each transaction flow record is determined according to the transaction merchant information.
In particular, the transaction flow data typically does not include information about the business turn of the transaction, i.e., the transaction flow record typically does not include information about the business turn. In order to determine whether the transaction user identifier and the device identifier have a matching relationship through the matching relationship between the transaction business district and the delivery business district and the relationship between the transaction time and the delivery time, the business district where the transaction business is located in each transaction flow record needs to be determined.
The determining, in step S102, the transaction business circles of each transaction flow record according to the transaction business information may specifically include the following steps: step one: acquiring preset business circle data, wherein the preset business circle data comprises business information of a plurality of business circles; step two: for each transaction flow record, reading transaction merchant information of the transaction flow record; step three: searching merchant information which is the same as the transaction merchant information in the preset business district data; step four: and marking the business circle to which the found merchant information belongs as the transaction business circle of the transaction flow record.
In one non-limiting embodiment of the present invention, before determining the transaction business turn of each transaction flow record according to the transaction merchant information, the method may further include: and determining a preset business district.
Further, determining the preset business turn may include the steps of: step A: determining a transaction user and a transaction merchant according to the transaction flow data; and (B) step (B): establishing a biggest group according to the association relation between transaction merchants, wherein the association relation refers to the association relation between a plurality of transaction merchants when the same transaction user has transaction with the transaction merchants within preset time; step C: the biggest groups are combined into business circles according to the transaction merchants overlapped among the biggest groups. So that a plurality of preset business circles can be obtained.
Further, before determining the transaction user and the transaction merchant according to the transaction flow data, the method may further include: and (5) data cleaning. Specifically, non-consumer transaction streamlines, micropatterns, online transaction streamlines, etc. may be eliminated, but are not limited thereto.
Further, establishing the biggest group according to the association relationship between transaction merchants may include the following steps: calculating time intervals of transactions of a plurality of transaction merchants in a preset time by the same transaction user; determining weights among the plurality of transaction merchants according to the time intervals; and combining transaction merchants with weights higher than preset standard values into a biggest group.
Further, merging the biggest groups into business circles according to the transaction merchants that overlap between the biggest groups may include: defining similarity functions between the maximum cliques; calculating the similarity between each biggest group; combining the biggest groups with the similarity higher than the similarity standard value into a biggest group; calculating the correlation degree between the biggest groups, wherein the correlation degree is the ratio of the number of transaction merchants overlapped between the biggest groups and the biggest groups to the total number of the transaction merchants; combining the biggest groups with the correlation degree higher than the correlation degree standard value into a business circle.
Further, the third party platform may obtain preset business turn data, where the preset business turn data includes business turn information of a plurality of business turns, and the business turn information of each business turn may include business information of all businesses in the business turn, and may further include business turn identifiers capable of uniquely determining the business turn. The merchant information may be a merchant identifier, a merchant terminal identifier, a merchant name, or other information that can uniquely determine a merchant.
Further, the third party platform may read the transaction merchant information in each transaction flow record, where the transaction merchant information may refer to the description about the transaction merchant information in step S101, which is not described herein.
Further, the third party platform compares the transaction merchant information in each transaction flow record with the merchant circle information of each merchant circle in sequence, and takes the merchant circle containing the same merchant information as the transaction merchant information in the transaction flow record as the transaction merchant circle of the transaction flow record. The compared transaction merchant information and merchant information of the business district may be information of the same dimension, for example, the transaction merchant information is a merchant name, and the merchant information of the business district is also a merchant name.
Further, before the step one, that is, before the preset business turn data is obtained, the method may further include: acquiring a preset non-transaction time period; and deleting the transaction flow records of which the transaction time falls into the preset non-transaction time period in the transaction flow data. By adopting the mode, obviously irrelevant data can be removed, and the data processing amount is reduced. For example, considering that the business in the business district is usually in a business-stopping state in the period from 0 a.m. to 6 a.m., the transaction running water records will not be generated with the transaction user due to the transaction actions such as consumption in this period, the preset non-transaction period can be set to be from 0 a.m. to 6 a.m., and the transaction running water records of the transaction running water data between 0 a.m. and 6 a.m. are deleted, and only the transaction business information of the transaction running water records in the rest of the transaction running water data is read. Therefore, the number of the follow-up reading transaction flow records can be reduced, and the workload of a third party platform is lightened.
In one non-limiting embodiment of the present invention, the business circles of each business flow record may be obtained by a Hadoop Streaming algorithm. The Hadoop Streaming algorithm abstracts the computing task into three phases, map, shuffle and sort, and reduce.
Specifically, a third party platform acquires a plurality of transaction flow records, takes the transaction flow records and preset business circle data as inputs of a Hadoop Streaming algorithm, filters transaction flow records from 0 early morning to 6 early morning in a map stage in the Hadoop Streaming algorithm, extracts transaction merchant information, transaction time and transaction user identification of the rest transaction flow records as first preprocessing data, wherein the first preprocessing data comprises a plurality of data items, the data items correspond to the transaction flow records one by one, and the data items can comprise transaction merchant information, transaction time and transaction user identification corresponding to the transaction flow records, for example, transaction merchant information #2\t transaction time\t transaction user identification;
further, the third party platform can also acquire preset business circle data in a map stage in the Hadoop Streaming algorithm, extract all business information and business circle identifications in each business circle data as second preprocessing data, wherein the second preprocessing data comprises a plurality of data items, the data items correspond to the business information one by one, and the data items can comprise corresponding business information and business circle identifications of business circles to which the businesses belong, for example, business information #1\t business circle identifications.
Further, in the shuffle and sort stage of the Hadoop Streaming algorithm, the third party platform may sort the first pre-processed data and the second pre-processed data according to the field ordering before \t, where the field before # is stored in blocks, that is, the field before # is used as a primary key for ordering, and #1 or #2 is used as a secondary key for ordering.
Further, in the reduction stage in the Hadoop Streaming algorithm, the first preprocessing data and the second preprocessing data containing the same transaction merchant information and merchant information are segmented into the same reduction, in each reduction, data items containing #1 can be arranged before data items containing #2, the data items in the reduction are sequentially read, the #1 is read, the dictionary d is emptied, merchant information and business circle identifications in the data items containing #1 are taken as being put into the dictionary d, the #2 is read, and if the transaction merchant information in the data items containing #2 is in the dictionary d, the business circle identification # transaction time\t transaction user identification #2 is output. Since the business turn identification can uniquely determine one business turn, the business turn of each transaction flow record in the transaction flow data is determined.
In the implementation of step S103, a placement business district of each advertisement placement record is determined according to the device location information, where the placement business district is a business district to which the user device placed with the advertisement in the advertisement placement record belongs.
Specifically, the advertisement delivery data generally does not include information of a business district where the user equipment to which the advertisement is delivered is located when each advertisement is delivered, that is, the advertisement delivery record generally does not include information of the business district. In order to determine whether the transaction user identifier and the device identifier have a matching relationship through the matching relationship between the transaction business district and the delivery business district and the relationship between the transaction time and the delivery time, the business district where the user device to be delivered with the advertisement in each advertisement delivery record is located needs to be determined.
The determining, in step S103, the delivery business circles of each advertisement delivery record according to the device location information may specifically include the following steps: step one: acquiring preset business district data, wherein the preset business district data comprises position information of a plurality of business districts; step two: for each advertisement putting record, reading the equipment position information of the user equipment to which the advertisement is put; step three: searching a business district with a distance smaller than or equal to a preset distance from the preset business district data according to the equipment position information of the user equipment to be advertised and the position information of the business district; step four: and marking the found business circles as the advertisement putting business circles of the advertisement putting records.
Further, the third party platform may obtain the preset business turn data, where the business turn data includes business turn information of multiple business turns, and the business turn information of each business turn may include location information of the business turn, or may include other information of the business turn. The location information of the business turn may be coordinate information of the business turn, for example, coordinate information of a center point of the business turn, or other coordinate information capable of determining a location of the business turn.
Further, the third party platform may read the device location information of the advertised user device in each advertisement delivery record, where the device location information may refer to the description of the device location information of the advertised user device in step S101, which is not described herein.
Further, considering that business circles usually have a certain range, the range of each business circle can be determined through a preset distance, and the preset distance can be set by a third party platform or can be obtained from the outside by the third party platform.
Further, the distance between the user equipment to be advertised and each business district can be calculated according to the equipment position information of the user equipment to be advertised and the position information of the business district, and the business district with the distance smaller than or equal to the preset distance with the user equipment is used as the advertising business district.
Further, if the advertisement delivery business circle of a certain advertisement delivery record cannot be determined in the preset business circle data, deleting the advertisement delivery record.
In one non-limiting embodiment of the invention, the placement business for each advertisement placement record may be determined by a geohash algorithm.
Specifically, the third party platform may determine the encoding length in the geohash algorithm according to the preset distance. For example, if the preset distance is 600 meters, the code length in the geohash algorithm is 6.
Further, the third party platform may obtain coordinates of a center point of each business turn, where the coordinates of the center point of each business turn are generally represented by using a hundred-degree coordinate system, and convert the coordinates of the center point of each business turn in the hundred-degree coordinate system into coordinates corresponding to a Mars coordinate system and a world coordinate system, so as to obtain 3 coordinate values of the center point of each business turn, and respectively convert the 3 coordinate values into corresponding geohash code values to obtain 3 geohash code coordinates of the center point of each business turn, where each code coordinate represents a rectangular range, for example, the geohash code coordinates with a 6-bit code length may represent a range of 0.34 square kilometers.
Further, for 3 geohash code coordinates of the center point of the business circle, for each geohash code coordinate, 8 adjacent geohash code coordinates are taken, referring to fig. 2, 201 in fig. 2 is a range represented by the geohash code coordinates of the center point of the business circle, and 202 in fig. 2 is a range represented by the 8 adjacent geohash code coordinates. Thus, the coordinate information of each business turn may be represented by a geohash code coordinate set, where a geohash code coordinate set includes 27 geohash code coordinates. The geohash code coordinate set may define a new rectangular range, i.e. the range of the business turn.
Further, the equipment coordinate information in each advertisement putting record is represented by a geohash code coordinate, and is compared with the geohash code coordinate in the geohash code coordinate set of each business turn, and the business turn to which the geohash code coordinate set containing the geohash code coordinate of the user equipment to which the advertisement is put belongs is the putting business turn of the advertisement putting record.
Furthermore, a plurality of advertisement delivery records and business district information of each business district can be used as input of a Hadoop Streaming algorithm, the business district information can comprise a geohash coding coordinate set of the business district and a business district identifier, and the delivery business district of each advertisement delivery record in advertisement delivery data is determined through the Hadoop Streaming algorithm.
In the implementation of step S104, according to the matching relationship between the transaction business circles and the delivery business circles, and between the transaction time and the delivery time, the transaction running data and the advertisement delivery data are analyzed to determine the matching relationship between the transaction user identifier and the device identifier.
Referring to fig. 3, fig. 3 shows a specific step of analyzing the transaction flow data and the advertisement delivery data according to the matching relationship between the transaction business turn and the delivery business turn, and the transaction time and the delivery time in step S104 to determine the matching relationship between the transaction user identifier and the device identifier, which may specifically include:
Step S301: processing the transaction flow data according to the transaction business turn to obtain first processing data, wherein each data item in the first processing data comprises a transaction business turn, transaction time and transaction user identification of a corresponding transaction flow record;
step S302: processing the advertisement delivery data according to the delivery business circle to obtain second processing data of the advertisement delivery record, wherein each data item in the second processing data comprises the delivery business circle, the delivery time and the equipment identifier of the corresponding advertisement delivery record;
step S303: and analyzing the first processing data and the second processing data, and if the transaction business circle of the transaction flow record is the same as the delivery business circle of the advertisement delivery record and the time difference between the transaction time of the transaction flow record and the delivery time of the advertisement delivery record is less than or equal to a preset time difference, determining that the transaction user identification of the transaction flow record is matched with the equipment identification of the advertisement delivery record.
In the implementation of step S301, the transaction merchant information in each transaction flow record may be replaced by a corresponding transaction business circle to obtain the first processing data, or each transaction flow record and the corresponding transaction business circle may be used as input of a Hadoop Streaming algorithm to obtain the first processing data.
In the implementation of step S302, the device coordinate information in each advertisement delivery record may be replaced with a corresponding delivery business circle to obtain the second processing data, or each advertisement delivery record and the corresponding delivery business circle may be used as input of the Hadoop Streaming algorithm to obtain the second processing data.
Referring to fig. 4, fig. 4 shows a specific step of step S303 in fig. 3, where step S303 may include:
step A, setting a matching list, wherein the matching list comprises a plurality of sub-tables, and the sub-tables are in one-to-one correspondence with business circles;
step B, data items in the first processing data or the second processing data are read in time sequence, wherein the data items comprise time, business circles and identifiers, the time is transaction time of transaction or delivery time of advertisement delivery, the business circles are transaction business circles of transaction or delivery business circles of advertisement delivery, and the identifiers are transaction user identifiers of transaction or equipment identifiers of advertisement delivery;
step C, determining a sub-table to be added into the data item according to a business district in the data item;
step D, calculating the time difference between the time of the currently read data item and the time of the current first data item in the sub-table;
E, if the time difference is smaller than or equal to the preset time difference, adding the time and the mark in the data item into the sub-table, and returning to the step B;
and F, if the time difference is larger than the preset time difference, determining that the transaction user identification and the equipment identification of each data item in the sub-table are mutually matched, emptying the sub-table, adding the time and the identification in the data item into the sub-table, and returning to the step B.
In step B, if no new data item is available, i.e. all data items in the first and second processed data have been read, the analysis process is ended.
Further, in the embodiment of the present invention, a matching list may be set, where the matching list includes a plurality of sub-tables, the sub-tables correspond to business circles one by one, data items in the first processing data or the second processing data are read in time sequence, the data items include business circles, time and identifiers, the sub-table to which the data item is to be added is determined according to the business circles in the data items, so that the transaction user identifier and the device identifier in each sub-table correspond to the same business circle, and if the time difference between the time of the currently read data item and the time of the currently first data item in the sub-table is greater than the preset time difference, it is determined that the transaction user identifier and the device identifier of each data item in the sub-table are mutually matched, so as to obtain a matching relationship between the transaction user identifier and the device identifier in the current sub-table, then the sub-table is emptied, and the time and the identifier in the data item are added into the sub-table, and then the next data item is read, and circulation is continued, so that the correspondence between the transaction user identifiers of the transaction flow records and the advertisement placement identifiers of the plurality of transaction flow records and the advertisement placement records can be determined.
Further, the third party platform may set a set of device identifiers for each transaction user identifier, where the transaction user identifiers correspond to the set of device identifiers one-to-one. After determining that the transaction user identifiers and the device identifiers of all the data items in the sub-table are mutually matched, adding the device identifiers in the current sub-table into a device identifier set corresponding to each transaction user identifier in the current sub-table after de-duplication, emptying the sub-table, and adding the time and the identifiers in the data items into the sub-table to serve as a first data item in the sub-table.
Further, for each set of device identifiers of the transaction user identifiers, the number of matches between each device identifier and the transaction user identifier may be calculated, so that the device identifier with the highest number of matches and the transaction user identifier may be considered as belonging to the same user.
Further, in the solution of the embodiment of the present invention, the method may further include: analyzing the transaction flow data to determine a plurality of transaction users, each transaction user having a corresponding transaction user identification; determining the equipment identification of the transaction user according to the matching relation between the transaction user identification and the equipment identification; and putting advertisements to the equipment of the transaction user according to the equipment identification of the transaction user. .
In particular, consumer preferences of the transaction user for individual merchants may be determined by analyzing the transaction flow data. That is, the consumption preference of each transaction user to the merchant can be determined according to the transaction amount, the transaction times and other information in the transaction flow data, and the portrait of the transaction user is realized, so that the transaction user with the consumption preference to a certain merchant or some merchants can be further screened out, that is, the marketing object is determined.
Further, according to the matching relationship between the transaction user identifier and the device user identifier obtained in the above, the transaction user identifier of the screened transaction user can be matched to the corresponding device identifier, so that the merchant can find the device of the transaction user to put advertisements according to the device identifier of the transaction user with consumption preference, and compared with the scheme of directly putting advertisements to each user without considering consumption preference, the scheme in the embodiment of the invention can greatly improve marketing effect.
Therefore, in the embodiment of the invention, different delivery schemes can be formulated according to the advertisement delivery requirement of the merchant, the user meeting the delivery requirement is determined based on the delivery schemes and according to the transaction flow data, and the device identification of the user meeting the delivery requirement can be determined and the advertisement is delivered to the device according to the device identification of the user because the matching relationship between the transaction user identification and the device identification is known.
It should be noted that, although the above embodiment is described taking transaction stream data and advertisement delivery data as an example, the transaction stream data and the push data may be analyzed, for example, specifically, the push data includes a plurality of push delivery records of push information (such as news, short video, etc.), the push delivery records include a delivery time of the push information, device location information of a user device to be delivered, and a data identifier, and similarly, the matching relationship between the user identifier and the device identifier may be determined by a matching relationship between the transaction time and the delivery time, and a matching relationship between a transaction business circle of a transaction and a delivery business circle of information push.
Fig. 5 is a schematic structural diagram of a data identifier matching device according to an embodiment of the present invention, where the data identifier matching device may include a receiving module 51, a first determining module 52, a second determining module 53, and a matching module 54, where:
the receiving module 51 is configured to obtain transaction flow data and advertisement delivery data, where the transaction flow data includes a plurality of transaction flow records, the transaction flow records include transaction time, transaction merchant information and transaction user identifiers, the advertisement delivery data includes a plurality of advertisement delivery records, and the advertisement delivery records include delivery time of advertisement delivery, device location information of user devices to which advertisements are delivered, and device identifiers;
The first determining module 52 is configured to determine a transaction business turn of each transaction flow record according to the transaction business information;
the second determining module 53 is configured to determine, according to the device location information, a serving lane of each advertisement serving record, where the serving lane is a serving lane to which a user device that is served an advertisement in the advertisement serving record belongs;
the matching module 54 is configured to analyze the transaction flow data and the advertisement delivery data according to a matching relationship between the transaction business district and the delivery business district, and the transaction time and the delivery time, so as to determine a matching relationship between the transaction user identifier and the device identifier.
Those skilled in the art will appreciate that the data identifier matching device in this embodiment may be used to implement the data identifier matching method in the embodiments shown in fig. 1 to 3 described above.
Further, the embodiment of the invention also discloses a storage medium, on which a computer program is stored, which when being executed by a processor, performs the technical scheme of the method in the embodiment shown in the above fig. 1 to 4. The storage medium may include a computer-readable storage medium such as a non-volatile (non-volatile) memory or a non-transitory (non-transitory) memory. The storage medium may include, but is not limited to, ROM, RAM, magnetic or optical disks, and the like.
Further, the embodiment of the invention also discloses a computing device, which comprises a memory and a processor, wherein the memory stores a computer program capable of running on the processor, and the processor executes the technical scheme of the method in the embodiment shown in the figures 1 to 4 when running the computer program.
Although the present invention is disclosed above, the present invention is not limited thereto. Various changes and modifications may be made by one skilled in the art without departing from the spirit and scope of the invention, and the scope of the invention should be assessed accordingly to that of the appended claims.
Claims (8)
1. A method for matching data identifiers, comprising:
acquiring transaction flow data and advertisement delivery data, wherein the transaction flow data comprises a plurality of transaction flow records, the transaction flow records comprise transaction time, transaction merchant information and transaction user identification, the advertisement delivery data comprises a plurality of advertisement delivery records, and the advertisement delivery records comprise the delivery time of advertisement delivery, the equipment position information and the equipment identification of user equipment to which the advertisement is delivered;
determining the trade business circle of each trade flow record according to the trade business information;
Determining a delivery business circle of each advertisement delivery record according to the equipment position information, wherein the delivery business circle is a business circle to which user equipment for delivering advertisements in the advertisement delivery records belongs;
according to the matching relation between the transaction business circles and the delivery business circles and between the transaction time and the delivery time, analyzing the transaction flow data and the advertisement delivery data to determine the matching relation between the transaction user identification and the equipment identification;
wherein the determining the transaction business circles of each transaction flow record according to the transaction business information comprises: acquiring preset business circle data, wherein the preset business circle data comprises business information of a plurality of business circles;
for each transaction flow record, reading transaction merchant information of the transaction flow record;
searching merchant information which is the same as the transaction merchant information in the preset business district data;
marking the business circle to which the found merchant information belongs as the transaction business circle of the transaction flow record;
wherein determining the delivery business circles of each advertisement delivery record according to the equipment position information comprises:
acquiring preset business district data, wherein the preset business district data comprises position information of a plurality of business districts;
For each advertisement putting record, reading the equipment position information of the user equipment to which the advertisement is put; searching a business district with a distance smaller than or equal to a preset distance from the preset business district data according to the equipment position information of the user equipment to be advertised and the position information of the business district;
marking the found business circles as the advertisement putting business circles of the advertisement putting records;
the method for determining the matching relationship between the transaction user identification and the equipment identification comprises the following steps of:
processing the transaction flow data according to the transaction business turn to obtain first processing data, wherein each data item in the first processing data comprises a transaction business turn, transaction time and transaction user identification of a corresponding transaction flow record;
processing the advertisement delivery record according to the delivery business circle to obtain second processing data, wherein each data item in the second processing data comprises a delivery business circle, delivery time and equipment identifier of the corresponding advertisement delivery record;
And analyzing the first processing data and the second processing data, and if the transaction business circle of the transaction flow record is the same as the delivery business circle of the advertisement delivery record and the time difference between the transaction time of the transaction flow record and the delivery time of the advertisement delivery record is less than or equal to a preset time difference, determining that the transaction user identification of the transaction flow record is matched with the equipment identification of the advertisement delivery record.
2. The data identification matching method as claimed in claim 1, wherein the reading before the acquiring the preset business turn data further comprises:
acquiring a preset non-transaction time period;
and deleting the transaction flow records of which the transaction time falls into the preset non-transaction time period in the transaction flow data.
3. The data identification matching method of claim 1, wherein said analyzing said first and second processed data comprises:
step A, setting a matching list, wherein the matching list comprises a plurality of sub-tables, and the sub-tables are in one-to-one correspondence with business circles;
step B, data items in the first processing data or the second processing data are read in time sequence, wherein the data items comprise time, business circles and identifiers, the time is transaction time of transaction or delivery time of advertisement delivery, the business circles are transaction business circles of transaction or delivery business circles of advertisement delivery, and the identifiers are transaction user identifiers of transaction or equipment identifiers of advertisement delivery;
Step C, determining a sub-table to be added into the data item according to a business district in the data item;
step D, calculating the time difference between the time of the currently read data item and the time of the current first data item in the sub-table;
e, if the time difference is smaller than or equal to the preset time difference, adding the time and the mark in the data item into the sub-table, and returning to the step B;
and F, if the time difference is larger than the preset time difference, determining that the transaction user identification and the equipment identification of each data item in the sub-table are mutually matched, emptying the sub-table, adding the time and the identification in the data item into the sub-table, and returning to the step B.
4. The data identity matching method of claim 1, further comprising:
and counting the matching times of each transaction user identifier and each device identifier.
5. The data identity matching method of claim 1, further comprising:
analyzing the transaction flow data to determine a plurality of transaction users, each transaction user having a corresponding transaction user identification;
determining the equipment identification of the transaction user according to the matching relation between the transaction user identification and the equipment identification;
And putting advertisements to the equipment of the transaction user according to the equipment identification of the transaction user.
6. A data identification matching device, comprising:
the receiving module is used for acquiring transaction flow data and advertisement delivery data, wherein the transaction flow data comprises a plurality of transaction flow records, the transaction flow records comprise transaction time, transaction merchant information and transaction user identification, the advertisement delivery data comprises a plurality of advertisement delivery records, and the advertisement delivery records comprise the delivery time of advertisement delivery, the equipment position information and the equipment identification of user equipment to which the advertisement is delivered;
the first determining module is used for determining the transaction business circles of each transaction flow record according to the transaction business information;
the second determining module is used for determining a delivery business circle of each advertisement delivery record according to the equipment position information, wherein the delivery business circle is a delivery business circle to which the user equipment for delivering advertisements in the advertisement delivery records belongs;
the matching module is used for analyzing the transaction flow data and the advertisement delivery data according to the matching relation between the transaction business circles and the delivery business circles and the transaction time and the delivery time so as to determine the matching relation between the transaction user identification and the equipment identification;
Wherein the determining the transaction business circles of each transaction flow record according to the transaction business information comprises:
acquiring preset business circle data, wherein the preset business circle data comprises business information of a plurality of business circles;
for each transaction flow record, reading transaction merchant information of the transaction flow record;
searching merchant information which is the same as the transaction merchant information in the preset business district data;
marking the business circle to which the found merchant information belongs as the transaction business circle of the transaction flow record;
wherein, the delivery business circle for determining each advertisement delivery record according to the equipment position information comprises:
acquiring preset business district data, wherein the preset business district data comprises position information of a plurality of business districts;
for each advertisement putting record, reading the equipment position information of the user equipment to which the advertisement is put; searching a business district with a distance smaller than or equal to a preset distance from the preset business district data according to the equipment position information of the user equipment to be advertised and the position information of the business district;
the found business turn is recorded as the advertisement putting business turn of the advertisement putting record,
The step of analyzing the transaction flow data and the advertisement delivery data according to the matching relationship between the transaction business circles and the delivery business circles and the transaction time and the delivery time to determine the matching relationship between the transaction user identification and the equipment identification comprises the following steps:
processing the transaction flow data according to the transaction business turn to obtain first processing data, wherein each data item in the first processing data comprises a transaction business turn, transaction time and transaction user identification of a corresponding transaction flow record;
processing the advertisement delivery record according to the delivery business circle to obtain second processing data, wherein each data item in the second processing data comprises a delivery business circle, delivery time and equipment identifier of the corresponding advertisement delivery record;
and analyzing the first processing data and the second processing data, and if the transaction business circle of the transaction flow record is the same as the delivery business circle of the advertisement delivery record and the time difference between the transaction time of the transaction flow record and the delivery time of the advertisement delivery record is less than or equal to a preset time difference, determining that the transaction user identification of the transaction flow record is matched with the equipment identification of the advertisement delivery record.
7. A storage medium having stored thereon a computer program, which when executed by a processor performs the steps of the data identification matching method of any of claims 1 to 5.
8. A computing device comprising a memory and a processor, the memory having stored thereon a computer program executable on the processor, characterized in that the processor executes the steps of the data identification matching method of any of claims 1 to 5 when the computer program is executed.
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