CN-122027680-A - Data generation method and data pushing method

CN122027680ACN 122027680 ACN122027680 ACN 122027680ACN-122027680-A

Abstract

The application provides a data generation method and a data pushing method, and the data generation method comprises the steps of responding to a data access request sent by a user, obtaining a user identifier and user information of the user, processing the user information in a preset time period to generate n-level original data, wherein the n-level original data comprises n-1-level original data, n is a positive integer, and generating target data based on the user identifier and the n-level original data.

Inventors

DENG FANGYUAN
XU RUIBO

Assignees

小船出海教育科技（北京）有限公司

Dates

Publication Date: 20260512
Application Date: 20251124

Claims (10)

1. A method of data generation, the method comprising: responding to a data access request sent by a user, and acquiring a user identification and user information of the user; Processing the user information in a preset time period to generate n-level original data, wherein the n-level original data comprises n-1-level original data, and n is a positive integer; and generating target data based on the user identification and the n-level original data.
2. The method for generating data according to claim 1, wherein the obtaining the user identification and the user information of the user in response to the data access request sent by the user comprises: Responding to a data request sent by a user, and acquiring a user identification of the user; And acquiring the user information based on a preset user information acquisition rule, wherein the preset user information acquisition rule comprises at least one of a front-end page embedded point, a rear-end service log and a database.
3. The data generating method according to claim 1 or 2, wherein the processing the user information for a preset period of time generates n-level raw data, comprising: Acquiring the request quantity of data access requests sent by a user in a preset first time period based on a user identifier; when the request quantity is a positive integer, processing the user information in a preset second time period to generate n-level original data, and resetting the request quantity; Wherein the second time period is later than the first time period.
4. The data generation method according to claim 3, wherein the generating target data based on the user identification and the n-level raw data comprises: Based on the n-level original data, sequentially acquiring the association degree of the original data of each level by taking the 1 st-level original data as the beginning; When the association degree of the original data at the same level is lower than a preset value, generating n-th-level original data, wherein the number of the n-th-level original data is at least one; Generating first data based on the nth level original data and the n-1 th level original data, and generating second data based on the user identification and the nth level original data; Target data is generated based on the first data and the second data.
5. The data generation method according to claim 4, characterized in that the method further comprises: acquiring the request quantity of the data access requests, and generating a second time period when the request quantity exceeds a first preset value; And/or the number of the groups of groups, And acquiring the historical access frequency of the data access request, and determining the time period of a second preset value of the historical access frequency as a second time period.
6. The data generation method according to claim 1 or 5, wherein the user information includes information content including at least one of a photo search class, a special learning class, and an auxiliary work class, and an information dimension including at least one of a basic operation record, process data, result data, and time series data.
7. A data pushing method, the method comprising: responding to a target data pushing request, and acquiring the weight of the target data; Pushing the target data to a target area of a target page based on the weight of the target data; The target data is generated based on the data generation method of any one of claims 1 to 6.
8. The data pushing method of claim 7, further comprising: acquiring the operation behaviors of the user on the target page, acquiring n-1 level original data based on the operation behaviors, and pushing the n-1 level original data to the target page until the user stops the operation behaviors.
9. A data generating apparatus, the apparatus comprising: the response unit is used for responding to a data access request sent by a user and acquiring the user identification and the user information of the user; The first generation unit is used for processing the user information in a preset time period to generate n-level original data, wherein the n-level original data comprises n-1-level original data, and n is a positive integer; And the second generation unit is used for generating target data based on the user identification and the n-level original data.
10. A data pushing device, the device comprising: the response unit is used for responding to the target data pushing request and acquiring the weight of the target data; A pushing unit, configured to push the target data to a target area of a target page based on a weight of the target data; The target data is generated based on the data generating apparatus of claim 9.

Description

Data generation method and data pushing method Technical Field The present application relates to the field of data processing technologies, and in particular, to a data generating method and a data pushing method. Background This section is intended to provide a background or context to the embodiments of the application that are recited in the claims. The description herein is not to be taken as an admission of prior art as including in this section. In the field of online education, along with increasing demands of users on personalized learning experience, how to accurately match users with learning tools and contents becomes an industry pain point, and most education platforms currently begin to optimize recommendation by using user behavior data. However, the prior art still has obvious limitations, such as that data acquisition stays in shallow information of clicking times, using time length and the like, capturing of deep behaviors of shot and searched question types, composition modification marks and the like is lacked, so that images of users are fuzzy, accurate analysis is difficult to support, recommendation logic often depends on fixed rules or delayed offline calculation, short-term learning requirement changes of users cannot be responded in real time, timeliness of recommendation results is insufficient, storage and query layers are slow in response when high-frequency query is caused by a dependency database, user experience is affected, or a storage structure is simplified for pursuing speed, association fracture of recommendation results and specific resources is caused, content is difficult to complete quickly, personalized granularity is coarse, the recommendation is limited to large-scale recommendation, specific learning stages and knowledge point requirements of users cannot be refined, and the homogenization problem is outstanding. Based on the above, the present application is highly required to propose a data generation method and a data pushing method capable of solving the above technical problems. Disclosure of Invention Aspects of the present application provide a data generating method and a data pushing method, which are used for improving the rationality of data processing and further enhancing the accuracy and reliability of services. In one aspect of the present application, there is provided a data generation method, the method including: responding to a data access request sent by a user, and acquiring a user identification and user information of the user; Processing the user information in a preset time period to generate n-level original data, wherein the n-level original data comprises n-1-level original data, and n is a positive integer; and generating target data based on the user identification and the n-level original data. Further, the step of responding to the data access request sent by the user to acquire the user identification and the user information of the user comprises the step of responding to the data request sent by the user to acquire the user identification of the user, and the step of acquiring the user information based on a preset user information acquisition rule, wherein the preset user information acquisition rule comprises at least one of a front-end page embedded point, a rear-end service log and a database. Further, the processing of the user information in the preset time period to generate n-level original data comprises the steps of obtaining the request quantity of data access requests sent by the user in the preset first time period based on user identification, processing the user information in the preset second time period to generate n-level original data when the request quantity is a positive integer, and resetting the request quantity, wherein the second time period is later than the first time period. Further, the generating of the target data based on the user identification and the n-th level of original data comprises the steps of sequentially obtaining the association degree of each level of original data based on the n-th level of original data by taking the 1 st level of original data as an initial, generating the n-th level of original data when the association degree of the original data of the same level is lower than a preset value, generating first data based on the n-th level of original data and the n-1-th level of original data, generating second data based on the user identification and the n-th level of original data, and generating the target data based on the first data and the second data. Further, the method further comprises the steps of obtaining the request quantity of the data access requests, generating a second time period when the request quantity exceeds a first preset value, and/or obtaining the historical access frequency of the data access requests, and determining the time period of the second preset value of the historical access frequency as the second time period. Further, the user informa