CN112270008A - Method and system for accurately calculating group characteristics - Google Patents
Method and system for accurately calculating group characteristics Download PDFInfo
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- CN112270008A CN112270008A CN202011282090.XA CN202011282090A CN112270008A CN 112270008 A CN112270008 A CN 112270008A CN 202011282090 A CN202011282090 A CN 202011282090A CN 112270008 A CN112270008 A CN 112270008A
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Abstract
The application discloses a method and a system for accurately calculating group characteristics. The method comprises the following steps: the method comprises the steps that a first system obtains first characteristic information of a first crowd packet; the first system determining at least one tag information of the first crowd bag according to the first feature information, the at least one tag information including the first feature information; the first system sends the at least one tag information of the first crowd package to a second system, so that the second system matches the at least one tag information of the first crowd package with data information in a database and determines target characteristic information of a target crowd package. Data mining can be used as a data base of business analysis by calculating relevant characteristics of the target crowd packet.
Description
Technical Field
The embodiment of the application relates to the field of big data processing, in particular to a method and a system for accurately calculating group characteristics.
Background
With the rapid development of big data technology, many enterprises are currently using big data to make business decisions and business operations. Due to the maturity and wide application of big data related technologies, the security and privacy issues of big data are more and more emphasized. At present, data interaction between a plurality of enterprises is basically to perform irreversible encryption on data of two parties by using the same rule, transmit the encrypted data to a database of the other party for data matching, and directly use a matched result for business application, wherein the matched result is also subjected to irreversible encryption.
In the prior art, different enterprises have different channels for acquiring data, for example, the matching rate is low when the data of two parties are matched at an ID level, and because the data at the ID level cannot be exported, the two parties are lack of basic data when performing relatively complex technical statistics on the database and data interaction, the enterprise lacks of basic data for business analysis.
Disclosure of Invention
The embodiment of the application provides a method and a system for accurately calculating group characteristics, which are used for improving more basic data for business analysis.
The first aspect of the embodiments of the present application provides a method for accurately calculating group characteristics, including:
the method comprises the steps that a first system obtains first characteristic information of a first crowd packet;
the first system determining at least one tag information of the first crowd bag according to the first feature information, the at least one tag information including the first feature information;
the first system sends the at least one tag information of the first crowd package to a second system, so that the second system matches the at least one tag information of the first crowd package with data information in a database and determines target characteristic information of a target crowd package.
Optionally, the determining, by the first system, at least one tag information of the first crowd package according to the first feature information includes:
the first system extracts at least one characteristic information of the first crowd packet in the first system database according to the first characteristic information;
and the first system counts at least one label information of the crowd packet according to the at least one characteristic information.
Optionally, before the first system sends the at least one tag information of the first crowd package to the second system, the method further comprises:
the first system performs an encryption operation on first characteristic information data of the first crowd packet.
Optionally, the first feature information is feature information with the maximum saturation in the feature information of the first crowd packet.
A second aspect of the embodiments of the present application provides a method for accurately calculating group characteristics, including:
the second system receives at least one label information of the first crowd packet sent by the first system;
the second system filters first label information of the first crowd package according to at least one label information of the first crowd package;
the second system matches the first label information with data information in a database and then determines a target crowd packet;
and the second system extracts the target characteristic information corresponding to the target crowd packet.
Optionally, the determining, by the second system, the target crowd packet after matching the first tag information with the data information in the database includes:
the second system matches corresponding data information in a database according to the first label information;
and the second system determines a corresponding target crowd packet by counting the data information meeting the preset conditions.
Optionally, the extracting, by the second system, feature information corresponding to the target crowd packet includes:
and the second system extracts target characteristic information according to the characteristic information corresponding to the target crowd packet.
Optionally, before the second system determines the target crowd packet after matching the first tag information with the data information in the database, the method further includes:
the second system performs an encryption operation on the first tag information.
A third aspect of the embodiments of the present application provides a system for accurately calculating group characteristics, including:
the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for a first system to acquire first characteristic information of a first crowd;
a first determining unit, configured to determine, by the first system, at least one tag information of the first crowd packet according to the first feature information, where the at least one tag information includes the first feature information;
and the sending unit is used for sending the at least one label message of the first crowd package to a second system by the first system, so that the second system matches the at least one label message of the first crowd package with the data message in the database and determines the target characteristic message of the target crowd package.
Optionally, the first determining unit includes:
an extraction module, configured to extract, by the first system, at least one feature information of the first crowd package in the first system database according to the first feature information;
and the counting module is used for counting at least one piece of label information of the crowd packet according to the at least one piece of characteristic information by the first system.
Optionally, before the sending unit, the system further includes:
a first execution unit, configured to perform an encryption operation on first characteristic information data of the first crowd packet by the first system.
A fourth aspect of the embodiments of the present application provides a system for accurately calculating group characteristics, including:
the receiving unit is used for receiving at least one piece of label information of the first crowd packet sent by the first system by the second system;
the screening unit is used for screening the first label information of the first crowd package according to at least one label information of the first crowd package by the second system;
the second determining unit is used for determining a target crowd packet after the second system matches the first label information with data information in a database;
and the extraction unit is used for extracting the target characteristic information corresponding to the target crowd packet by the second system.
Optionally, the second determining unit includes:
the matching module is used for matching corresponding data information in a database by the second system according to the first label information;
and the determining module is used for determining the corresponding target crowd packet by the second system through counting the data information meeting the preset conditions.
Optionally, the extracting unit includes:
and the extraction module is used for extracting the target characteristic information according to the characteristic information corresponding to the target crowd packet by the second system.
Optionally, before the second determining unit, the system further includes:
and the second execution unit is used for executing encryption operation on the first label information by the second system.
A fifth aspect of the embodiments of the present application provides a system for accurately calculating group characteristics, including:
the device comprises a processor, a memory, an input and output unit and a bus;
the processor is connected with the memory, the input and output unit and the bus;
the processor specifically performs the following operations:
acquiring first characteristic information of a first crowd packet;
determining at least one tag information of the first crowd packet according to the first feature information, the at least one tag information including the first feature information;
and sending the at least one tag information of the first crowd package to a second system, so that the second system matches the at least one tag information of the first crowd package with data information in a database, and determines target characteristic information of a target crowd package.
Optionally, the processor is further configured to perform the operations of any of the alternatives of the first aspect.
A sixth aspect of the embodiments of the present application provides a system for accurately calculating group characteristics, including:
the device comprises a processor, a memory, an input and output unit and a bus;
the processor is connected with the memory, the input and output unit and the bus;
the processor specifically performs the following operations:
receiving at least one label information of a first crowd packet sent by a first system;
screening first label information of the first crowd package according to at least one label information of the first crowd package;
matching the first label information with data information in a database and then determining a target crowd packet;
and extracting target characteristic information corresponding to the target crowd packet.
Optionally, the processor is further configured to perform the operations of any of the alternatives of the second aspect.
A seventh aspect of embodiments of the present application provides a computer-readable storage medium for accurately calculating group characteristics, including:
the computer-readable storage medium has a program stored thereon, which when executed on a computer performs the method for precision population feature computation as described above.
According to the technical scheme, the embodiment of the application has the following advantages: in the application, when the feature information of a user is exposed in a first system, the first system obtains first feature information of a first crowd package, the first system determines at least one piece of label information of the first crowd package according to the first feature information, and then sends the first label information to a second system to perform data interaction of the at least one piece of label information of the first crowd package, so that the second system matches the label information with data information in a database to determine target feature information of the target crowd package. Thus, the second system can use the target characteristic information as a data base of business analysis.
Drawings
FIG. 1 is a schematic flow chart illustrating an embodiment of a method for accurately calculating group characteristics according to an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart illustrating a method for accurately calculating group characteristics according to another embodiment of the present disclosure;
FIG. 3 is a schematic flow chart illustrating a method for accurately calculating group characteristics according to another embodiment of the present disclosure;
FIG. 4 is a schematic flow chart illustrating a method for accurately calculating group characteristics according to another embodiment of the present disclosure;
FIG. 5 is a schematic structural diagram of an embodiment of a system for accurately calculating group characteristics according to an embodiment of the present disclosure;
FIG. 6 is a schematic structural diagram of another embodiment of a system for accurately calculating group characteristics according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of another embodiment of a system for accurately calculating group characteristics according to an embodiment of the present application.
Detailed Description
The embodiment of the application provides a method and a system for accurately calculating group characteristics, which are used for improving more basic data for business analysis.
Referring to fig. 1, an embodiment of a method for accurately calculating group characteristics in an embodiment of the present application includes:
101. the method comprises the steps that a first system obtains first characteristic information of a first crowd packet;
in practical application, data interaction between enterprises basically includes that data of two parties are irreversibly encrypted by the same rule, the encrypted data are transmitted to a database of the other party for data matching, and a matched result is directly applied and used as a functional technology. Because the channels of acquiring data of different enterprises are different, the matching rate of the data of both parties is low when the data are matched, so that the data cannot be exported, and most enterprises only stay on some simple statistics and lack basic data of business analysis based on the reasons of the technology, the complexity and the like.
In this embodiment, a certain number of crowd packets are extracted according to tag details in an enterprise, and data of system-matched crowd packets are encrypted and then subjected to related data analysis, first, the system acquires first feature information of a first crowd packet, for example, for a certain advertisement delivery activity, the system acquires user IDs of all exposure crowds delivered in one month in the activity delivery activity, all the exposure crowds are used as the first crowd packet, exposure is equivalent to that a user can see the advertisement, if the user needs the information, the user can directly click and purchase the information through the advertisement, and then the system screens feature information with the maximum saturation of user portrait features as the first feature information.
102. The first system determines at least one tag information of the first crowd package according to the first characteristic information, the at least one tag information including the first characteristic information;
in this embodiment, the system acquires the first feature information of the first crowd package and then extracts the user portrait tags of the exposed users, that is, the first crowd package, from the system database, for example, the first feature information is a user ID, and the system may query other tags of the users in the database, such as gender, age, academic history, hobbies, and the like, according to the user ID.
103. The first system sends the at least one label information of the first crowd package to the second system, so that the second system matches the at least one label information of the first crowd package with the data information in the database, and determines the target characteristic information of the target crowd package.
In this embodiment, the system queries and extracts at least one tag of the first crowd package and then sends at least one tag information to the second system, where the second system may be a cooperative enterprise, for example, an enterprise that has post-link e-commerce behavior data of advertisement activities, such as user browsing, shopping, ordering, and the like, and uploads the post-link e-commerce behavior data to the database to perform data interaction, where the first system sends the at least one tag information of the first crowd package to the second system, and the sent data may be all portrait feature information of the first crowd package, or may be first feature information, for example, user ID information, and the second system may also query other portrait tag information of the user in the database according to the user ID.
In the embodiment, the crowd packet is extracted to perform relevant data mining analysis on the e-commerce behavior data of the enterprise matching crowd packet, so that the mined crowd packet has sufficient stability, and a data basis is provided for further business analysis of the crowd packet.
Referring to fig. 2, another embodiment of the method for accurately calculating group characteristics in the embodiment of the present application includes:
201. the method comprises the steps that a first system obtains first characteristic information of a first crowd packet;
step 201 in this embodiment is similar to step 101 in the previous embodiment, and is not described herein again.
202. The first system extracts at least one piece of feature information of the first crowd packet in a first system database according to the first feature information;
in this embodiment, when the first system acquires the first feature information of the first personal group package, at least one piece of feature information of the first personal group package may be queried in the database according to the first feature information, for example, the user ID information is used as the first feature information, and the system queries at least one piece of feature information of the user, such as the gender, the mobile phone number, and the communication address, according to the user ID information.
203. The first system counts at least one label information of the crowd package according to at least one characteristic information;
in this embodiment, the tag information may be user ID information of the crowd packet, a crowd packet magnitude label, other age tag information, and the like, for example, the first system counts at least one tag information of the crowd packet ID, the crowd packet label details, the crowd packet magnitude, and the like according to feature information such as the user ID, the gender, the age, and the mobile phone number of the first crowd packet. The statistical crowd packet label information is beneficial to providing a data basis for subsequent data interaction statistics.
204. The first system executes encryption operation on first characteristic information data of the first crowd packet;
in this embodiment, the first system sends at least one piece of tag information of the first group of people package to the second system, and performs MD5 encryption operation on the first feature information of the first group of people package, where the encryption is irreversible encryption, that is, the encrypted data cannot be decrypted and traced back, so that the security of data interaction is improved.
205. The first system sends the at least one label information of the first crowd package to the second system, so that the second system matches the at least one label information of the first crowd package with the data information in the database, and determines the target characteristic information of the target crowd package.
Step 205 in this embodiment is similar to step 103 in the previous embodiment, and is not described herein.
Referring to fig. 3, another embodiment of the method for accurately calculating group characteristics in the embodiment of the present application includes:
301. the second system receives at least one label information of the first crowd packet sent by the first system;
in this embodiment, at least one piece of tag information may be one piece of tag information, or may be multiple pieces of tag information, where a low-recognition-amount tag of the first crowd packet may be set to perform data interaction, so as to avoid generating tag information with higher saturation to perform data query, and because of the first feature information and the encryption, security during data interaction is affected.
302. The second system filters first label information of the first crowd bag according to at least one label information of the first crowd bag;
after receiving at least one tag information of the first crowd packet, the second system may filter the first tag information according to the filtering, for example, the crowd packet ID information, the gender, the age tag information, the crowd packet magnitude information, and the like, of the first crowd packet.
303. The second system matches the first label information with data information in a database and then determines a target crowd packet;
and the second system matches the crowd packet magnitude information of different first crowd packets with the user data information which is browsed by the advertisement activities and placed in the database, and further extracts the back link e-commerce behavior data of the users in a certain time period in the database according to the matched user IDs to determine a stable target crowd packet.
304. And the second system extracts target characteristic information corresponding to the target crowd packet.
In this embodiment, after the second system determines the target crowd packet, the second system may extract feature details corresponding to the crowd packet ID according to the determined target crowd packet, where the feature details are strong correlation features of a product corresponding to an advertisement campaign, and subsequent business analysis on the advertisement may perform technical and other types of analysis according to the strong correlation features.
Referring to fig. 4, another embodiment of the method for accurately calculating group characteristics in the embodiment of the present application includes:
401. the second system receives at least one label information of the first crowd packet sent by the first system;
402. the second system filters first label information of the first crowd bag according to at least one label information of the first crowd bag;
403. The second system executes encryption operation on the first label information;
in this embodiment, after receiving the data information sent by the first system, the second system needs to perform MD5 encryption on the first tag information before performing data interaction, where the encryption is irreversible encryption, that is, the encrypted data cannot be decrypted and traced back, so that the security of data interaction is improved.
404. The second system matches corresponding data information in the database according to the first label information;
in this embodiment, the second system matches the data corresponding to the first tag information in the database according to the received first tag information, for example, the first tag information is the user ID information of the first crowd packet, and for the matched user ID, may further query other tag information corresponding to the database. The queried other tag information may be matched to the information received from the first system.
405. The second system determines a corresponding target crowd packet by counting data information meeting preset conditions;
in this embodiment, for the matched user ID, the back link e-commerce behavior data of the user ID in a certain time period is further extracted, for data security, the back link e-commerce behavior conversion rates such as browsing, purchase adding, order placing and the like in each time period are calculated for the first crowd packet, the uploaded crowd packets are arranged in a descending order according to the back link e-commerce behavior conversion rates, and the target crowd packets meeting preset conditions are statistically analyzed, where the preset conditions may be set as crowd packets which are ranked ahead and have stable performance in a certain time period.
406. And the second system extracts target characteristic information according to the characteristic information corresponding to the target crowd packet.
In this embodiment, after the second system determines the target crowd packet, the second system may extract the feature details corresponding to the crowd packet ID according to the determined target crowd packet, and provide basic data about the target crowd packet system that needs to perform business analysis.
Referring to fig. 5, a detailed description is provided below of a system for accurately calculating group characteristics in an embodiment of the present application, where an embodiment of the system for accurately calculating group characteristics in the embodiment of the present application includes:
an obtaining unit 501, configured to obtain, by a first system, first feature information of a first crowd packet;
a first determining unit 502, configured to determine, by the first system, at least one tag information of the first crowd package according to the first feature information, where the at least one tag information includes the first feature information;
a first executing unit 503, configured to perform an encryption operation on the first feature information data of the first crowd packet by the first system.
A sending unit 504, configured to send, by the first system, the at least one piece of tag information of the first crowd packet to the second system, so that the second system matches the at least one piece of tag information of the first crowd packet with the data information in the database, and determines target feature information of the target crowd packet.
In this embodiment, the first determining unit 502 may include an extracting module 5021 and a counting module 5022.
An extracting module 5021, configured to extract, by the first system, at least one piece of feature information of the first crowd package in the first system database according to the first feature information;
the statistic module 5022 is configured to count the at least one tag information of the crowd package according to the at least one feature information by the first system.
In this embodiment, after the obtaining unit 501 obtains the first feature information of the first crowd package, the extracting module 5021 in the first determining unit 502 extracts at least one piece of feature information of the first crowd package in the first system database, where the at least one piece of tag information includes at least one piece of tag information of the crowd package counted by the first feature information counting module 5022 according to the at least one piece of feature information; before sending the data information, the first execution unit 503 performs an encryption operation on the first feature information data of the first crowd packet, and the sending unit 504 sends at least one tag information of the first crowd packet to the second system, so that the second system matches the at least one tag information of the first crowd packet with the data information in the database, and determines the target feature information of the target crowd packet. The crowd packet is extracted to carry out relevant data mining analysis on the E-commerce behavior data of the enterprise matching crowd packet, so that the mined crowd packet has sufficient stability, and a data basis is provided for further business analysis of the crowd packet.
Referring to fig. 6, a system for accurately calculating group characteristics in an embodiment of the present application is described in detail below, where another embodiment of accurately calculating group characteristics in an embodiment of the present application includes:
a receiving unit 601, configured to receive, by a second system, at least one tag information of a first crowd packet sent by a first system;
the screening unit 602 is configured to screen, by the second system, first tag information of the first crowd packet according to at least one tag information of the first crowd packet;
a second executing unit 603, configured to perform an encryption operation on the first tag information by the second system;
a second determining unit 604, configured to determine the target crowd packet after the second system matches the first tag information with the data information in the database;
the extracting unit 605 is configured to extract, by the second system, target feature information corresponding to the target crowd packet.
In this embodiment, the second determining unit 604 may include a matching module 6041 and a determining module 6042.
A matching module 6041, configured to match, by the second system, corresponding data information in the database according to the first tag information;
a determining module 6042, configured to determine, by the second system, the corresponding target crowd packet by counting data information that meets a preset condition.
In this embodiment, after the receiving unit 601 of the second system receives at least one tag information of the first crowd package sent by the first system, the filtering unit 602 filters the first tag information of the first crowd package according to the at least one tag information of the first crowd package, and at the same time, the second executing unit 603 performs an encryption operation on the first tag information, the matching module 6041 in the second determining unit 604 matches corresponding data information in the database according to the first tag information, the determining module 6042 determines a corresponding target crowd package by counting data information meeting a preset condition, and finally the extracting unit 605 extracts target feature information corresponding to the target crowd package.
Referring to fig. 7, a system for accurately calculating group characteristics in an embodiment of the present application is described in detail below, where another embodiment of the system for accurately calculating group characteristics in an embodiment of the present application includes:
a processor 701, a memory 702, an input/output unit 703, a bus 704;
the processor 701 is connected with the memory 702, the input/output unit 703 and the bus 704;
the processor 701 performs the following operations:
acquiring first characteristic information of a first crowd packet;
determining at least one tag information of the first crowd package according to the first characteristic information, wherein the at least one tag information comprises the first characteristic information;
and sending the at least one label information of the first crowd packet to the second system, so that the second system matches the at least one label information of the first crowd packet with the data information in the database, and determining the target characteristic information of the target crowd packet.
Optionally, the processor 701 corresponds to the steps in the embodiments shown in fig. 1 to fig. 2, and details are not described here.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and the like.
Claims (10)
1. A method for accurately computing population characteristics, comprising:
the method comprises the steps that a first system obtains first characteristic information of a first crowd packet;
the first system determining at least one tag information of the first crowd bag according to the first feature information, the at least one tag information including the first feature information;
the first system sends the at least one tag information of the first crowd package to a second system, so that the second system matches the at least one tag information of the first crowd package with data information in a database and determines target characteristic information of a target crowd package.
2. The method of claim 1, wherein the first system determines at least one tag information for the first crowd packet based on the first characteristic information, comprising:
the first system extracts at least one characteristic information of the first crowd packet in the first system database according to the first characteristic information;
and the first system counts at least one label information of the crowd packet according to the at least one characteristic information.
3. The method of claim 1, wherein before the first system transmits at least one tag information of the first crowd package to a second system, the method further comprises:
the first system performs an encryption operation on first characteristic information data of the first crowd packet.
4. The method according to any one of claims 1 to 3, wherein the first feature information is feature information with the highest saturation among feature information of the first crowd packet.
5. A method for accurately computing population characteristics, comprising:
the second system receives at least one label information of the first crowd packet sent by the first system;
the second system filters first label information of the first crowd package according to at least one label information of the first crowd package;
the second system matches the first label information with data information in a database and then determines a target crowd packet;
and the second system extracts the target characteristic information corresponding to the target crowd packet.
6. The method of claim 5, wherein the second system matching the first tag information with data information in a database to determine a target crowd packet comprises:
the second system matches corresponding data information in a database according to the first label information;
and the second system determines a corresponding target crowd packet by counting the data information meeting the preset conditions.
7. The method of claim 1, wherein the second system extracts feature information corresponding to the target crowd packet, comprising:
and the second system extracts target characteristic information according to the characteristic information corresponding to the target crowd packet.
8. The method of any of claims 5 to 7, wherein prior to determining a target demographic group after the second system matches the first tag information with data information in a database, the method further comprises:
the second system performs an encryption operation on the first tag information.
9. A system for accurately computing population characteristics, comprising:
the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for a first system to acquire first characteristic information of a first crowd;
a first determining unit, configured to determine, by the first system, at least one tag information of the first crowd packet according to the first feature information, where the at least one tag information includes the first feature information;
and the sending unit is used for sending the at least one label message of the first crowd package to a second system by the first system, so that the second system matches the at least one label message of the first crowd package with the data message in the database and determines the target characteristic message of the target crowd package.
10. A system for accurately computing population characteristics, comprising:
the receiving unit is used for receiving at least one piece of label information of the first crowd packet sent by the first system by the second system;
the screening unit is used for screening the first label information of the first crowd package according to at least one label information of the first crowd package by the second system;
the second determining unit is used for determining a target crowd packet after the second system matches the first label information with data information in a database;
and the extraction unit is used for extracting the target characteristic information corresponding to the target crowd packet by the second system.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113590040A (en) * | 2021-07-29 | 2021-11-02 | 郑州阿帕斯数云信息科技有限公司 | Data processing method, device, equipment and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017080170A1 (en) * | 2015-11-12 | 2017-05-18 | 乐视控股（北京）有限公司 | Group user profiling method and system |
| CN108537578A (en) * | 2018-03-26 | 2018-09-14 | 杭州米趣网络科技有限公司 | Advertisement sending method based on big data and device |
| US20190102652A1 (en) * | 2016-08-31 | 2019-04-04 | Tencent Technology (Shenzhen) Company Limited | Information pushing method, storage medium and server |
| CN110969473A (en) * | 2018-09-30 | 2020-04-07 | 北京国双科技有限公司 | User label generation method and device |
| CN111339402A (en) * | 2020-02-10 | 2020-06-26 | 口碑(上海)信息技术有限公司 | Service processing method and device |
-
2020
- 2020-11-16 CN CN202011282090.XA patent/CN112270008A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017080170A1 (en) * | 2015-11-12 | 2017-05-18 | 乐视控股（北京）有限公司 | Group user profiling method and system |
| US20190102652A1 (en) * | 2016-08-31 | 2019-04-04 | Tencent Technology (Shenzhen) Company Limited | Information pushing method, storage medium and server |
| CN108537578A (en) * | 2018-03-26 | 2018-09-14 | 杭州米趣网络科技有限公司 | Advertisement sending method based on big data and device |
| CN110969473A (en) * | 2018-09-30 | 2020-04-07 | 北京国双科技有限公司 | User label generation method and device |
| CN111339402A (en) * | 2020-02-10 | 2020-06-26 | 口碑(上海)信息技术有限公司 | Service processing method and device |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113590040A (en) * | 2021-07-29 | 2021-11-02 | 郑州阿帕斯数云信息科技有限公司 | Data processing method, device, equipment and storage medium |
| CN113590040B (en) * | 2021-07-29 | 2024-03-19 | 郑州阿帕斯数云信息科技有限公司 | Data processing method, device, equipment and storage medium |
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