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CN-121996673-A - Data processing method and device, electronic equipment, medium and product

CN121996673ACN 121996673 ACN121996673 ACN 121996673ACN-121996673-A

Abstract

The application discloses a data processing method, a data processing device, electronic equipment, a medium and a product. And acquiring user behavior data matched with the data query conditions, and integrating the user behavior data according to the user identification corresponding to the user behavior data to obtain behavior sequence data of each user. And carrying out data analysis processing on the behavior sequence data of each user according to the analysis type indication information to obtain a first analysis result, wherein the first analysis result comprises the data analysis result of each user. By the method, the data analysis efficiency can be improved, and different data analysis requirements can be met.

Inventors

  • ZHANG BIN

Assignees

  • 马上消费金融股份有限公司

Dates

Publication Date
20260508
Application Date
20241101

Claims (15)

  1. 1. A method of data processing, the method comprising: Receiving a data analysis request, wherein the data analysis request comprises data query conditions and analysis type indication information; Acquiring user behavior data matched with the data query conditions, and integrating the user behavior data according to user identifications corresponding to the user behavior data to obtain behavior sequence data of each user; And carrying out data analysis processing on the behavior sequence data of each user according to the analysis type indication information to obtain a first analysis result, wherein the first analysis result comprises the data analysis result of each user.
  2. 2. The method according to claim 1, wherein the performing data analysis processing on the behavior sequence data of each user according to the analysis type indication information to obtain a first analysis result includes: acquiring a file address of a target data analysis module corresponding to the analysis type indication information; obtaining a module file of the target data analysis module from a file server by utilizing the file address; And operating the target data analysis module according to the module file of the target data analysis module, and carrying out data analysis processing on the behavior sequence data of each user through the target data analysis module to obtain the first analysis result.
  3. 3. The method of claim 2, wherein the target data analysis module is created based on a function implementation template and registered with the file address based on a registration function, the target data analysis module comprising any one of a funnel analysis module, a persistence analysis module, a path analysis module, an attribution analysis module, an interval analysis module, a life value analysis module, and a session analysis module.
  4. 4. A method according to any one of claims 1-3, wherein the method further comprises: Receiving a data storage request, wherein the data storage request is sent according to a matching result between a target hash and identifiers of a plurality of task execution servers, the data storage request comprises a first user identifier and user behavior data to be stored, and the target hash is generated according to the first user identifier; and responding to the data storage request, and storing the first user identification and the user behavior data to be stored in a local storage space in an associated mode.
  5. 5. A method according to any one of claims 1 to 3, wherein the behavior sequence data of each user includes N behavior records arranged in time sequence, N is an integer greater than or equal to 1, and the performing data analysis processing on the behavior sequence data of each user according to the analysis type indication information to obtain a first analysis result includes: Under the condition that the analysis type indication information comprises funnel analysis indication information, carrying out funnel analysis processing on N behavior records included in the behavior sequence data of each user according to the sequence from the early to the late of a time stamp to obtain a funnel set of each user, wherein the funnel set comprises M funnels, a first behavior record included in each funnel corresponds to an initial behavior step of funnel analysis, M is the number of behavior records which are positioned before the first behavior record and correspond to the initial behavior step in the N behavior records, the first behavior record corresponds to a termination behavior step of the funnel analysis, or the first behavior record is the last behavior record in the N behavior records, and one funnel comprises the first behavior record; and determining a first analysis result according to the behavior records included in each funnel in the M funnels of each user.
  6. 6. The method of claim 5, wherein funnel analysis is performed on the N behavior records included in the behavior sequence data of each user in the order from the early to the late according to the time stamp, so as to obtain a funnel set of each user, and the method comprises: Inquiring a first behavior record corresponding to an initial behavior step of funnel analysis in N behavior records included in the behavior sequence data of each user according to the sequence from the early to the late of the time stamp, generating a first funnel of each user, and adding the first behavior record into the first funnel; traversing the behavior records positioned after the first behavior record in the N behavior records according to the sequence from the early to the late of the time stamps; if the behavior step corresponding to the second behavior record currently traversed is different from the initial behavior step, adding the second behavior record into the first funnel; If the action step corresponding to the second action record is not the termination action step of the funnel analysis, continuing to traverse until the action step corresponding to the third action record currently traversed is the termination action step, adding the third action record into the first funnel, and stopping traversing; and adding the first funnel of each user into the funnel set of each user.
  7. 7. The method of claim 6, wherein the method further comprises: if the behavior step corresponding to the second behavior record currently traversed is the same as the initial behavior step, generating a second funnel of each user, and adding the second behavior record into the second funnel; Traversing the behavior records positioned after the second behavior record in the N behavior records according to the sequence from the early to the late of the time stamps; If the behavior step corresponding to the fourth behavior record currently traversed is different from the initial behavior step, adding the fourth behavior record into the second funnel; If the action step corresponding to the fourth action record is not the termination action step, continuing to traverse until the action step corresponding to the fifth action record currently traversed is the termination action step, adding the fifth action record into the second funnel, and stopping traversing; And adding the second funnel of each user into the funnel set of each user.
  8. 8. The method of claim 5, wherein determining the first analysis result from the behavior record included in each of the M funnels of each user comprises: Determining an optimal transformation behavior sequence of each user according to behavior records in M funnels included in a funnel set of each user in the sequence from late to early according to the time stamps; Determining a grouping value sequence of each user according to a grouping value corresponding to the behavior record included in the optimal conversion behavior sequence of each user; Taking the optimal transformation behavior sequence, the grouping value sequence and the transformation depth of each user as data analysis results of each user, wherein the transformation depth is determined according to the number of behavior steps included in the optimal transformation behavior sequence; And determining a first analysis result according to the data analysis result of each user.
  9. 9. The method of claim 8, wherein the determining the optimal transformation behavior sequence for each user according to the behavior records in the M funnels included in the funnel set of each user in the order from late to early according to the time stamps comprises: Sequencing the behavior records of M funnels included in the funnel set of each user according to the time from the early to the late to obtain L behavior records of each user, wherein the last behavior record in the L behavior records is the behavior record corresponding to the termination behavior step, or the last behavior record in the L behavior records is the last behavior record in the N behavior records, wherein L is an integer greater than or equal to 0, and L is smaller than or equal to N; Acquiring corresponding I behavior records of the L behavior records of each user in an updating time window, wherein I is an integer greater than or equal to 0 and is smaller than or equal to L; Inquiring the I behavior records according to the sequence of the time stamps from late to early and the behavior steps included in the funnel analysis, and determining a target behavior sequence matched with the behavior steps included in the funnel analysis; and taking the target behavior sequence as an optimal transformation behavior sequence of each user.
  10. 10. The method of claim 8, wherein the data analysis request further includes screening indication information, the method further comprising: Screening the grouping value sequence included in the data analysis result of each user based on the grouping value indicated by the screening indication information, screening S users from at least one user, wherein S is an integer greater than or equal to 0; and taking the data analysis results of the S users as first analysis results.
  11. 11. The method of claim 8, wherein the data analysis request further includes conversion time interval indication information, the method further comprising: Acquiring time stamps of all behavior steps in the optimal conversion behavior sequence of each user; determining a conversion time interval of each user according to the time stamp of each behavior step included by each user; And taking the shortest conversion time interval in the conversion time intervals of each user as the optimal conversion time interval.
  12. 12. A data processing apparatus, the apparatus comprising: The receiving and transmitting unit is used for receiving a data analysis request, wherein the data analysis request comprises a data query condition and analysis type indication information; The acquisition unit is used for acquiring user behavior data matched with the data query conditions, and integrating the user behavior data according to the user identification corresponding to the user behavior data to obtain behavior sequence data of each user; And the processing unit is used for carrying out data analysis processing on the behavior sequence data of each user according to the analysis type indication information to obtain a first analysis result, wherein the first analysis result comprises the data analysis result of each user.
  13. 13. An electronic device, the electronic device comprising: a processor adapted to implement one or more computer programs, and A computer readable storage medium storing one or more computer programs adapted to be loaded by the processor and to perform the data processing method of any of claims 1-11.
  14. 14. A computer readable storage medium, characterized in that the computer readable storage medium stores one or more computer programs adapted to be loaded by a processor and to perform the data processing method according to any of claims 1-11.
  15. 15. A computer program product, characterized in that the computer program product comprises a computer program, which computer program is stored in a computer-readable storage medium, from which computer-readable storage medium a processor of an electronic device reads, which computer program is executed by a processor, which computer program causes the electronic device to carry out the data processing method according to any one of claims 1-11.

Description

Data processing method and device, electronic equipment, medium and product Technical Field The present application relates to the field of computer technology, and in particular to a data processing method, a data processing apparatus, an electronic device, a computer readable storage medium and a computer program product. Background A User Behavior analysis system (User Behavior ANALYSIS SYSTEM) is an analysis system popular in the operation analysis industry and the data analysis industry, and is mainly used for changing or adjusting the operation strategy of an enterprise through the calculation results of various models in the analysis system. The main analysis models include funnel analysis, retention analysis, attribution analysis, path analysis, interval analysis, LTV (Life Time Value) analysis, and the like. Currently, a popular computing implementation utilizes Clickhouse (a columnar database management system, dedicated to high performance data analysis and data warehouse applications) for user behavior analysis. In ClickHouse, behavior data of users are stored in clusters in a scattered manner, and as the data volume increases, the query performance cannot meet the requirement of being capable of real-time query, more and more analysis and calculation can only be performed in offline analysis, and operation indexes cannot be obtained in real time, so that a certain delay is brought to the operation of a real-time strategy. Therefore, how to improve the data analysis efficiency is a technical problem to be solved. Disclosure of Invention The embodiment of the application provides a data processing method and device, electronic equipment, media and products, which can improve the data analysis efficiency and meet different data analysis requirements. The embodiment of the application discloses a data processing method, which comprises the following steps: Receiving a data analysis request, wherein the data analysis request comprises data query conditions and analysis type indication information; Acquiring user behavior data matched with the data query conditions, and integrating the user behavior data according to user identifications corresponding to the user behavior data to obtain behavior sequence data of each user; And carrying out data analysis processing on the behavior sequence data of each user according to the analysis type indication information to obtain a first analysis result, wherein the first analysis result comprises the data analysis result of each user. In one aspect, an embodiment of the present application discloses a data processing apparatus, including: The receiving and transmitting unit is used for receiving a data analysis request, wherein the data analysis request comprises a data query condition and analysis type indication information; The acquisition unit is used for acquiring user behavior data matched with the data query conditions, and integrating the user behavior data according to the user identification corresponding to the user behavior data to obtain behavior sequence data of each user; And the processing unit is used for carrying out data analysis processing on the behavior sequence data of each user according to the analysis type indication information to obtain a first analysis result, wherein the first analysis result comprises the data analysis result of each user. In one aspect, an embodiment of the application discloses an electronic device, which includes a processor adapted to implement one or more computer programs, and a computer-readable storage medium storing one or more computer programs adapted to be loaded by the processor and to perform the data processing method described above. In one aspect, a computer readable storage medium is disclosed, the computer readable storage medium storing one or more computer programs adapted to be loaded by a processor and to perform the data processing method described above. In one aspect, embodiments of the present application disclose a computer program product comprising a computer program stored in a computer readable storage medium. The processor of the electronic device reads the computer program from the computer-readable storage medium, and the processor executes the computer program so that the electronic device performs the data processing method described above. In the embodiment of the application, a task execution server receives a data analysis request, wherein the data analysis request comprises a data query condition and analysis type indication information. And acquiring user behavior data matched with the data query conditions, and integrating the user behavior data according to the user identification corresponding to the user behavior data to obtain behavior sequence data of each user. Because the local storage space of the task execution server stores the user behavior data matched with the data query condition, connection with other servers is not required to be established to acquire th