CN110766468A - Big data advertisement management method and device, computer equipment and storage medium - Google Patents
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Abstract
The invention relates to the technical field of computer technology, in particular to a big data advertisement management method, a device, computer equipment and a storage medium, wherein the big data advertisement management method comprises the following steps: s10: acquiring pushed advertisement data, wherein the pushed advertisement data comprises pushed advertisement type data; s20: classifying the pushed advertisement data according to the pushed advertisement type data to obtain advertisement data to be processed; s30: if an advertisement analysis request is obtained, obtaining analysis latitude data from the advertisement analysis request; s40: and acquiring data to be analyzed from the advertisement data to be processed according to the analyzed latitude data, and sending the data to be analyzed to a client for analyzing the data to be analyzed. The invention has the effects of managing and analyzing the pushed advertisement data and facilitating accurate advertisement delivery to the user in the follow-up process.
Description
Technical Field
The present invention relates to the technical field of computer technologies, and in particular, to a method and an apparatus for managing big data advertisements, a computer device, and a storage medium.
Background
Currently, with the rapid development of the internet industry, mobile advertising becomes a new mode of the current internet advertising industry. The mobile advertisement can recommend corresponding advertisement information to the corresponding user according to the consumption habit of the user.
In the existing advertisement push, whether the pushed advertisement meets the consumption habit or interest of the corresponding user needs to be judged according to the effect of the pushed advertisement, so that the data generated by the pushed advertisement needs to be managed and analyzed. The present invention has been made based on this.
Disclosure of Invention
The invention aims to provide a big data advertisement management method, a device, computer equipment and a storage medium, which can manage and analyze pushed advertisement data and facilitate accurate advertisement delivery to a user in the follow-up process.
The above object of the present invention is achieved by the following technical solutions:
a big data advertisement management method comprises the following steps:
s10: acquiring pushed advertisement data, wherein the pushed advertisement data comprises pushed advertisement type data;
s20: classifying the pushed advertisement data according to the pushed advertisement type data to obtain advertisement data to be processed;
s30: if an advertisement analysis request is obtained, obtaining analysis latitude data from the advertisement analysis request;
s40: and acquiring data to be analyzed from the advertisement data to be processed according to the analyzed latitude data, and sending the data to be analyzed to a client for analyzing the data to be analyzed.
By adopting the technical scheme, the pushed advertisement data are classified according to the pushed advertisement type data in the pushed advertisement data, the pushed advertisement data can be subjected to distributed management according to the classification result when the pushed advertisement data are stored, and the high cohesion and transparency of a distributed system are utilized, so that the definition and usability of functional application and business process when the pushed advertisement data are managed are improved, the pushed advertisement data can be conveniently and correspondingly analyzed subsequently, and the accuracy of the subsequently pushed advertisement is improved; after the advertisement analysis request for the pushed advertisement is acquired, the corresponding data to be analyzed is acquired according to the analysis latitude data, the acquired data to be analyzed can better meet the requirement of user analysis, redundant data cannot be acquired, the acquired data cannot be lost, and the analysis efficiency is improved.
The invention is further configured to: step S10 includes:
s11: acquiring advertisement push media data;
s12: and acquiring the corresponding pushed advertisement data according to the advertisement pushed media data, and associating the advertisement pushed media data with the pushed advertisement data.
By adopting the technical scheme, the corresponding pushed advertisement data is obtained according to the advertisement pushed media data, and classification can be performed according to different pushing modes of the advertisement, so that the pushed advertisement is more targeted when being analyzed.
The invention is further configured to: step S40 includes:
s41: acquiring character strings to be matched from the analyzed latitude data;
s42: and acquiring the data to be analyzed from the advertisement data to be processed through the character string to be matched.
By adopting the technical scheme, the character strings to be matched are matched in the advertisement data to be processed, and the corresponding analysis latitude data can be obtained.
The invention is further configured to: step S42 includes:
s421: acquiring data type data from the advertisement data to be processed, and taking all the data type data as a data set to be matched;
s422: performing matching query in the matching data set by using the character string to be matched;
s423: and taking the data corresponding to the data type data successfully matched and inquired as the data to be analyzed.
By adopting the technical scheme, the data type data of the advertisement data to be processed is taken as the data set to be matched, and then the character string to be matched is matched in the data set to be matched, so that the data capacity to be matched can be reduced, and the matching efficiency can be improved.
The invention is further configured to: after step S40, the big data advertisement management method further includes:
s50: obtaining an analysis result from the client;
s60: and establishing a user characteristic identification model according to the analysis result, wherein the user characteristic identification model is used for identifying the interest level data of the user for promoting the advertisement data.
By adopting the technical scheme, the user characteristic identification model is established, so that the advertisement type which the user is interested in can be identified by using the user characteristic identification model according to the previous analysis result when the user characteristic identification model is used for subsequently pushing the advertisement to the user, and then the corresponding advertisement can be accurately delivered to the user.
The second aim of the invention is realized by the following technical scheme:
a big-data advertisement management apparatus, the big-data advertisement management apparatus comprising:
the data acquisition module is used for acquiring pushed advertisement data, wherein the pushed advertisement data comprises pushed advertisement type data;
the classification module is used for classifying the pushed advertisement data according to the pushed advertisement type data to obtain advertisement data to be processed;
the request acquisition module is used for acquiring analysis latitude data from the advertisement analysis request if the advertisement analysis request is acquired;
and the result sending module is used for acquiring data to be analyzed from the advertisement data to be processed according to the analysis latitude data and sending the data to be analyzed to a client side for analyzing the data to be analyzed.
By adopting the technical scheme, the pushed advertisement data are classified according to the pushed advertisement type data in the pushed advertisement data, the pushed advertisement data can be subjected to distributed management according to the classification result when the pushed advertisement data are stored, and the high cohesion and transparency of a distributed system are utilized, so that the definition and usability of functional application and business process when the pushed advertisement data are managed are improved, the pushed advertisement data can be conveniently and correspondingly analyzed subsequently, and the accuracy of the subsequently pushed advertisement is improved; after the advertisement analysis request for the pushed advertisement is acquired, the corresponding data to be analyzed is acquired according to the analysis latitude data, the acquired data to be analyzed can better meet the requirement of user analysis, redundant data cannot be acquired, the acquired data cannot be lost, and the analysis efficiency is improved.
The third object of the invention is realized by the following technical scheme:
a computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the big data advertisement management method when executing the computer program.
The fourth object of the invention is realized by the following technical scheme:
a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the big-data advertisement management method described above.
In conclusion, the beneficial technical effects of the invention are as follows:
1. the pushed advertisement data are classified according to the pushed advertisement type data in the pushed advertisement data, the pushed advertisement data can be subjected to distributed management according to the classification result when the pushed advertisement data are stored, and the high cohesion and transparency of a distributed system are utilized, so that the definition and usability of functional application and business flow when the pushed advertisement data are managed are improved, the pushed advertisement data can be conveniently and correspondingly analyzed subsequently, and the accuracy of subsequent pushed advertisements is improved;
2. after the advertisement analysis request for the pushed advertisement is acquired, the corresponding data to be analyzed is acquired according to the analysis latitude data, the acquired data to be analyzed can better meet the requirement of user analysis, redundant data cannot be acquired, the acquired data cannot be lost, and the analysis efficiency is improved.
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Description of the invention
FIG. 1 is a flow chart of a big data advertisement management method according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating an implementation of step S10 in a big data advertisement management method according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating an implementation of step S40 in a big data advertisement management method according to an embodiment of the present invention;
FIG. 4 is a flowchart illustrating the implementation of step S42 in the big data advertisement management method according to an embodiment of the present invention;
FIG. 5 is another flow chart of a big data advertisement management method in an embodiment of the present invention;
FIG. 6 is a schematic block diagram of a big data advertisement management apparatus in an embodiment of the present invention;
FIG. 7 is a schematic diagram of a computer device according to an embodiment of the invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
The first embodiment is as follows:
in an embodiment, as shown in fig. 1, the present invention discloses a big data advertisement management method, which specifically includes the following steps:
s10: pushed advertisement data is obtained, wherein the pushed advertisement data comprises pushed advertisement type data.
In the present embodiment, the pushed advertisement data refers to data generated by an advertisement pushed to a user over a past period of time. The pushed advertisement type data refers to the type of advertisement of each pushed advertisement data. By way of example, the pushed advertisement type data may include, but is not limited to, types of home appliances, electronics, apparel, household items, apparel, cosmetics, skin care products, food, or games.
Specifically, after each time an advertisement is pushed to a user or a group of users, corresponding advertisement content is obtained from the pushed advertisement, and corresponding pushed advertisement type data is obtained according to the advertisement content. For example, if an advertisement related to clothing is pushed to the user, it is determined that the pushed advertisement type of the pushed advertisement is an advertisement of clothing.
S20: and classifying the pushed advertisement data according to the pushed advertisement type data to obtain advertisement data to be processed.
In this embodiment, the advertisement data to be processed refers to data that needs to be analyzed and processed for advertisement.
Specifically, according to the pushed advertisement type data, advertisement contents of corresponding types are obtained, pushed advertisement data belonging to the same pushed advertisement type data are classified into one type, and after all the pushed advertisement data are classified, the pushed advertisement data of each type are used as corresponding advertisement data to be processed.
S30: and if the advertisement analysis request is obtained, obtaining analysis latitude data from the advertisement analysis request.
In this embodiment, the advertisement analysis request refers to a message requesting analysis processing of pushed advertisement data. The analysis latitude data is the kind of data that needs to be acquired from the pushed advertisement data and analyzed, triggered by the user.
Specifically, when a user needs to analyze a pushed advertisement, the analysis latitude data is formed by determining the type of data to be acquired and then the type of the data, and the analysis latitude data is sent to a server as an advertisement analysis request. And after the server side obtains the advertisement analysis request, obtaining analysis latitude data from the advertisement analysis request.
S40: and acquiring data to be analyzed from the advertisement data to be processed according to the analysis latitude data, and sending the data to be analyzed to a client for analyzing the data to be analyzed.
Specifically, according to the analysis latitude data, data to be analyzed is obtained from the advertisement data to be processed, and the data to be analyzed is sent to the client side for analyzing the data to be analyzed. The client may be the client that triggers the advertisement analysis request in step S30, or may be the client that sends the advertisement analysis request to the corresponding client according to the content in the advertisement analysis request, which is not limited herein.
In this embodiment, in the pushed advertisement data, the pushed advertisement data is classified according to the pushed advertisement type data, and when the pushed advertisement data is stored, the pushed advertisement data is distributively managed according to the classification result, and the high cohesiveness and transparency of the distributed system are utilized, so that the clearness and usability of functional application and business process when the pushed advertisement data is managed are improved, the pushed advertisement data is conveniently and correspondingly analyzed subsequently, and the accuracy of the subsequently pushed advertisement is improved; after the advertisement analysis request for the pushed advertisement is acquired, the corresponding data to be analyzed is acquired according to the analysis latitude data, the acquired data to be analyzed can better meet the requirement of user analysis, redundant data cannot be acquired, the acquired data cannot be lost, and the analysis efficiency is improved.
In an embodiment, as shown in fig. 2, in step S10, pushed advertisement data is obtained, where the pushed advertisement data includes pushed advertisement type data, which includes the following steps:
s11: advertisement push media data is obtained.
In this embodiment, the advertisement push media data is a way to push an advertisement to a user. Such as a terminal on a vehicle or a handheld mobile terminal of a user, etc.
Specifically, the advertisement push media data is acquired according to a preset setting.
S12: and acquiring corresponding pushed advertisement data according to the advertisement pushed media data, and associating the advertisement pushed media data with the pushed advertisement data.
Specifically, the number of the advertisement push media data is obtained first, and the corresponding pushed advertisement data is obtained one by one or simultaneously according to the number and is associated with the advertisement push media data. The association may be performed by establishing a corresponding data table for each type of advertisement push media data, and storing pushed advertisement data belonging to the advertisement push media data in the data table.
In an embodiment, as shown in fig. 3, in step S40, that is, according to the analysis latitude data, obtaining data to be analyzed from the advertisement data to be processed, and sending the data to be analyzed to the client for analyzing the data to be analyzed, the method specifically includes the following steps:
s41: and acquiring the character string to be matched from the analyzed latitude data.
In this embodiment, the to-be-matched character string refers to a character string used for matching out the to-be-analyzed data in the to-be-processed advertisement data.
Specifically, the latitude data is analyzed to include a plurality of types of data to be acquired, and each type of data is used as a corresponding character string to be matched.
S42: and acquiring data to be analyzed from the advertisement data to be processed through the character string to be matched.
Specifically, the character string to be matched is used to match data corresponding to the analyzed latitude data in the advertisement data to be processed, and the data is used as the data to be analyzed.
In an embodiment, as shown in fig. 4, in step S42, that is, obtaining data to be analyzed from advertisement data to be processed through a character string to be matched specifically includes the following steps:
s421: and acquiring data type data from the advertisement data to be processed, and taking all the data type data as a data set to be matched.
In this embodiment, the data set to be matched refers to a data set in which all data in the advertisement data to be processed is recorded.
Specifically, data type data corresponding to all data in the advertisement data to be processed is obtained, and the data type data and the corresponding data are placed in a hash mapping, so that the corresponding data can be mapped through the data type data.
And further, taking the data type data of the advertisement data to be processed as a data set to be matched.
S422: and performing matching query in the matching data set by using the character string to be matched.
Specifically, a matching query is performed in the matching dataset using the string to be matched.
S423: and taking the data corresponding to the data type data matched with the query as the data to be analyzed.
Specifically, if the corresponding data type data is matched from the advertisement data to be processed through matching character string matching, the corresponding data is obtained through hash mapping in step S421, and is used as the data to be analyzed.
In one embodiment, as shown in fig. 5, after step S40, the big data advertisement management method further includes:
s50: and obtaining an analysis result from the client.
Specifically, after the analysis of the client for analyzing the data to be analyzed is completed, a corresponding analysis result is obtained from the client. The obtaining mode may be uploading or triggering by an operator.
S60: and establishing a user characteristic identification model according to the analysis result, wherein the user characteristic identification model is used for identifying the interest level data of the user for promoting the advertisement data.
In the present embodiment, the user feature recognition model refers to a model for recognizing the type of advertisement in which a visitor or a user is interested.
Specifically, the manner of establishing the user feature recognition model may be the following steps:
the method comprises the following steps: and acquiring pushed advertisement data, wherein the pushed advertisement data comprises access crowd attribute data and pushed advertisement content.
In the present embodiment, the pushed advertisement data refers to data of an advertisement that has been pushed to the mobile terminal of the user. The visiting crowd attribute data refers to data of the user who obtains the advertisement, wherein the visiting crowd attribute data comprises information of age stratification, gender and occupation of the user and total number of people of the crowd. The push advertisement content refers to the specific content of the advertisement that has been pushed and the type of the corresponding advertisement. For example, the types of the advertisement include types of home appliances, electronic products, clothes, household goods, clothes, cosmetics, skin care products, foods, or games.
Specifically, when an advertisement is pushed every time, the content of the advertisement is recorded and stored, the visit personality attribute data pushed by the advertisement is counted and recorded, and the obtained pushed advertisement content and the visit personality data are used as the pushed advertisement data.
Step two: and acquiring visitor behavior data in the visitor population data.
In this embodiment, the visitor behavior data refers to a behavior of an operation performed by the visitor after acquiring the pushed advertisement.
Specifically, after the user or the visitor acquires the pushed advertisement, the behavior of the operation of the visitor on the advertisement is acquired. The specific obtaining mode may be that, according to operations that may be performed by the visitor in advance, for example, for an advertisement of an entity commodity class, after the visitor obtains the advertisement, the visitor may close the advertisement, stop on the advertisement page to browse, click the advertisement to enter a related page, or download software related to advertisement content, and set a corresponding interface, and count the operations performed by the visitor, thereby obtaining visitor behavior data in the visitor group.
Step three: and classifying the visitor behavior data according to preset visited level data to obtain visitor interest level data.
In this embodiment, the accessed level data refers to threshold data for determining the interest of the user in the advertisement through the visitor behavior data. The visitor interest level data refers to data of the degree of interest of the visitor in the pushed advertisement.
Specifically, the accessed level data is set according to an operation that may be performed by the visitor, for example, for a time that the visitor stays on the advertisement page, the corresponding accessed level data is divided by the time that the visitor stays on the advertisement page, for example, if the time that the visitor stays on the advertisement page exceeds 5 minutes, the corresponding accessed level is level a, the time that the visitor stays on the advertisement page for 3 to 5 minutes, the corresponding accessed level is level B, the time that the visitor stays on the advertisement page for 1 to 3 minutes is level C, and if the time that the visitor stays on the advertisement page is less than 1 minute, the corresponding accessed level.
Furthermore, the interest of each visitor in the pushed advertisement is calculated through the visited level data of each visitor behavior, the interest level of each visitor in the pushed advertisement is judged through setting the threshold level of the interest level, such as the first level, the second level, the third level and the like, and then the visitor behavior data is classified, namely the visitor corresponding to the visitor behavior data is classified, and then the visitor interest level data is obtained.
Step four: and acquiring advertisement type data of the pushed advertisement content, and correspondingly associating the advertisement type data with visitor interest level data according to the visited level threshold to obtain a corresponding user characteristic identification model.
In this embodiment, the access level threshold refers to a numerical value for identifying the degree of interest of the visitor to the advertisement type.
Specifically, by setting the access level threshold according to the access level data after acquiring the advertisement type data in the pushed advertisement data, and by passing the access threshold, classifying visitors corresponding to the visitor interest level data for identification in the visitor crowd data, the distribution of interest in the advertisement type data enables an advertiser triggering the pushed advertisement data to capture the interest of the visitor population in the advertisement type data, e.g., if the visitor interest level data exceeds a certain numerical value, the visitor is determined to be interested in the advertisement type, if the visitor interest level data is in a certain numerical value interval, the visitor is considered to be more interested in the advertisement type, and if the interest level of the visitor is lower than a certain value, the visitor is considered not to be interested in the advertisement type.
Furthermore, according to the visit level threshold value, after the visitors are classified, the advertisement type data and the visitor interest level data are correlated to obtain a corresponding user characteristic identification model, so that when the advertisements are pushed subsequently, the advertisements can be pushed to the crowd interested in the types of the subsequently pushed advertisements according to the user characteristic identification model.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Example two:
in one embodiment, a big data advertisement management device is provided, which corresponds to the big data advertisement management method in the above embodiments one to one. As shown in fig. 6, the big data advertisement management apparatus includes a data acquisition module 10, a classification module 20, a request acquisition module 30, and a result transmission module 40. The functional modules are explained in detail as follows:
a data obtaining module 10, configured to obtain pushed advertisement data, where the pushed advertisement data includes pushed advertisement type data;
the classification module 20 is configured to classify the pushed advertisement data according to the pushed advertisement type data to obtain advertisement data to be processed;
a request obtaining module 30, configured to obtain analysis latitude data from the advertisement analysis request if the advertisement analysis request is obtained;
and the result sending module 40 is configured to obtain data to be analyzed from the advertisement data to be processed according to the analysis latitude data, and send the data to be analyzed to the client for analyzing the data to be analyzed.
Preferably, the data acquisition module 10 comprises:
a media data obtaining sub-module 11, configured to obtain advertisement push media data;
and the association submodule 12 is configured to obtain corresponding pushed advertisement data according to the advertisement pushed media data, and associate the advertisement pushed media data with the pushed advertisement data.
Preferably, the result transmitting module 40 includes:
the character string setting submodule 41 is used for acquiring a character string to be matched from the analysis latitude data;
and the matching submodule 42 is used for acquiring data to be analyzed from the advertisement data to be processed through the character string to be matched.
Preferably, the matching sub-module 42 includes:
a data set obtaining unit 421, configured to obtain data type data from the advertisement data to be processed, and use all the data type data as a data set to be matched;
a matching unit 422, configured to perform matching query in the matching data set by using the character string to be matched;
and the data to be analyzed obtaining unit 423 is configured to use data corresponding to the data type data successfully matched with the query as data to be analyzed.
Preferably, the big data advertisement management apparatus further comprises:
a result obtaining module 50, configured to obtain an analysis result from the client;
and a model establishing module 60 for establishing a user characteristic identification model according to the analysis result, wherein the user characteristic identification model is used for identifying the interest level data of the user for promoting the advertisement data.
For the specific limitation of the big data advertisement management device, reference may be made to the above limitation of the big data advertisement management method, which is not described herein again. The modules in the big data advertisement management device can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
Example three:
in one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 7. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing pushed advertisement data and a user characteristic identification model. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a big data advertisement management method.
In one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
s10: acquiring pushed advertisement data, wherein the pushed advertisement data comprises pushed advertisement type data;
s20: classifying the pushed advertisement data according to the pushed advertisement type data to obtain advertisement data to be processed;
s30: if the advertisement analysis request is obtained, obtaining analysis latitude data from the advertisement analysis request;
s40: and acquiring data to be analyzed from the advertisement data to be processed according to the analysis latitude data, and sending the data to be analyzed to a client for analyzing the data to be analyzed.
Example four:
in one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
s10: acquiring pushed advertisement data, wherein the pushed advertisement data comprises pushed advertisement type data;
s20: classifying the pushed advertisement data according to the pushed advertisement type data to obtain advertisement data to be processed;
s30: if the advertisement analysis request is obtained, obtaining analysis latitude data from the advertisement analysis request;
s40: and acquiring data to be analyzed from the advertisement data to be processed according to the analysis latitude data, and sending the data to be analyzed to a client for analyzing the data to be analyzed.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present 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 solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A big data advertisement management method is characterized by comprising the following steps:
s10: acquiring pushed advertisement data, wherein the pushed advertisement data comprises pushed advertisement type data;
s20: classifying the pushed advertisement data according to the pushed advertisement type data to obtain advertisement data to be processed;
s30: if an advertisement analysis request is obtained, obtaining analysis latitude data from the advertisement analysis request;
s40: and acquiring data to be analyzed from the advertisement data to be processed according to the analyzed latitude data, and sending the data to be analyzed to a client for analyzing the data to be analyzed.
2. The big data advertisement management method of claim 1, wherein the step S10 comprises:
s11: acquiring advertisement push media data;
s12: and acquiring the corresponding pushed advertisement data according to the advertisement pushed media data, and associating the advertisement pushed media data with the pushed advertisement data.
3. The big data advertisement management method of claim 1, wherein the step S40 comprises:
s41: acquiring character strings to be matched from the analyzed latitude data;
s42: and acquiring the data to be analyzed from the advertisement data to be processed through the character string to be matched.
4. The big data advertisement management method of claim 3, wherein the step S42 comprises:
s421: acquiring data type data from the advertisement data to be processed, and taking all the data type data as a data set to be matched;
s422: performing matching query in the matching data set by using the character string to be matched;
s423: and taking the data corresponding to the data type data successfully matched and inquired as the data to be analyzed.
5. The big data advertisement management method of claim 1, wherein after the step S40, the big data advertisement management method further comprises:
s50: obtaining an analysis result from the client;
s60: and establishing a user characteristic identification model according to the analysis result, wherein the user characteristic identification model is used for identifying the interest level data of the user for promoting the advertisement data.
6. A big data advertisement management apparatus, characterized in that the big data advertisement management apparatus comprises:
the data acquisition module is used for acquiring pushed advertisement data, wherein the pushed advertisement data comprises pushed advertisement type data;
the classification module is used for classifying the pushed advertisement data according to the pushed advertisement type data to obtain advertisement data to be processed;
the request acquisition module is used for acquiring analysis latitude data from the advertisement analysis request if the advertisement analysis request is acquired;
and the result sending module is used for acquiring data to be analyzed from the advertisement data to be processed according to the analysis latitude data and sending the data to be analyzed to a client side for analyzing the data to be analyzed.
7. The big data advertisement management device of claim 6, wherein the data acquisition module comprises:
the media data acquisition submodule is used for acquiring advertisement push media data;
and the association submodule is used for acquiring the corresponding pushed advertisement data according to the advertisement pushed media data and associating the advertisement pushed media data with the pushed advertisement data.
8. The big data advertisement management device of claim 6, wherein the result transmission module comprises:
the character string setting submodule is used for acquiring a character string to be matched from the analysis latitude data;
and the matching submodule is used for acquiring the data to be analyzed from the advertisement data to be processed through the character string to be matched.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the big data advertisement management method according to any of claims 1 to 5 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the big data advertisement management method according to any one of claims 1 to 5.
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| CN109670094A (en) * | 2018-10-25 | 2019-04-23 | 深圳市慧动创想科技有限公司 | Processing method, device, computer equipment and the storage medium of ad data |
| CN109711873A (en) * | 2018-12-15 | 2019-05-03 | 深圳壹账通智能科技有限公司 | Intelligent advertisement delivering method, device, computer equipment and storage medium |
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| CN109271420A (en) * | 2018-09-03 | 2019-01-25 | 平安医疗健康管理股份有限公司 | Information-pushing method, device, computer equipment and storage medium |
| CN109670094A (en) * | 2018-10-25 | 2019-04-23 | 深圳市慧动创想科技有限公司 | Processing method, device, computer equipment and the storage medium of ad data |
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