CN111507786B - Data processing method, device and equipment - Google Patents
Data processing method, device and equipment Download PDFInfo
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- CN111507786B CN111507786B CN201910092622.4A CN201910092622A CN111507786B CN 111507786 B CN111507786 B CN 111507786B CN 201910092622 A CN201910092622 A CN 201910092622A CN 111507786 B CN111507786 B CN 111507786B
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Abstract
The embodiment of the invention provides a data processing method, a device and equipment, wherein the method comprises the following steps: determining that the first user has funds flowing into the second user according to the funds flow data; determining a first commodity category corresponding to a first user and a second commodity category corresponding to a second user; if the first commodity category and the second commodity category have correlation, determining that the supply-demand relationship exists between the first user and the second user, so that the supply-demand relationship among different users is mined.
Description
Technical Field
The present invention relates to the field of internet technologies, and in particular, to a data processing method, apparatus, and device.
Background
With the continuous development of the internet, online shopping modes have become widely adopted shopping modes in lives of people. However, this does not mean that the on-line shopping approach has completely replaced the off-line shopping approach, and at present, the on-line and off-line shopping approaches coexist.
An industrial chain of a commodity may go through 4 links, respectively, a factory, an agent, a seller, and a consumer, each of which plays a role in the industrial chain. Different roles may perform various operations on the merchandise, which tend to be overwhelmed by the massive amounts of data on the line.
In some scenarios, it is of great importance to mine the relationship, i.e. supply-demand relationship or as order relationship, of different users (such as users of different roles or users of the same role) in the industry chain. For example, zhang Sanhas been determined to be fake, and if it was found to be in stock to Li IV and Wang V respectively, and Zhao Liu was found to be also in stock to be re-spammed, then this fake link would be found.
Disclosure of Invention
The embodiment of the invention provides a data processing method, a device and equipment, which are used for mining the supply and demand relationship among users.
In a first aspect, an embodiment of the present invention provides a data processing method, including:
determining that the first user has funds flowing into the second user according to the funds flow data;
determining a first commodity category corresponding to the first user and a second commodity category corresponding to the second user;
and if the first commodity category and the second commodity category have correlation, determining that a supply-demand relationship exists between the first user and the second user.
In a second aspect, an embodiment of the present invention provides a data processing apparatus, including:
the first determining module is used for determining that the first user has funds to flow into the second user according to the funds flow data;
The second determining module is used for determining a first commodity category corresponding to the first user and a second commodity category corresponding to the second user;
and the third determining module is used for determining that a supply-demand relationship exists between the first user and the second user if the first commodity category and the second commodity category have correlation.
In a third aspect, an embodiment of the present invention provides an electronic device, including a processor and a memory, where the memory has executable code stored thereon, and when the executable code is executed by the processor, causes the processor to perform the data processing method in the first aspect.
Embodiments of the present invention provide a non-transitory machine-readable storage medium having stored thereon executable code which, when executed by a processor of an electronic device, causes the processor to perform the data processing method in the first aspect.
In addition, the embodiment of the invention also provides a data processing method, which comprises the following steps:
determining that an interaction behavior exists between the first user and the second user according to the interaction record;
determining a first object feature corresponding to the first user and a second object feature corresponding to the second user, wherein the first object and the second object are related to the interaction behavior;
And if the first object feature and the second object feature have correlation, determining that a dependency relationship exists between the first user and the second user.
In the embodiment of the invention, for a certain piece of fund flow data, it is assumed that the piece of data for transferring funds from the first user to the second user is determined according to the fund flow of the fund flow data, and in order to determine whether a supply-demand relationship exists between the first user and the second user, first, a first commodity category corresponding to the first user, namely, a category to which a commodity sold by the first user belongs, and a second commodity category corresponding to the second user, namely, a category to which a commodity sold by the second user belongs are determined. And secondly, judging whether the first commodity category and the second commodity category have correlation, and if the first commodity category and the second commodity category have correlation, determining that a supply-demand relationship exists between the first user and the second user, namely, that the relationship of the first user delivering goods to the second user exists.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a data processing method according to an embodiment of the present invention;
FIG. 2 is a flowchart of another data processing method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of estimating the category of goods sold by a user based on a funding relationship according to an embodiment of the present invention;
FIG. 4 is a schematic view of a scenario in which a supply-demand relationship is identified, corresponding to the embodiment shown in FIG. 2;
FIG. 5 is a flowchart of another data processing method according to an embodiment of the present invention;
FIG. 6 is a schematic view of a scenario in which a supply-demand relationship is identified, corresponding to the embodiment shown in FIG. 5;
FIG. 7 is a schematic diagram of a data processing apparatus according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of an electronic device corresponding to the data processing apparatus provided in the embodiment shown in fig. 7.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, the "plurality" generally includes at least two, but does not exclude the case of at least one.
It should be understood that the term "and/or" as used herein is merely one relationship describing the association of the associated objects, meaning that there may be three relationships, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
The words "if", as used herein, may be interpreted as "at … …" or "at … …" or "in response to a determination" or "in response to a detection", depending on the context. Similarly, the phrase "if determined" or "if detected (stated condition or event)" may be interpreted as "when determined" or "in response to determination" or "when detected (stated condition or event)" or "in response to detection (stated condition or event), depending on the context.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a product or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such product or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a commodity or system comprising such elements.
In addition, the sequence of steps in the method embodiments described below is only an example and is not strictly limited.
The data processing method provided by the embodiment of the invention can be used for judging whether the supply and demand relationship (which can be called as an industry chain relationship and a stock relationship) exists between two users with funds. The core of the supply-demand relationship judgment is to judge whether the commodity purchased by the buyer (fund issuer) has correlation with the commodity sold by the buyer. Such as: when a user A orders a product purchased from a user B and sells the computer product, the user A and the user B can be considered to have a commodity-entering relation.
How to mine the supply-demand relationship between different users is described below in connection with some embodiments as follows.
Fig. 1 is a flowchart of a data processing method according to an embodiment of the present invention, where the data processing method may be performed by an e-commerce platform device, such as a server. As shown in fig. 1, the method comprises the steps of:
101. and determining that the first user has funds flowing into the second user according to the funds flow data.
In this embodiment, the data of the funds flow indicates the data of the funds flow, and in practical application, for example, the order data and the transfer data may both indicate the funds flow, so they may be used as the data of the funds flow.
Wherein, for order data: when a user purchases a commodity of a merchant in an online shopping mode and finishes payment, order data corresponding to the transaction is generated, wherein the order data comprises relevant information such as a buyer, a seller, commodity, amount and the like.
For transfer data: a merchant may purchase a lot of goods offline from a manufacturer or an agent, and pay the manufacturer or the agent by an online payment means, so as to form a piece of transfer data, where the piece of transfer data includes information related to a payer, a payee, a transfer time, a transfer amount, and the like.
Based on this, a server as an execution subject of the embodiment of the present invention may maintain a plurality of order data generated on the e-commerce platform side and a plurality of transfer data generated on the payment platform side. The server can analyze order data and transfer data generated in a period of time based on artificial triggering or periodically so as to mine the supply and demand relations existing among different users.
Thus, in an alternative, step 101 may be implemented as: and acquiring order data, and if the buyer and the seller contained in the order data are the first user and the second user respectively, determining that the first user has funds and flows into the second user.
In another alternative, step 101 may be implemented as: and acquiring transfer data, and if the payer and the payee contained in the transfer data are the first user and the second user respectively, determining that the first user has funds to flow into the second user.
102. And determining a first commodity category corresponding to the first user and a second commodity category corresponding to the second user.
As described previously: the core of the supply-demand relation judgment is to judge whether the commodity purchased by the buyer (fund issuer) has correlation with the commodity sold by the buyer. Therefore, in step 102, it is determined that the first category of the commodity corresponding to the first user is actually a category to which the commodity sold by the first user belongs; the determination of the category of the second commodity to which the second user corresponds is actually a determination of the category to which the commodity sold by the second user belongs, in other words, a determination of the category to which the commodity purchased by the first user from the second user belongs.
Taking the case where order data is taken as the fund flow data as an example, one determination manner of the first commodity category and the second commodity category is described below, and the case where transfer data is described and other determination manners will be described in the following embodiments.
In the case of taking order data as the fund flow data, since the first user purchases a certain commodity from the second user to generate the order data, the order data includes the purchased commodity information, at this time, it may be determined that the second commodity category corresponding to the second user is a category to which the commodity included in the order data belongs, for example, an online shop corresponding to the second user inquires about which category the commodity purchased by the first user belongs.
In addition, if the first user is an online merchant, the first user has an online shop, and at this time, the commodity category contained in the online shop of the first user can be queried as a first commodity category corresponding to the first user.
103. If the first commodity category and the second commodity category have correlation, determining that a supply-demand relationship exists between the first user and the second user.
In the embodiment of the invention, the correlation between commodity categories can be defined by the following two dimensions: similarity and pin entry relationship. These two dimensions are alternative relationships.
Specifically, if the degree of similarity between the first item category and the second item category is greater than or equal to a set threshold, it is determined that there is a correlation between the first item category and the second item category. Or if there is a marketing relationship between the first category of merchandise and the second category of merchandise, determining that there is a correlation between the first category of merchandise and the second category of merchandise.
The following describes how to determine the similarity and the marketing relationship between the first commodity category and the second commodity category.
Firstly, the total number of all commodity categories corresponding to all online shops in the e-commerce platform is generally limited, for example, about hundreds of commodity categories are relatively stable and are not updated frequently, so that the calculation of the similarity of every two commodity categories can be performed in advance, and when the similarity of the first commodity category and the second commodity category needs to be calculated, only the obtained similarity calculation result needs to be queried.
Alternatively, the similarity between the two commodity categories may be manually pre-labeled. Specifically, the commodity category may be set to take a secondary category, structurally denoted as a/b, where a represents the primary category and b represents the secondary category. Thus, for example: if the first class and the second class of the two commodity categories are the same, the similarity of the two commodity categories is regarded as F1, if the first class and the second class of the two commodity categories are the same and the second class of the two commodity categories are different, the similarity of the two commodity categories is regarded as F2, and if the first class and the second class of the two commodity categories are different, the similarity of the two commodity categories is regarded as F3. It will be appreciated that F1, F2 and F3 are preset values, F1 is greater than F2, F2 is much greater than F3, and in addition, a threshold may be set to a value between F3 and F2, so that if the similarity between two commodity categories is F2 or F1, there is a correlation between the two commodity categories.
Optionally, another method for calculating the similarity between commodity categories is also provided herein-word 2vec.
The purpose of the word2vec method is to vectorize each word into the form of a coded vector, and if two words are more similar, the coded vectors vector of the two words are more similar, that is, the smaller the included angle between the corresponding vectors (or the larger the cosine distance). Here we use word2vec to vectorize each commodity category, which can be achieved specifically by:
respectively acquiring a plurality of commodity categories sold by a plurality of online shops;
constructing a co-occurrence relation pair corresponding to each online shop according to the combination condition of a plurality of commodity categories corresponding to each online shop, wherein one co-occurrence relation pair consists of two different commodity categories in the commodity categories;
determining coding vectors corresponding to the commodity categories according to the co-occurrence relation pairs;
and determining the similarity between the two corresponding commodity categories according to the distance between every two coding vectors.
Specifically, the online shops are merchants with online shops on the side of the electronic commerce platform, and the interfaces of the online shops generally contain commodity category information corresponding to various commodities operated by the corresponding merchants, so that various commodity categories sold by each online shop, namely each merchant, can be extracted based on the commodity category information.
For example, assume that the categories of merchandise sold by any merchant are as follows: green plant gardening/nutrient soil; green plant gardening/flower art packaging paper; green plant gardening/spading. Since the merchant sells 3 commodity categories in total, the 3 commodity categories can be combined in pairs to form 3 co-occurrence relation pairs:
green plant gardening/nutrient soil-green plant gardening/flower art packaging paper;
green plant gardening/nutrient soil-green plant gardening/spade;
green gardening/flower art packaging paper-green gardening/spade.
The co-occurrence relation pairs are constructed aiming at all online shops, so that the co-occurrence relation pairs corresponding to all online shops can be used as training samples and provided for word2vec for modeling learning, and finally, the coding vector of each commodity category is obtained. If the cosine distance between the code vectors corresponding to any two commodity categories is greater than or equal to a certain set threshold (e.g., 0.7), then the two commodity categories can be considered to have correlation.
The following table illustrates the similarity calculation results for several commodity categories:
assume that the first category of merchandise in the above step is: medicinal materials/spatholobus stem, the second commodity category is: the medicinal material/luffa can be found by inquiring the similarity calculation result, wherein the similarity between the medicinal material and the luffa is 0.74 and is larger than the set threshold value of 0.7, so that the medicinal material and the luffa have correlation.
The manner in which the similarity between two categories of merchandise is calculated is described above.
The following describes how to determine whether a sales relationship exists between two commodity categories, which can be implemented specifically by the following steps:
respectively acquiring N commodity categories purchased by each of a plurality of online shops and M commodity categories sold, wherein M and N are integers greater than or equal to 1;
according to the combination situation of N commodity categories and M commodity categories corresponding to each online shop, M x N marketing relation pairs corresponding to each online shop are constructed, wherein one marketing relation pair consists of one of the N commodity categories and one of the M commodity categories;
and screening out target marketing relation pairs with occurrence times and conditional probability meeting a set threshold.
Specifically, for any on-line shop, N commodity categories purchased and M commodity categories sold are counted, and then combined into m×n marketing relation pairs, which represent multiplier numbers. The M commodity categories sold by a certain online shop can be obtained by inquiring corresponding shop interfaces in the electronic commerce platform. The N commodity categories purchased by a certain online shop can be obtained by counting the order data of the online shop within a period of time and the commodity categories of all commodities in the order data.
For example, suppose that N categories of merchandise purchased by an online store are: horticultural/succulent plants, horticultural/flowerpot, horticultural/nutrient solutions. M commodity categories sold by the online shops are as follows: green plants/horticultural plants, green plants/fruit trees. Then the following M x n=6 entry relationship pairs may be constructed:
horticultural/succulent plants-green plants/horticultural plants;
gardening/flowerpot-green plant/gardening plant;
gardening/nutrient solution-green plants/horticultural plants;
gardening/succulent plants-green plants/fruit trees;
gardening/flowerpot-green planting/fruit tree;
gardening/nutrient solution-green plants/fruit trees.
The establishment of the marketing relation pairs is carried out for all online shops, so that marketing relations corresponding to all online shops are summarized, the occurrence times and the occurrence probability of each marketing relation pair are calculated, and finally, the final target marketing relation pair is determined through threshold judgment.
For example, the number of occurrences and probability for any pair of business relationships may be determined as follows: assuming that the summarized results show that 100 online shops purchase the commodity category of gardening/flowerpot, and on the basis of the commodity category of green plants/fruit trees sold by 30 online shops, the occurrence number of the marketing relation pair consisting of gardening/flowerpot-green plants/fruit trees is 30 times, and the probability is 30%. The occurrence number threshold and the probability threshold may be set respectively, and if the occurrence number of a certain pin-entry relationship pair is higher than the occurrence number threshold and the probability is higher than the probability threshold, the pin-entry relationship pair is considered as a target pin-entry relationship pair.
Based on this, regarding the first commodity category and the second commodity category in the foregoing step, if the finally obtained target sales relationship pair includes a sales relationship pair constituted of the first commodity category and the second commodity category, it is determined that there is a correlation between the first commodity category and the second commodity category.
Fig. 2 is a flowchart of another data processing method according to an embodiment of the present invention, as shown in fig. 2, may include the following steps:
201. and acquiring order data, and if the buyer and the seller contained in the order data are the first user and the second user respectively, determining that the first user has funds and flows into the second user.
202. And determining a second commodity category corresponding to the second user as a category to which the commodity contained in the order data belongs.
In this embodiment, in a scenario in which the first user purchases a commodity from the second user through online shopping, the second commodity category corresponding to the second user is the commodity category of the commodity purchased by the first user, that is, the commodity category to which the commodity sold by the second user belongs.
203. And if the first user does not have the online store, acquiring a plurality of third users for transferring accounts to the first user, wherein the third users respectively have the online store.
204. And if the number of the plurality of third users accords with a set threshold value, determining that the first commodity category corresponding to the first user is at least a part of commodity categories in the commodity categories contained in the online shops of the plurality of third users.
In this embodiment, in a scenario in which the first user purchases a commodity from the second user through online shopping, determining the first commodity category corresponding to the first user is determining the commodity category sold by the first user. At this time, if the first user has an online shop, that is, a merchant on the e-commerce platform side, the first commodity category corresponding to the first user can be directly determined to be the commodity category contained in the online shop. However, if the first user does not have an online store, such as the first user may be some offline agent, then a scheme is provided herein for inferring the category of merchandise sold by the first user based on the funding relationship.
The core idea of the scheme for presuming the commodity category sold by a certain user based on the funding relation is that: if there is a significant commonality in the categories of merchandise sold by multiple users among the downstream users of a user X (i.e., the users transferring funds to the user), such as a high similarity between the categories of merchandise sold by 4 of the 7 downstream users, then that user X is considered to be selling the similar category of merchandise.
In particular, the funds transactions records for the first user may be aggregated over a period of time to obtain a user transfer to the first user. In fact, the users transferring to the first user may include both merchants where online shops exist and merchants where online shops do not exist, and the merchants where online shops exist are referred to as a plurality of third users. And then, counting to obtain commodity categories sold by each third user in the plurality of third users, calculating the correlation among the commodity categories sold by different third users, and if a certain number of commodity categories have correlation, and the number of the plurality of third users accords with a set threshold, determining the first commodity category corresponding to the first user as the certain number of commodity categories.
The following is illustrated in connection with fig. 3: assuming that the first user is user X illustrated in the figure, the following four users are found to transfer money to user X according to the fund transfer records of user X: user a, user B, user C, and user D. Through the traversal of the online shops of the four users, the four users are clothing stores, and the commodity categories managed by the four clothing stores have obvious similarity, the user X can be considered to have similar commodity categories, so that the user X can be a clothing store, or a clothing agency or a clothing manufacturer.
205. If the first commodity category and the second commodity category have correlation, and the order summary information between the first user and the second user accords with the setting condition, determining that a supply-demand relationship exists between the first user and the second user.
It should be noted that, when the first commodity category is plural and the second commodity category is plural, it may be calculated whether each combination of the plural first commodity categories and the plural second commodity categories meets the correlation requirement. If the ratio of the number of combinations satisfying the correlation requirement is greater than the set value, it is considered that there is a correlation between the plurality of first commodity categories and the plurality of second commodity categories.
In addition, in the embodiment of the invention, for determining whether the supply-demand relationship exists between the two users with the fund relationship, besides considering the correlation between the commodity categories sold by the two users, the order summary information between the two users is also considered, so that the influence of normal consumption behavior on the supply-demand relationship judgment result is avoided. For example, after determining that there is a correlation between the first commodity category corresponding to the first user and the second commodity category corresponding to the second user, further consider order summary information such as the number of orders, the number of commodities in transactions, the total amount of orders, etc. between the first user and the second user, and consider that there is a supply-demand relationship between the first user and the second user only if the order summary information satisfies the set condition, such as the number of orders is greater than 100, the number of commodities in transactions is greater than 100, and the total amount of orders is greater than 5000 yuan.
In summary, the present embodiment introduces a process for identifying a supply-demand relationship between users based on order data based on the order data. For a better visual understanding of this process, it is illustrated in connection with fig. 4. As shown in fig. 4, shoe store a, fruit store B, shoe store C, and general consumer D respectively place an order for a user X to purchase a shoe product, and since the shoe products purchased in shoe store a and shoe store C are also sold by themselves, there is a high correlation, and the relationship between shoe store a and shoe store C and user X is determined as a supply-demand relationship.
Fig. 5 is a flowchart of another data processing method according to an embodiment of the present invention, as shown in fig. 5, may include the following steps:
501. and acquiring transfer data, and if the payer and the payee contained in the transfer data are the first user and the second user respectively, determining that the first user has funds to flow into the second user.
In this embodiment, the identification process of the supply-demand relationship between users is introduced based on the transfer data.
502. If the first user has an online shop, determining that the first commodity category corresponding to the first user is the commodity category contained in the online shop.
In contrast, if the first user does not have an online store, the first category of merchandise corresponding to the first user may be determined in the manner described with reference to the embodiment shown in FIG. 2.
503. And if the second user does not have the online store, acquiring a plurality of fourth users for transferring accounts to the second user, wherein the fourth users respectively have the online store.
504. And if the number of the fourth users accords with a set threshold value, determining that the second commodity category corresponding to the second user is the at least partial commodity category.
505. If the first commodity category and the second commodity category have correlation, and the transfer summary information between the first user and the second user accords with the setting condition, determining that a supply-demand relationship exists between the first user and the second user.
In this embodiment, the transfer summary information may be, for example, the total number of transfers between the first user and the second user in a certain period of time, the total amount, and the like.
The parts of the embodiment that are not described in detail may refer to the related descriptions in the embodiment shown in fig. 2, and the description is omitted.
For a better visual understanding of the supply-demand relationship identification process in the present embodiment, it is illustrated in connection with fig. 6. As shown in fig. 6, there are a user a, a user B, a user C, a user D, and a user E, respectively, having a transfer behavior to a user X, wherein the user a, the user B, and the user C are clothing stores, the user D is a snack store, and the user E is a consumer. Since the commodity categories corresponding to the users can be determined based on the determination method of the commodity categories sold by the users introduced above when only the users have the transfer behavior to the user X but do not know the intention of the transfer, it is assumed that the user a, the user B, and the user C sell clothing categories and that the user X is also the purpose of selling clothing is presumed, however, the transfer summary information of the user a and the user X does not satisfy the set condition (for example, the user a transfers to the user X only once in a period of time and the amount of money is low), thereby finally determining that the user B and the user C have the supply-demand relationship with the user X, respectively.
Through the above embodiments, in the commodity transaction scenario, it may be determined whether there is a supply-demand relationship, i.e., a large number of long-term stable business relationships, between the first user and the second user on the commodity transaction. In other application scenarios, some other dependency may exist between different users. Therefore, in order to mine whether a certain dependency relationship exists between different users in various scenes, the embodiment of the invention further provides the following general scheme:
determining that an interaction behavior exists between the first user and the second user according to the interaction record;
determining a first object feature corresponding to a first user and a second object feature corresponding to a second user, wherein the first object and the second object are related to the interaction behavior;
if the first object feature and the second object feature have correlation, determining that a dependency relationship exists between the first user and the second user.
Wherein, the object is related to the interaction behavior, which means that when some interaction behavior exists between different users, the object which digs whether some dependency relationship exists between different users is some object.
When the interaction record is funds flow data, the first object and the second object are commodities, and the index for measuring the commodity characteristics adopts commodity categories, the method is a scheme for determining whether the supply-demand relationship on the commodity exists between the first user and the second user in the foregoing embodiment.
For another example, in one embodiment, the parking space rental relationship between different users may be mined. At this time, the interaction record may be embodied as a transmission record of a certain sharing certificate, where the sharing certificate is a digital verification code and a two-dimensional code, so that a user entering a certain parking lot can unlock the parking space lock through the digital verification code or the two-dimensional code.
In this scenario, user a has ownership of a certain parking space, and can rent the parking space for other users to use when the user does not use the parking space. The intelligent parking lock is deployed on the parking space, and can be unlocked only by a user inputting a correct digital verification code or brushing a correct two-dimensional code. For example, the user B rents the parking space of the user A, then the user A can send the digital verification code or the two-dimensional code to the user B through a certain APP, and then the service end corresponding to the APP can acquire the interaction information that the user A sends the digital verification code or the two-dimensional code to the user B.
The user a may be registered by transmitting its own vehicle information and user information to the server in advance as a registrant. The vehicle information may include information such as license plate number, vehicle type, vehicle color, etc., and the user information may include a face image of the user.
For a parking space, an image acquisition device can be deployed on a parking space lock at the parking space, and each time a vehicle is intended to drive into the parking space, the image acquisition device can acquire vehicle information and user information (assumed to be user B) of the current vehicle and send the acquired vehicle information and user information to a server when the parking space lock is unlocked correctly.
Therefore, the server can compare the vehicle information and the user information uploaded by the parking space lock with the user information and the vehicle information of the registrant corresponding to the parking space so as to determine whether the registrant is in parking behavior. If the parking behavior of the registrant is not the parking behavior of the registrant, the server determines that the user B corresponding to the collected user information has a leasing relation with the registrant, namely the user A.
In the above example, user a and user B are the first user and the second user, respectively. The first object feature is user information and vehicle information of user a and the second object feature is user information and vehicle information of user B. The correlation between the first object feature and the second object feature is embodied as: if the two object features are not identical, then the two are considered to have a correlation.
A data processing apparatus of one or more embodiments of the present invention will be described in detail below. Those skilled in the art will appreciate that these data processing devices may be configured using commercially available hardware components through the steps taught by the present solution.
Fig. 7 is a schematic structural diagram of a data processing device according to an embodiment of the present invention, where a server of the data processing device may manage or query order data and various transfer data of an e-commerce platform. As shown in fig. 7, the apparatus includes: a first determination module 11, a second determination module 12, a third determination module 13.
A first determining module 11 is configured to determine that the first user has funds flowing into the second user according to the funds flow data.
And a second determining module 12, configured to determine a first commodity category corresponding to the first user and a second commodity category corresponding to the second user.
And a third determining module 13, configured to determine that a supply-demand relationship exists between the first user and the second user if there is a correlation between the first commodity category and the second commodity category.
Alternatively, the first determining module 11 may be configured to: acquiring order data; and if the purchaser and the seller contained in the order data are the first user and the second user respectively, determining that funds exist for the first user and flow into the second user.
Alternatively, the first determining module 11 may be configured to: acquiring transfer data; and if the payer and the payee contained in the transfer data are the first user and the second user respectively, determining that the first user has funds to flow into the second user.
Alternatively, the second determining module 12 may be configured to: and determining a second commodity category corresponding to the second user as a category to which the commodity contained in the order data belongs.
Alternatively, the second determining module 12 may be configured to: and if the first user has an online shop, determining that the first commodity category corresponding to the first user is the commodity category contained in the online shop.
Alternatively, the second determining module 12 may be configured to: if the first user does not have an online store, a plurality of third users for transferring accounts to the first user are obtained, wherein the third users respectively have online stores;
and if the number of the plurality of third users accords with a set threshold value, determining that the first commodity category corresponding to the first user is the at least part of commodity categories.
Optionally, the second user does not have an online shop, and the second determining module 12 may be configured to: obtaining a plurality of fourth users transferring accounts to the second user, wherein the fourth users respectively have online shops; and if the number of the fourth users accords with a set threshold value, determining that the second commodity category corresponding to the second user is the at least part of commodity category.
Alternatively, the third determining module 13 may be configured to: and if the first commodity category and the second commodity category have correlation, and the order summary information between the first user and the second user accords with a set condition, determining that a supply-demand relationship exists between the first user and the second user.
Alternatively, the third determining module 13 may be configured to: and if the first commodity category and the second commodity category have correlation, and the transfer summary information between the first user and the second user accords with a set condition, determining that a supply-demand relationship exists between the first user and the second user.
Alternatively, the third determining module 13 may be configured to: and if the similarity between the first commodity category and the second commodity category is greater than or equal to a set threshold value, determining that the first commodity category and the second commodity category have correlation.
Alternatively, the third determining module 13 may be configured to: respectively acquiring a plurality of commodity categories sold by a plurality of online shops; constructing a co-occurrence relation pair corresponding to each online shop according to the combination condition of a plurality of commodity categories corresponding to each online shop, wherein one co-occurrence relation pair consists of two different commodity categories in the commodity categories; determining the coding vectors corresponding to the commodity categories according to the co-occurrence relation pairs; according to the distance between every two coding vectors, determining the similarity between two corresponding commodity categories; and if the obtained similarity is determined to be greater than or equal to a set threshold value through inquiring the obtained similarities, determining that the first commodity category and the second commodity category have correlation.
Alternatively, the third determining module 13 may be configured to: and if the first commodity category and the second commodity category have a marketing relationship, determining that the first commodity category and the second commodity category have correlation.
Alternatively, the third determining module 13 may be configured to: respectively acquiring N commodity categories purchased by each of a plurality of online shops and M commodity categories sold, wherein M and N are integers greater than or equal to 1; according to the combination situation of N commodity categories and M commodity categories corresponding to each online shop, M x N marketing relation pairs corresponding to each online shop are constructed, wherein one marketing relation pair consists of one of the N commodity categories and one of the M commodity categories; screening out target marketing relation pairs with occurrence times and conditional probability meeting a set threshold; and if the target marketing relation pair comprises a marketing relation pair formed by the first commodity category and the second commodity category, determining that the first commodity category and the second commodity category have correlation.
The apparatus shown in fig. 7 may perform the method in the foregoing embodiments, and for the portions of this embodiment not described in detail, reference may be made to the related descriptions of the foregoing embodiments, which are not repeated here.
In one possible design, the structure of the data processing apparatus shown in fig. 7 may be implemented as an electronic device, such as a server. As shown in fig. 8, the electronic device may include: a processor 21, and a memory 22. Wherein the memory 22 has executable code stored thereon which, when executed by the processor 21, causes at least the processor 21 to perform the steps of:
determining that the first user has funds flowing into the second user according to the funds flow data;
determining a first commodity category corresponding to the first user and a second commodity category corresponding to the second user;
and if the first commodity category and the second commodity category have correlation, determining that a supply-demand relationship exists between the first user and the second user.
The configuration of the network device may further include a communication interface 23, which is used to communicate with other devices or a communication network.
Additionally, embodiments of the present invention provide a non-transitory machine-readable storage medium having stored thereon executable code that, when executed by a processor of an electronic device, causes the processor to perform the steps of the foregoing embodiments.
The apparatus embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by adding necessary general purpose hardware platforms, or may be implemented by a combination of hardware and software. Based on such understanding, the foregoing aspects, in essence and portions contributing to the art, may be embodied in the form of a computer program product, which may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory. The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (13)
1. A method of data processing, comprising:
determining that the first user has funds flowing into the second user according to the funds flow data;
determining a first commodity category corresponding to the first user and a second commodity category corresponding to the second user;
if the first commodity category and the second commodity category have correlation, determining that a supply-demand relationship exists between the first user and the second user;
the first user and/or the second user have an online shop; the method further comprises the steps of:
respectively acquiring N commodity categories purchased by each of a plurality of online shops and M commodity categories sold, wherein M and N are integers greater than or equal to 1;
according to the combination situation of N commodity categories and M commodity categories corresponding to each online shop, M x N marketing relation pairs corresponding to each online shop are constructed, wherein one marketing relation pair consists of one of the N commodity categories and one of the M commodity categories;
screening out target marketing relation pairs with occurrence times and conditional probability meeting a set threshold;
and if the target marketing relation pair comprises a marketing relation pair formed by the first commodity category and the second commodity category, determining that the first commodity category and the second commodity category have correlation.
2. The method of claim 1, wherein determining that the first user has funds flowing into the second user based on the funds flow data comprises:
acquiring order data;
and if the purchaser and the seller contained in the order data are the first user and the second user respectively, determining that funds exist for the first user and flow into the second user.
3. The method of claim 1, wherein determining that the first user has funds flowing into the second user based on the funds flow data comprises:
acquiring transfer data;
and if the payer and the payee contained in the transfer data are the first user and the second user respectively, determining that the first user has funds to flow into the second user.
4. The method of claim 2, wherein the determining the second category of merchandise corresponding to the second user comprises:
and determining a second commodity category corresponding to the second user as a category to which the commodity contained in the order data belongs.
5. A method according to claim 2 or 3, wherein said determining a first category of merchandise to which the first user corresponds comprises:
And if the first user has an online shop, determining that the first commodity category corresponding to the first user is the commodity category contained in the online shop.
6. A method according to claim 2 or 3, wherein said determining a first category of merchandise to which the first user corresponds comprises:
if the first user does not have an online store, a plurality of third users for transferring accounts to the first user are obtained, wherein the third users respectively have online stores;
and if the number of the plurality of third users accords with a set threshold value, determining that the first commodity category corresponding to the first user is the at least part of commodity categories.
7. The method of claim 3, wherein the second user does not have an online marketplace, and wherein the determining the second category of merchandise to which the second user corresponds comprises:
obtaining a plurality of fourth users transferring accounts to the second user, wherein the fourth users respectively have online shops;
and if the number of the fourth users accords with a set threshold value, determining that the second commodity category corresponding to the second user is the at least part of commodity category.
8. The method of claim 2, wherein determining that a supply-demand relationship exists between the first user and the second user if there is a correlation between the first category of merchandise and the second category of merchandise, comprises:
and if the first commodity category and the second commodity category have correlation, and the order summary information between the first user and the second user accords with a set condition, determining that a supply-demand relationship exists between the first user and the second user.
9. The method of claim 3, wherein determining that a supply-demand relationship exists between the first user and the second user if there is a correlation between the first category of merchandise and the second category of merchandise, comprises:
and if the first commodity category and the second commodity category have correlation, and the transfer summary information between the first user and the second user accords with a set condition, determining that a supply-demand relationship exists between the first user and the second user.
10. The method according to claim 1, wherein the method further comprises:
And if the similarity between the first commodity category and the second commodity category is greater than or equal to a set threshold value, determining that the first commodity category and the second commodity category have correlation.
11. The method according to claim 10, wherein the method further comprises:
respectively acquiring a plurality of commodity categories sold by a plurality of online shops;
constructing a co-occurrence relation pair corresponding to each online shop according to the combination condition of a plurality of commodity categories corresponding to each online shop, wherein one co-occurrence relation pair consists of two different commodity categories in the commodity categories;
determining the coding vectors corresponding to the commodity categories according to the co-occurrence relation pairs;
according to the distance between every two coding vectors, determining the similarity between two corresponding commodity categories;
and if the similarity between the first commodity category and the second commodity category is greater than or equal to a set threshold, determining that there is a correlation between the first commodity category and the second commodity category includes:
and if the obtained similarity is determined to be greater than or equal to a set threshold value through inquiring the obtained similarities, determining that the first commodity category and the second commodity category have correlation.
12. A data processing apparatus, comprising:
the first determining module is used for determining that the first user has funds to flow into the second user according to the funds flow data;
the second determining module is used for determining a first commodity category corresponding to the first user and a second commodity category corresponding to the second user;
a third determining module, configured to determine that a supply-demand relationship exists between the first user and the second user if there is a correlation between the first commodity category and the second commodity category;
the third determining module is further configured to: respectively acquiring N commodity categories purchased by each of a plurality of online shops and M commodity categories sold, wherein M and N are integers greater than or equal to 1; according to the combination situation of N commodity categories and M commodity categories corresponding to each online shop, M x N marketing relation pairs corresponding to each online shop are constructed, wherein one marketing relation pair consists of one of the N commodity categories and one of the M commodity categories; screening out target marketing relation pairs with occurrence times and conditional probability meeting a set threshold; and if the target marketing relation pair comprises a marketing relation pair formed by the first commodity category and the second commodity category, determining that the first commodity category and the second commodity category have correlation.
13. An electronic device, comprising: a memory, a processor; wherein the memory has stored thereon executable code which, when executed by the processor, causes the processor to perform the data processing method of any of claims 1 to 12.
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