阅读说明：本技术 用户行为数据的处理方法及装置、电子设备 (User behavior data processing method and device and electronic equipment ) 是由 艾宝艺 于 2021-09-08 设计创作，主要内容包括：本发明实施例提供了一种用户行为数据的处理方法及装置、电子设备,该方法包括：获取多个业务场景对应的行为数据集合,其中,行为数据集合包括每一业务场景下的用户行为数据；确定预设配置文件中每一业务场景对应的配置信息,其中,配置信息包括业务场景下所需监控的用户行为数据的标识以及所需监控的用户行为数据的统计方式；基于配置信息中的标识,从行为数据集合中筛选得到每一业务场景下所需监控的用户行为数据；基于配置信息中的统计方式,分别统计每一业务场景下所需监控的用户行为数据,得到并显示每一业务场景下的数据统计结果。本发明实施例,无需针对每一业务场景开发埋点代码,从而可以减少出错的情况发生,提升开发效率。(The embodiment of the invention provides a method and a device for processing user behavior data and electronic equipment, wherein the method comprises the following steps: acquiring behavior data sets corresponding to a plurality of service scenes, wherein the behavior data sets comprise user behavior data in each service scene; determining configuration information corresponding to each service scene in a preset configuration file, wherein the configuration information comprises an identifier of user behavior data to be monitored in the service scene and a statistical mode of the user behavior data to be monitored; based on the identification in the configuration information, user behavior data required to be monitored in each service scene is obtained by screening from the behavior data set; and respectively counting the user behavior data to be monitored in each service scene based on the counting mode in the configuration information, and obtaining and displaying the data counting result in each service scene. According to the embodiment of the invention, the embedded point code does not need to be developed aiming at each service scene, so that the occurrence of errors can be reduced, and the development efficiency is improved.)

1. A method for processing user behavior data, the method comprising:

acquiring behavior data sets corresponding to a plurality of service scenes, wherein the behavior data sets comprise user behavior data in each service scene;

determining configuration information corresponding to each service scene in a preset configuration file, wherein the configuration information comprises an identifier of user behavior data to be monitored in the service scene and a statistical mode of the user behavior data to be monitored;

based on the identification in the configuration information, user behavior data which needs to be monitored in each service scene is screened from the behavior data set;

and respectively counting the user behavior data to be monitored in each service scene based on the statistical mode in the configuration information, and obtaining and displaying the data statistical result in each service scene.

2. The method of claim 1, wherein the screening the behavior data set for the user behavior data required to be monitored in each of the service scenarios based on the identifier in the configuration information comprises:

and for each service scene, screening user behavior data with the identifier in the configuration information corresponding to the service scene from the user behavior data in the service scene based on the same screening module to obtain the user behavior data to be monitored in each service scene, wherein the screening module is a module which is created in advance and used for screening data.

3. The method according to claim 2, wherein each piece of user behavior data in each service scenario is transmitted in a JavaScript object notation format, and the identifier in the configuration information is a key-value pair data structure;

the step of screening, based on the same screening module, user behavior data having the identifier in the configuration information corresponding to the service scenario from the user behavior data in the service scenario for each service scenario to obtain the user behavior data to be monitored in each service scenario includes:

for each service scene, respectively matching all key value pairs in each piece of user behavior data in the service scene with the key value pairs indicated by the identifiers in the configuration information corresponding to the service scene based on the same screening module;

and determining user behavior data to which the successfully matched key value pairs belong as user behavior data to be monitored in the service scene aiming at each service scene, wherein the successfully matched key value pairs are key value pairs with the same key and value.

4. The method according to claim 1, wherein the separately counting user behavior data to be monitored in each of the service scenarios based on the statistical manner in the configuration information includes:

and selecting a target algorithm corresponding to the statistical mode in the configuration information corresponding to the service scene from the same algorithm set for calculating according to the user behavior data required to be monitored in each service scene.

5. The method of claim 1, wherein before the obtaining the behavior data sets corresponding to the plurality of service scenarios, the method further comprises:

receiving an identifier and a statistical mode input by a user aiming at each service scene;

and generating the configuration file based on the identification and the statistical mode.

6. The method of claim 1, wherein after obtaining and displaying the statistics for each of the service scenarios, the method further comprises:

acquiring an early warning value corresponding to each data statistical result;

and displaying early warning prompt information under the condition that at least one item of data statistical result exceeds the corresponding early warning value.

7. An apparatus for processing user behavior data, the apparatus comprising:

the data module is used for acquiring behavior data sets corresponding to a plurality of service scenes, wherein the behavior data sets comprise user behavior data in each service scene;

the configuration module is used for determining configuration information corresponding to each service scene in a preset configuration file, wherein the configuration information comprises an identifier of user behavior data to be monitored in the service scene and a statistical mode of the user behavior data to be monitored;

the first processing module is used for screening the behavior data set to obtain user behavior data required to be monitored in each service scene based on the identification in the configuration information;

and the second processing module is used for respectively counting the user behavior data required to be monitored in each service scene based on the counting mode in the configuration information, and obtaining and displaying the data counting result in each service scene.

8. The apparatus according to claim 7, wherein the first processing module is specifically configured to, for each service scenario, filter, based on a same filtering module, user behavior data having an identifier in configuration information corresponding to the service scenario from the user behavior data in the service scenario to obtain user behavior data that needs to be monitored in each service scenario, where the filtering module is a module that is created in advance and is used for filtering data.

9. The apparatus according to claim 8, wherein each piece of user behavior data in each service scenario is transmitted in a JavaScript object notation format, and the identifier in the configuration information is a key-value pair data structure;

the first processing module is specifically configured to, for each service scenario, match, based on the same screening module, all key-value pairs in each piece of user behavior data in the service scenario with key-value pairs indicated by an identifier in configuration information corresponding to the service scenario, respectively; and determining user behavior data to which the successfully matched key value pairs belong as user behavior data to be monitored in the service scene aiming at each service scene, wherein the successfully matched key value pairs are key value pairs with the same key and value.

10. The apparatus according to claim 7, wherein the second processing module is specifically configured to select, for user behavior data to be monitored in each service scenario, a target algorithm corresponding to a statistical manner in configuration information corresponding to the service scenario from a same algorithm set for calculation.

11. The apparatus of claim 7, further comprising:

the receiving module is used for receiving the identification and the statistical mode input by the user aiming at each service scene;

and the generating module is used for generating the configuration file based on the identification and the statistical mode.

12. The apparatus of claim 7, further comprising:

the first early warning module is used for acquiring an early warning value corresponding to each data statistical result;

and the second early warning module is used for displaying early warning prompt information under the condition that at least one item of data statistical result exceeds the corresponding early warning value.

13. An electronic device is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor and the communication interface are used for realizing mutual communication by the memory through the communication bus;

a memory for storing a computer program;

a processor for implementing the steps of the method for processing user behavior data according to any one of claims 1 to 6 when executing a program stored in the memory.

14. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of processing user behavior data according to any one of claims 1 to 6.

Technical Field

The invention relates to the technical field of data acquisition, in particular to a method and a device for processing user behavior data and electronic equipment.

Background

By "buried point," it is a term in the field of data collection that refers to the relevant technology and its implementation for capturing, processing, and transmitting specific user behaviors or events. Briefly, the buried point is a common technical means for collecting and counting user behavior data. Since user behavior data can be used to further optimize products or provide data support for operations, most platforms have a buried point requirement.

Generally, a platform will interface a plurality of service scenarios, and user behavior data in each service scenario needs to be collected and counted. Because the user behavior data which needs to be concerned or monitored in different service scenes are different, developers are required to develop different embedded point codes aiming at different service scenes so as to achieve the purpose of collecting and counting the user behavior data in different service scenes.

However, in the process of developing the embedded point codes for different service scenarios, because a large amount of repeated and non-repeated contents exist among the embedded point codes, the whole development process is complex, error is easy to make, and the efficiency is low.

Disclosure of Invention

In view of the above problems, embodiments of the present invention are provided to provide a method and an apparatus for processing user behavior data, and an electronic device, which overcome the above problems or at least partially solve the above problems.

In a first aspect, an embodiment of the present invention provides a method for processing user behavior data, where the method includes:

acquiring behavior data sets corresponding to a plurality of service scenes, wherein the behavior data sets comprise user behavior data in each service scene;

determining configuration information corresponding to each service scene in a preset configuration file, wherein the configuration information comprises an identifier of user behavior data to be monitored in the service scene and a statistical mode of the user behavior data to be monitored;

based on the identification in the configuration information, user behavior data which needs to be monitored in each service scene is screened from the behavior data set;

and respectively counting the user behavior data to be monitored in each service scene based on the statistical mode in the configuration information, and obtaining and displaying the data statistical result in each service scene.

Optionally, the screening, based on the identifier in the configuration information, user behavior data that needs to be monitored in each service scenario from the behavior data set includes:

and for each service scene, screening user behavior data with the identifier in the configuration information corresponding to the service scene from the user behavior data in the service scene based on the same screening module to obtain the user behavior data to be monitored in each service scene, wherein the screening module is a module which is created in advance and used for screening data.

Optionally, each piece of user behavior data in each service scene is transmitted in a JavaScript object notation (JSON) format, and the identifier in the configuration information is a key value pair data structure;

the step of screening, based on the same screening module, user behavior data having the identifier in the configuration information corresponding to the service scenario from the user behavior data in the service scenario for each service scenario to obtain the user behavior data to be monitored in each service scenario includes:

for each service scene, respectively matching all key value pairs in each piece of user behavior data in the service scene with the key value pairs indicated by the identifiers in the configuration information corresponding to the service scene based on the same screening module;

and determining user behavior data to which the successfully matched key value pairs belong as user behavior data to be monitored in the service scene aiming at each service scene, wherein the successfully matched key value pairs are key value pairs with the same key and value.

Optionally, the separately counting user behavior data to be monitored in each service scenario based on the statistical manner in the configuration information includes:

and selecting a target algorithm corresponding to the statistical mode in the configuration information corresponding to the service scene from the same algorithm set for calculating according to the user behavior data required to be monitored in each service scene.

Optionally, before the obtaining of the behavior data sets corresponding to a plurality of service scenarios, the method further includes:

receiving an identifier and a statistical mode input by a user aiming at each service scene;

and generating the configuration file based on the identification and the statistical mode.

Optionally, after obtaining and displaying the data statistics result in each of the service scenarios, the method further includes:

acquiring an early warning value corresponding to each data statistical result;

and displaying early warning prompt information under the condition that at least one item of data statistical result exceeds the corresponding early warning value.

In a second aspect, an embodiment of the present invention further provides a device for processing user behavior data, where the device includes:

the data module is used for acquiring behavior data sets corresponding to a plurality of service scenes, wherein the behavior data sets comprise user behavior data in each service scene;

the configuration module is used for determining configuration information corresponding to each service scene in a preset configuration file, wherein the configuration information comprises an identifier of user behavior data to be monitored in the service scene and a statistical mode of the user behavior data to be monitored;

the first processing module is used for screening the behavior data set to obtain user behavior data required to be monitored in each service scene based on the identification in the configuration information;

and the second processing module is used for respectively counting the user behavior data required to be monitored in each service scene based on the counting mode in the configuration information, and obtaining and displaying the data counting result in each service scene.

Optionally, the first processing module is specifically configured to, for each service scenario, filter, based on the same filtering module, user behavior data having an identifier in configuration information corresponding to the service scenario from the user behavior data in the service scenario to obtain user behavior data that needs to be monitored in each service scenario, where the filtering module is a module that is created in advance and is used for filtering data.

Optionally, each piece of user behavior data in each service scene is transmitted in a JSON format, and an identifier in the configuration information is a key-value pair data structure;

the first processing module is specifically configured to, for each service scenario, match, based on the same screening module, all key-value pairs in each piece of user behavior data in the service scenario with key-value pairs indicated by an identifier in configuration information corresponding to the service scenario, respectively; and determining user behavior data to which the successfully matched key value pairs belong as user behavior data to be monitored in the service scene aiming at each service scene, wherein the successfully matched key value pairs are key value pairs with the same key and value.

Optionally, the second processing module is specifically configured to select, for user behavior data to be monitored in each service scenario, a target algorithm corresponding to a statistical manner in configuration information corresponding to the service scenario from the same algorithm set for calculation.

Optionally, the apparatus further comprises:

the receiving module is used for receiving the identification and the statistical mode input by the user aiming at each service scene;

and the generating module is used for generating the configuration file based on the identification and the statistical mode.

Optionally, the apparatus further comprises:

the first early warning module is used for acquiring an early warning value corresponding to each data statistical result;

and the second early warning module is used for displaying early warning prompt information under the condition that at least one item of data statistical result exceeds the corresponding early warning value.

In a third aspect, an embodiment of the present invention provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, where the processor and the communication interface complete communication between the memory and the processor through the communication bus;

a memory for storing a computer program;

and the processor is used for realizing the steps of the processing method of the user behavior data when executing the program stored in the memory.

In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the processing method for user behavior data as described in the first aspect.

In the embodiment of the present invention, behavior data sets corresponding to a plurality of service scenarios may be obtained, where a behavior data set includes user behavior data in each service scenario. And acquiring the user behavior data under each service scene from the plurality of service scenes in butt joint, and providing a data basis for screening and counting the subsequent user behavior data. And then determining configuration information corresponding to each service scene in a preset configuration file, wherein the configuration information comprises an identifier of user behavior data to be monitored in the service scene and a statistical mode of the user behavior data to be monitored. That is to say, configuration files are preset, and the configuration files are generated by presetting the relevant characteristics of the user behavior data which need to be monitored in different service scenes for subsequent screening and statistical use. Therefore, when the user behavior data are screened, the user behavior data which need to be monitored in each service scene are screened from the behavior data set based on the identification in the configuration information. When the user behavior data is counted, the user behavior data required to be monitored in each service scene is respectively counted based on the counting mode in the configuration information, and the data counting result in each service scene is obtained and displayed instead of utilizing the embedded point codes corresponding to a plurality of service scenes. According to the embodiment of the invention, the embedded point code does not need to be developed aiming at each service scene, so that the occurrence of errors can be reduced, and the development efficiency is improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.

Fig. 1 is a flowchart illustrating steps of a method for processing user behavior data according to an embodiment of the present invention;

fig. 2 is a flowchart of an actual application architecture of a processing method of user behavior data according to an embodiment of the present invention;

fig. 3 is a block diagram of a device for processing user behavior data according to an embodiment of the present invention;

fig. 4 is a block diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

In various embodiments of the present invention, it should be understood that the sequence numbers of the following processes do not mean the 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.

Referring to fig. 1, an embodiment of the present invention provides a method for processing user behavior data, where the method includes:

step 101: acquiring behavior data sets corresponding to a plurality of service scenes,

it should be noted that the behavior data set includes user behavior data in each service scenario. That is to say, the behavior data sets corresponding to a plurality of service scenarios are sets formed by the user behavior data in each service scenario. The user behavior data is data representing user behavior, namely data generated when a user performs some operation behavior. It can be understood that the user behavior data related to the present invention is information authorized by the user or by each party. Preferably, the user behavior data may also include user information, and the user information (including but not limited to device information of the user, personal information of the user, etc.), related data, etc. related to the present invention are also information authorized by the user or authorized by various parties.

In general, the user behavior data acquired by the invention is similar to the user behavior data acquired by the buried point technology. Each piece of user behavior data represents one behavior of a user or one business operation under a business scenario. For example, a platform receives multiple service scenes (a recruitment service scene, a room renting service scene, a chat service scene, etc.), a user generates corresponding user behavior data when performing some behaviors in the service scenes, for example, a piece of user behavior data representing that the user issues recruitment information is generated when the user issues the recruitment information, and a piece of user behavior data representing that the user delivers resume is generated when the user delivers the resume, and then the user behavior data in the recruitment scene includes: the data of the user behavior for representing the recruitment information issued by the user and the data of the user behavior for representing the delivery resume of the user. Here, only two pieces of user behavior data are taken as an example, and the number of user behavior data in each service scenario is usually large in actual situations. For example, the user behavior data may be all the user behavior data in a certain time period, and the time period may be set arbitrarily according to the needs. Of course, the user behavior data in each service scene may also be acquired in real time, and the user behavior data acquired each time is expanded into the behavior data set.

Step 102: and determining configuration information corresponding to each service scene in a preset configuration file.

It should be noted that the configuration file is a file generated by pre-configuration, where the configuration information includes an identifier of user behavior data to be monitored in a service scenario and a statistical manner of the user behavior data to be monitored. It is understood that the user behavior data required to be focused on in each service scenario is different, for example, the user behavior data required to be focused on in the recruitment service scenario is generally the user behavior data characterizing the posting of the recruitment information and the delivery resume. User behavior data which needs to be concerned in a house renting service scene is generally user behavior data representing house source information browsed by a user. And respectively configuring aiming at each service scene based on the difference of the user behavior data which needs to be concerned under each service scene.

Preferably, each service scenario has a unique service identifier, and different service scenarios can be distinguished through the service identifiers. The correspondence between the service scene and the configuration information in the configuration file may be a correspondence between the service identifier and the configuration information of the service scene. Correspondingly, the user behavior data in the service scene in the behavior data set is the user behavior data corresponding to the service identifier of the service scene. Therefore, the user behavior data and the corresponding configuration information under the same service scene can be determined through the service identification.

The identification of the user behavior data may be some field or fields in the user behavior data. The identifier is used for screening data, and therefore, the identifier of the user behavior data can also be understood as a screening condition for screening the user behavior data. The statistical manner of the user behavior data may be any statistical manner, and is used for performing statistics on the user behavior, for example, a plurality of pieces of user behavior data in a certain service scene represent that the user delivers the resume for many times, and the statistical manner is used for counting the total number of times that the user delivers the resume in a certain time period or the average number of times that the user delivers the resume every day.

Step 103: and based on the identification in the configuration information, screening the behavior data set to obtain the user behavior data required to be monitored in each service scene.

It should be noted that, when the user behavior data required to be monitored in each service scenario is screened from the behavior data set, the user behavior data required to be monitored in the service scenario may be screened from the user behavior data in the service scenario based on the identifier in the configuration information corresponding to the service scenario for each service scenario. For example, the plurality of service scenarios includes a first service scenario, a second service scenario, and a third service scenario; and if the identifier in the configuration information corresponding to the first service scene is A, the identifier in the configuration information corresponding to the second service scene is B, and the identifier in the configuration information corresponding to the third service scene is C, screening the user behavior data required to be monitored in the first service scene from the user behavior data in the first service scene in the behavior data set based on the identifier A. And screening the user behavior data under the second service scene in the behavior data set based on the identifier B to obtain the user behavior data required to be monitored under the second service scene. And screening the user behavior data under the third service scene in the behavior data set based on the identifier C to obtain the user behavior data required to be monitored under the third service scene.

In an actual service scenario (in a recruitment service scenario), user behavior data generated during the delivery resume of the user will include a current location of the user (for example, a city where the user is located), and in order to count the number of the delivery resumes in the city M every day, an identifier in configuration information corresponding to the recruitment service scenario may be "city-M", so that all user behavior data representing the delivery resumes of the user in the city M can be screened from the user behavior data in the recruitment service scenario.

It can be understood that the identifier in the configuration information may be understood as a filtering condition for filtering user behavior data that needs to be monitored in a service scenario, and the filtering conditions for filtering user behavior data in different service scenarios are different. The screening logics for screening data from the user behavior data in the service scenario may be the same, and here, the screening logics for screening data from the user behavior data may be created in advance, and the screening logics are the same for different service scenarios.

Step 104: and respectively counting the user behavior data to be monitored in each service scene based on the counting mode in the configuration information, and obtaining and displaying the data counting result in each service scene.

It should be noted that the statistical manner in the configuration information corresponding to each service scenario may be the same or different. And for each service scene, counting the user behavior data to be monitored in the service scene by adopting a statistical mode in the configuration information corresponding to the service scene to obtain a data statistical result in the service scene. For example, the plurality of service scenarios include a first service scenario, a second service scenario, and a third service scenario; and counting the user behavior data to be monitored in the first service scene based on the first statistical mode to obtain a data statistical result in the first scene, wherein the statistical mode in the configuration information corresponding to the first service scene is a first statistical mode, the statistical mode in the configuration information corresponding to the second service scene is a second statistical mode, and the statistical mode in the configuration information corresponding to the third service scene is a third statistical mode. And counting the user behavior data to be monitored in the second service scene based on the second statistical mode to obtain a data statistical result in the second scene. And counting the user behavior data to be monitored in the third service scene based on the third statistical mode to obtain a data statistical result in the third scene.

The displayed data statistics can be understood as a monitoring index. Namely, the monitoring index under each service scene can be monitored by displaying the data statistical result. When displaying the data statistical results, the data statistical results in different service scenes can be displayed in different display areas, and the same display area only displays the data statistical results in the same service scene.

In the embodiment of the invention, behavior data sets corresponding to a plurality of service scenes can be acquired, wherein the behavior data sets comprise user behavior data in each service scene. And acquiring the user behavior data under each service scene from the plurality of service scenes in butt joint, and providing a data basis for screening and counting the subsequent user behavior data. And then determining configuration information corresponding to each service scene in a preset configuration file, wherein the configuration information comprises an identifier of user behavior data to be monitored in the service scene and a statistical mode of the user behavior data to be monitored. That is to say, configuration files are preset, and the configuration files are generated by presetting the relevant characteristics of the user behavior data which need to be monitored in different service scenes for subsequent screening and statistical use. Therefore, when the user behavior data are screened, the user behavior data which need to be monitored in each service scene are screened from the behavior data set based on the identification in the configuration information. When the user behavior data is counted, the user behavior data required to be monitored in each service scene is respectively counted based on the counting mode in the configuration information, and the data counting result in each service scene is obtained and displayed instead of utilizing the embedded point codes corresponding to a plurality of service scenes. According to the embodiment of the invention, the embedded point code does not need to be developed aiming at each service scene, so that the occurrence of errors can be reduced, and the development efficiency is improved.

Optionally, based on the identifier in the configuration information, the user behavior data that needs to be monitored in each service scenario is obtained by screening from the behavior data set, including:

and aiming at each service scene, screening user behavior data with the identifier in the configuration information corresponding to the service scene from the user behavior data in the service scene based on the same screening module to obtain the user behavior data needing to be monitored in each service scene, wherein the screening module is a module which is created in advance and used for screening data.

It should be noted that the screening module is a pre-created functional module with screening logic, and the screening module can screen out data meeting screening conditions from a given data source by using given screening conditions. Here, the given data source is user behavior data in a service scenario, and the screening condition is an identifier in the configuration information.

Preferably, a plurality of threads may be used for processing, each thread is used for screening, for one service scenario, user behavior data having the identifier in the configuration information corresponding to the service scenario from the user behavior data in the service scenario by using the same screening module, so as to improve screening efficiency. Of course, it is also possible to use one thread to perform processing, and use the same thread to sequentially screen, for each service scenario, user behavior data having the identifier in the configuration information corresponding to the service scenario from the user behavior data in the service scenario by using the same screening module. And aiming at each service scene, the user behavior data with the identifier in the configuration information corresponding to the service scene is the user behavior data needing to be monitored in the service scene.

In the embodiment of the invention, the screening module used for screening the user behavior data required to be monitored in each service scene is the same pre-established functional module, so that a plurality of screening modules do not need to be developed aiming at each service scene, the development workload is reduced, and the development efficiency is improved.

Optionally, each piece of user behavior data in each service scenario is transmitted in a JSON format, and the identifier in the configuration information is a key-value pair data structure.

It should be noted that the data transferred in JSON format is essentially key: value (value) constitutes key-value pair data. Here, a key in a key pair may also be understood as a field, and a value is a field value corresponding to the field. For example, the key-value pair city: beijing, can also be expressed as a field: city, field value: beijing.

For each service scene, screening user behavior data with the identifier in the configuration information corresponding to the service scene from the user behavior data in the service scene based on the same screening module to obtain the user behavior data required to be monitored in each service scene, including:

and aiming at each service scene, respectively matching all key value pairs in each piece of user behavior data in the service scene with the key value pairs indicated by the identifiers in the configuration information corresponding to the service scene based on the same screening module.

It should be noted that matching key-value pairs with each other includes: key-to-key and value-to-value matches. For example, two key-value pairs are city: beijing, city: the key-value pairs are matched with each other as between city and between beijing and tianjin.

And determining the user behavior data to which the successfully matched key value pair belongs as the user behavior data to be monitored in the service scene aiming at each service scene.

It should be noted that the key-value pair that matches successfully is a key-value pair whose key and value are the same. For example, two key-value pairs are city: beijing, city: tianjin, two key-value pairs fail to match because the two values beijing are not identical to tianjin. And the user behavior data to which the key value pair successfully matched belongs is the user behavior data to which the key value pair successfully matched with the key value pair indicated by the identifier in the configuration information belongs. For example, the user behavior data in the recruitment service scenario includes three pieces of user behavior data, where the first piece of user behavior data includes a key-value pair city: beijing, the second piece of user behavior data includes key-value pair city: tianjin, the third piece of user behavior data includes key value pair id: 00320, the key value pair indicated by the identifier in the configuration information corresponding to the recruitment service scene is city: beijing. And the screened user behavior data needing to be monitored in the recruitment service scene is the first piece of user behavior data.

In the embodiment of the invention, the user behavior data and the configuration information both adopt key value pair data structures, and the user behavior data required to be monitored is screened from the user behavior data in the business scene through the mutual matching of the key value pairs.

Optionally, the step of separately counting user behavior data to be monitored in each service scenario based on a statistical manner in the configuration information includes:

and selecting a target algorithm corresponding to the statistical mode in the configuration information corresponding to the service scene from the same algorithm set for calculating according to the user behavior data required to be monitored in each service scene.

It should be noted that the set of algorithms includes a plurality of pre-created target algorithms. It can be understood that the target algorithm in the algorithm set is an algorithm shared in the statistical process performed in each service scenario.

Multiple target algorithms may be pre-created based on statistical approaches in different traffic scenarios. For example, 5 statistical methods are used in 10 different service scenarios, and a target algorithm is created in advance for each statistical method to obtain 5 target algorithms.

Preferably, the statistical mode in the configuration information may be an identifier of a target algorithm, a target algorithm having the identifier of the target algorithm indicated by the statistical mode in the configuration information is searched from the algorithm set when the statistical mode is performed on the user behavior data to be monitored in each service scene, and the searched target algorithm is used for performing statistical calculation. For example, the set of algorithms includes a first algorithm having an identification a, a second algorithm having an identification B, a third algorithm having an identification C, and a fourth algorithm having an identification D. And if the statistical mode in the configuration information corresponding to a certain service scene is the identifier C, performing statistical calculation by using a third algorithm with the identifier C when the user behavior data required to be monitored in the service scene is counted. It will be appreciated that the target algorithm may be a summation, averaging, or the like algorithm.

In the embodiment of the invention, the target algorithms used for counting the user behavior data required to be monitored in each service scene are derived from the same algorithm set, and the service scenes using the same statistical mode can share the same target algorithm in the algorithm set, so that a set of statistical algorithms does not need to be developed for each service scene, the development workload is reduced, and the development efficiency is improved.

Optionally, before acquiring the behavior data sets corresponding to the plurality of service scenarios, the method further includes:

and receiving the identification and the statistical mode input by the user aiming at each service scene.

It should be noted that the user may enter identification of user behavior data and statistical approaches based on the needs. For example, the number of average daily delivery resumes in the city M needs to be counted, the user inputs the city-M aiming at the recruitment service scene, and inputs the statistical mode of averaging, so that the total number of the delivery resumes can be determined based on fixed time after user behavior data representing the delivery resumes in the city M are screened out, and the number of the daily delivery resumes is calculated by using the statistical method of averaging.

And generating a configuration file based on the identification and the statistical mode.

It should be noted that, a configuration file template may be created in advance, an input box for user input is set in the configuration file template, and after the user inputs the identifier and the statistical mode in the input box, the configuration file is generated.

In the embodiment of the invention, a user can input the identifier and the statistical mode corresponding to each service scene according to the requirements of each service scene, thereby generating the configuration file comprising the configuration information corresponding to each service scene.

Optionally, after obtaining and displaying the data statistics in each service scenario, the method further includes:

and acquiring the early warning value corresponding to each data statistical result.

It should be noted that the early warning value is a pre-stored value, and a user can set the value of the early warning value according to the requirement.

And displaying early warning prompt information under the condition that at least one item of data statistical result exceeds the corresponding early warning value.

It should be noted that the warning prompt message may be any message with a warning function, for example, it may be a pop-up box, or a data statistic result exceeding the warning value may be marked with a striking color to be distinguished from a data statistic result not exceeding the warning value. The striking color may be red, yellow, etc.

In the embodiment of the invention, the early warning prompt information can be sent out under the condition that the data statistical result exceeds the early warning value, so that the user can find the early warning information in time.

Fig. 2 is a flowchart of an actual application architecture of the processing method of user behavior data provided by the present invention. The multiple service scenarios in fig. 2 include a recruitment service scenario, a chat service scenario, and a house renting service scenario, but fig. 2 is only an example, and the present invention is not limited to the three service scenarios shown in fig. 2.

The risk detection is used to detect service data in each service scenario, and pull configuration information configured by the user in advance for each service scenario from the service configuration management system, that is, the configuration file in the embodiment of the present invention. Firstly, reading service data, wherein the service data is user behavior data in each service scene. And then, screening the user behavior data required to be monitored in each service scene through field matching, wherein the process is similar to the process of screening the user behavior data required to be monitored in the service scene through key value pair matching in the embodiment of the invention, and is not repeated here. And then, carrying out buried point matching, namely carrying out statistics on the screened user behavior data by using a statistical mode in the configuration file. By collecting data and uploading the data to the monitoring service, the data statistical results under each service scene can be transmitted to the monitoring service, and finally the data statistical results are displayed by using the monitoring large disc. The monitoring large disc is an electronic device with a display function, and monitoring personnel can know the user behavior condition in each service scene through monitoring data displayed on the large disc.

It should be noted that data can be transmitted in the form of K-V (key value pair) using the JSON data format of the general string type. And defining the data type, the monitoring type, the statistical mode and the like of the field through a field configuration table (configuration file), and carrying out pre-configuration on the system when the data is initialized based on key monitoring indexes of the wind control core business process. When the system runs, the data and the configuration are correlated, real-time user behavior data are collected, and relevant statistical calculation is carried out. The embodiment of the invention can complete the monitoring of the embedded points under different service scenes based on a set of mechanism.

The processing method of the user behavior data provided by the embodiment of the present invention is described above, and a processing apparatus of the user behavior data provided by the embodiment of the present invention is described below with reference to the accompanying drawings.

Referring to fig. 3, an embodiment of the present invention further provides a device for processing user behavior data, where the device includes:

the data module 31 is configured to obtain behavior data sets corresponding to a plurality of service scenarios, where a behavior data set includes user behavior data in each service scenario;

the configuration module 32 is configured to determine configuration information corresponding to each service scenario in a preset configuration file, where the configuration information includes an identifier of user behavior data to be monitored in the service scenario and a statistical manner of the user behavior data to be monitored;

the first processing module 33 is configured to filter user behavior data to be monitored in each service scenario from the behavior data set based on the identifier in the configuration information;

and the second processing module 34 is configured to separately count the user behavior data to be monitored in each service scenario based on the statistical manner in the configuration information, and obtain and display a data statistical result in each service scenario.

Optionally, the first processing module 33 is specifically configured to, for each service scenario, screen, based on the same screening module, user behavior data having an identifier in configuration information corresponding to the service scenario from the user behavior data in the service scenario to obtain user behavior data to be monitored in each service scenario, where the screening module is a module created in advance and used for screening data.

Optionally, each piece of user behavior data in each service scene is transmitted in a JSON format, and the identifier in the configuration information is a key value pair data structure;

the first processing module 33 is specifically configured to match, based on the same screening module, all key-value pairs in each piece of user behavior data in a service scene with key-value pairs indicated by the identifier in the configuration information corresponding to the service scene, respectively, for each service scene; and determining the user behavior data to which the successfully matched key value pair belongs as the user behavior data to be monitored in the service scene aiming at each service scene, wherein the successfully matched key value pair is the key value pair with the same key and value.

Optionally, the second processing module 34 is specifically configured to select, for user behavior data to be monitored in each service scenario, a target algorithm corresponding to a statistical manner in configuration information corresponding to the service scenario from the same algorithm set for calculation.

Optionally, the apparatus further comprises:

the receiving module is used for receiving the identification and the statistical mode input by the user aiming at each service scene;

and the generating module is used for generating the configuration file based on the identification and the statistical mode.

Optionally, the apparatus further comprises:

the first early warning module is used for acquiring an early warning value corresponding to each data statistical result;

and the second early warning module is used for displaying early warning prompt information under the condition that at least one item of data statistical result exceeds the corresponding early warning value.

The processing device for user behavior data provided in the embodiment of the present invention can implement each process implemented by the processing method for user behavior data in the method embodiments of fig. 1 to fig. 2, and is not described here again to avoid repetition.

In the embodiment of the invention, behavior data sets corresponding to a plurality of service scenes can be acquired, wherein the behavior data sets comprise user behavior data in each service scene. And acquiring the user behavior data under each service scene from the plurality of service scenes in butt joint, and providing a data basis for screening and counting the subsequent user behavior data. And then determining configuration information corresponding to each service scene in a preset configuration file, wherein the configuration information comprises an identifier of user behavior data to be monitored in the service scene and a statistical mode of the user behavior data to be monitored. That is to say, configuration files are preset, and the configuration files are generated by presetting the relevant characteristics of the user behavior data which need to be monitored in different service scenes for subsequent screening and statistical use. Therefore, when the user behavior data are screened, the user behavior data which need to be monitored in each service scene are screened from the behavior data set based on the identification in the configuration information. When the user behavior data is counted, the user behavior data required to be monitored in each service scene is respectively counted based on the counting mode in the configuration information, and the data counting result in each service scene is obtained and displayed instead of utilizing the embedded point codes corresponding to a plurality of service scenes. According to the embodiment of the invention, the embedded point code does not need to be developed aiming at each service scene, so that the occurrence of errors can be reduced, and the development efficiency is improved.

On the other hand, the embodiment of the invention also provides electronic equipment, which comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus;

a memory for storing a computer program;

and the processor is used for realizing the steps of the processing method of the user behavior data when executing the program stored in the memory.

For example, fig. 4 shows a schematic physical structure diagram of an electronic device.

As shown in fig. 4, the electronic device may include: a processor (processor)410, a communication Interface 420, a memory (memory)430 and a communication bus 440, wherein the processor 410, the communication Interface 420 and the memory 430 are communicated with each other via the communication bus 440. The processor 410 may call logic instructions in the memory 430 to perform the following method:

acquiring behavior data sets corresponding to a plurality of service scenes, wherein the behavior data sets comprise user behavior data in each service scene;

determining configuration information corresponding to each service scene in a preset configuration file, wherein the configuration information comprises an identifier of user behavior data to be monitored in the service scene and a statistical mode of the user behavior data to be monitored;

based on the identification in the configuration information, user behavior data required to be monitored in each service scene is obtained by screening from the behavior data set;

and respectively counting the user behavior data to be monitored in each service scene based on the counting mode in the configuration information, and obtaining and displaying the data counting result in each service scene.

In addition, the logic instructions in the memory 430 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of 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 invention. 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 other various media capable of storing program codes.

In still another aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is implemented to, when executed by a processor, perform the processing method for user behavior data provided in the foregoing embodiments, for example, the method includes:

acquiring behavior data sets corresponding to a plurality of service scenes, wherein the behavior data sets comprise user behavior data in each service scene;

determining configuration information corresponding to each service scene in a preset configuration file, wherein the configuration information comprises an identifier of user behavior data to be monitored in the service scene and a statistical mode of the user behavior data to be monitored;

based on the identification in the configuration information, user behavior data required to be monitored in each service scene is obtained by screening from the behavior data set;

and respectively counting the user behavior data to be monitored in each service scene based on the counting mode in the configuration information, and obtaining and displaying the data counting result in each service scene.

The above-described embodiments of the apparatus are merely illustrative, and 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 modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.

Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.

Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; 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; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.