CN116431366B

CN116431366B - Behavior path analysis method, system, storage terminal, server terminal and client terminal

Info

Publication number: CN116431366B
Application number: CN202310667779.1A
Authority: CN
Inventors: 陈世元; 何双全
Original assignee: Beijing Jidu Technology Co Ltd
Current assignee: Beijing Jidu Technology Co Ltd
Priority date: 2023-06-07
Filing date: 2023-06-07
Publication date: 2023-10-31
Anticipated expiration: 2043-06-07
Also published as: CN116431366A

Abstract

The embodiment of the application provides a behavior path analysis method, a behavior path analysis system, a storage terminal, a server and a client. The method for the storage end comprises the following steps that event data generated by different user behaviors are stored in the storage end, and the method suitable for the storage end comprises the following steps: receiving a program statement sent by a server; the program statement comprises a first statement and a second statement; the first statement is used for inquiring the event, and the second statement is used for processing the inquired event; inquiring a plurality of events from the event data according to the first statement; executing the second sentence to process the plurality of events to obtain an event tree; and sending the event tree to a server, and generating a behavior path analysis chart by the server according to the event tree. By adopting the technical scheme provided by the embodiment of the application, the flow of behavior path analysis can be simplified, and the rapid analysis of the behavior path can be realized.

Description

Behavior path analysis method, system, storage terminal, server terminal and client terminal

Technical Field

The present application relates to the field of computer technologies, and in particular, to a behavior path analysis method, a system, a storage end, a server and a client.

Background

Behavioral path analysis is an analysis scheme that monitors the flow direction of users, thereby counting the depth of use of the product. The scheme is mainly characterized in that according to a behavior event log generated by executing actions in products (such as applications or websites) by each user, the circulation rule and characteristics of the user are analyzed so as to mine modes of access, clicking and the like of each user, and specific business uses are further realized.

At present, the existing scheme has the problems of complex analysis flow, low analysis implementation speed and the like when the analysis and calculation of the behavior path are implemented, and in addition, the user path is mostly divided by the session identifier (such as the session ID) in the analysis process to provide support for the subsequent analysis, so that the session ID must be stored in the log, and once the session ID is lacking in the log, the analysis of the behavior path of the user cannot be implemented, so that the application range has great limitation.

Disclosure of Invention

In view of the above, the present application provides a behavior path analysis method, system, storage, server and client for solving the above problems or at least partially solving the above problems.

In a first embodiment of the present application, a behavioral path analysis method is provided. The method is suitable for a storage end, and event data generated by different user behaviors are stored in the storage end. The method comprises the following steps:

Receiving a program statement sent by a server; the program statement comprises a first statement and a second statement; the first statement is used for inquiring the event, and the second statement is used for processing the inquired event;

inquiring a plurality of events from the event data according to the first statement;

executing the second sentence to process the plurality of events to obtain an event tree;

and sending the event tree to a server, and generating a behavior path analysis chart by the server according to the event tree.

In a second embodiment of the present application, a behavior path analysis method is also provided. The method is suitable for the server side. The method comprises the following steps:

receiving an analysis request sent by a client, wherein the analysis request comprises a plurality of request parameters;

filling the plurality of request parameters into the adaptive positions in the program statement template to generate a program statement;

the program statement is sent to a storage end, and the storage end inquires a plurality of events from event data generated by different stored user behaviors by executing the program statement, and processes the events to obtain an event tree;

the program statement comprises a first statement and a second statement; the first statement is used for inquiring the event, and the second statement is used for processing the inquired event.

In a third embodiment of the present application, a behavior path analysis method is also provided. The method is suitable for a storage end, wherein event data of different user behaviors are stored in the storage end; the method comprises the following steps:

performing first grouping processing on a plurality of events inquired from the event data to obtain a plurality of first event groups; wherein each event in each first event group has the same user identification and is ordered according to the time stamp, and each event is respectively associated with the next adjacent event in the group;

dividing each first event group according to the time interval to determine the corresponding level in the event tree for the plurality of events according to the dividing result;

generating an event tree according to the levels corresponding to the events;

In a fourth embodiment of the present application, a behavior path analysis method is also provided. The method is suitable for the client; the method comprises the following steps:

displaying an interactive interface;

responding to the query condition and the time interval input through the interactive interface, generating a program statement through a server according to the query condition and the time interval, and sending the program statement to a storage end;

Receiving a graph data structure sent by the server;

generating and displaying a behavior path analysis chart on the interactive interface according to the chart data structure;

the program statement is generated by filling the query condition and the time interval into an adaptive position in a program statement template by a server; the program statement comprises a first statement used for inquiring the event and a second statement used for processing the inquired event; the graph data structure is generated by the server according to an event tree returned by the storage end through executing the program statement; and the storage end stores event data generated by different user behaviors.

In a fifth embodiment of the present application, a behavioral path analysis system is provided. The system comprises: a server side and a storage side, wherein,

the server side is used for sending program sentences to the storage side, wherein the program sentences comprise a first sentence and a second sentence; the first statement is used for inquiring the event, and the second statement is used for processing the inquired event;

the storage end is used for storing event data of different user behaviors and inquiring a plurality of events from the event data according to the first statement; executing the second sentence to process the plurality of events to obtain an event tree; and sending the event tree to a server, and generating a behavior path analysis chart by the server according to the event tree.

In a sixth embodiment of the present application, a storage terminal is also provided. The storage terminal comprises: the system comprises a memory and a processor, wherein the memory is used for storing event data generated by different user behaviors and a computer program; the processor is coupled to the memory, and is configured to execute the computer program stored in the memory, so as to implement the steps in the behavior path analysis method provided in the first embodiment or the third embodiment.

In a seventh embodiment of the present application, a server is also provided. The storage terminal comprises: a memory and a processor, wherein the memory is used for storing a computer program; the processor is coupled to the memory, and is configured to execute the computer program stored in the memory, so as to implement the steps in the behavior path analysis method provided in the second embodiment.

In an eighth embodiment of the present application, a client is also provided. The client comprises: the system comprises a display, a memory and a processor, wherein the display is used for displaying an interactive interface and a behavior path analysis chart on the interactive interface; the memory is used for storing a computer program; the processor is coupled to the memory, and is configured to execute the computer program stored in the memory, so as to implement the steps in the behavior path analysis method provided in the fourth embodiment.

In the technical scheme provided by the embodiment of the application, after the server receives the analysis request sent by the client, the server can generate a program statement by filling a plurality of request parameters carried in the analysis request into the adaptive positions in the program statement template, and sends the program statement to the storage end. The program statement comprises a first statement and a second statement; the first statement is used for inquiring the event, and the second statement is used for processing the inquired event. After receiving the program statement sent by the server, the storage end can inquire a plurality of events from event data generated by different user behaviors stored by the storage end according to the first statement; and then, through executing a second sentence, processing the plurality of events to obtain an event tree, sending the event tree to a server, and generating a corresponding behavior path analysis graph by the server according to the event tree so as to enable an analyst to perform user behavior path analysis according to the behavior path analysis graph. The program statement sent by the server to the storage terminal in the scheme has the functions of inquiring the event and processing the inquired event, so that the storage terminal can directly process the inquired event without returning the inquired event to the server for processing by the server, and finally, only the corresponding processing result (event tree) is returned to the server for processing by the server, thereby effectively reducing the data volume of communication transmission between the storage terminal and the server, being beneficial to quickly returning a behavior path analysis chart to an analyst so as to accelerate the realization of user behavior path analysis, and having more remarkable beneficial effects especially under the conditions of larger inquired event data volume or large concurrent analysis request and the like. In summary, the scheme of the embodiment rapidly realizes the analysis of the user behavior path in a query mode, and can simplify the flow of the analysis of the user behavior path.

In another technical scheme provided by the embodiment of the application, after a storage end queries a plurality of events in event data stored by the storage end, a plurality of first event groups can be obtained by carrying out first grouping processing on the plurality of events, each event in each first event group has the same user identification and is ordered according to a timestamp, and each event is respectively associated with the next adjacent event in the group; further, each first event group may be further segmented according to a time interval, so as to determine levels corresponding to each of the event trees for the plurality of events according to a segmentation result, generate event trees according to the levels corresponding to the plurality of events, send the event trees to a server, and generate a behavior path analysis graph according to the event trees by the server. According to the embodiment, the first event group (or the called user path) corresponding to each user is segmented by using the time interval, so that the user path is supported to be segmented by any time interval, the problem that the application range is limited due to the fact that the user identifier is used for segmenting the user path in the existing scheme can be effectively solved, and the application range is wide.

In still another technical scheme provided by the embodiment of the application, the client is provided with a display interactive interface, an analyst can input corresponding query conditions and time intervals through the interactive interface, the client responds to the input operation and generates a program statement through the server according to the query conditions and the time intervals, and the program statement is sent to the storage end. The program statement is generated by filling the query condition and the time interval into an adaptive position in a program statement template by a server; the program statement comprises a first statement for inquiring the event and a second statement for processing the inquired event; the graph data structure is generated by the server according to an event tree returned by the storage end through executing the program statement; and the storage end stores event data generated by different user behaviors. The method and the device provide the function of randomly designating the time interval for the analyst, so that when the subsequent storage end executes the program statement to realize the behavior path analysis, the subsequent storage end can perform corresponding segmentation processing based on the time interval, the problem that the application range is limited due to the fact that the conventional scheme uses the user identifier to perform the corresponding segmentation processing can be effectively solved, and the application range is wide.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions of the prior art, the drawings that are needed to be utilized in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application and that other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a behavior path analysis method according to an embodiment of the present application;

FIG. 2 is a schematic diagram of collecting event data generated by user behavior according to an embodiment of the present application;

FIG. 3 is a flowchart illustrating another behavior path analysis method according to an embodiment of the present application;

FIG. 4a is a schematic diagram of an analysis interactive interface provided by a client according to an embodiment of the present application;

FIG. 4b is a schematic diagram of processing multiple queried events according to an embodiment of the present application;

FIG. 4c is a schematic diagram of a behavior path analysis system corresponding to FIG. 4b according to an embodiment of the present application;

FIG. 5a is a schematic diagram of an event tree according to an embodiment of the present application;

FIG. 5b is a schematic diagram of a data structure of the present application;

Fig. 6 and fig. 7 are schematic flow diagrams of two further behavioral path analysis methods according to embodiments of the present application;

fig. 8 to 11 are schematic structural diagrams of a behavior path analysis device according to an embodiment of the present application;

FIG. 12 is a schematic diagram of a storage end structure according to an embodiment of the present application;

fig. 13 is a schematic structural diagram of a computer program product according to an embodiment of the present application.

Detailed Description

The existing scheme is mostly realized based on a computing framework such as a Flink and Spark when the behavior path analysis computation is realized for a user. Based on the computing frameworks such as the Flink, the Spark and the like, corresponding developers are often required to write some function program codes required for computing analysis, so that the complexity of an analysis flow is increased, the time for computing analysis realization is prolonged, and the development cost is high. In addition, the existing scheme basically uses session identification (such as session ID) to divide the user path in the analysis process, so as to provide support for the subsequent implementation of behavioral path analysis. For example, two path segments are obtained by segmentation: path segment 1: a-B-C, path segment 2: D-E-F, the two path segments have different session IDs. Where a session is a series of requests and responses that occur continuously between a client (browser, etc.) and a server, such as: the whole shopping process of a user on a website is a session, and the session ID of the session can be generated when the user is monitored to enter the website. In the above-mentioned existing analysis scheme, the user path is divided based on the session identifier, a session ID field is required to be in the log for recording the user behavior event, so as to record the corresponding session ID through the session ID field, once the session ID is not recorded for the user behavior event in the log, the whole analysis scheme cannot utilize the user behavior event data recorded in the log to realize the analysis, which obviously makes the application scope of the whole scheme have a larger limitation.

In order to solve the technical problems, the novel behavior path analysis technical scheme provided by the embodiments of the application can simplify the implementation flow of behavior path analysis and improve the analysis performance; in addition, the user path is divided by designating any time interval, so that the problem that the application range of the scheme is smaller due to the fact that the user path is divided by the session identification of the user can be solved.

The user path refers to time series data (i.e., time series event data) generated by a series of behavior events performed by a user on a product (such as an Application (APP) or an applet (an application that can be used without downloading and installing), and the time series data can be collected and stored by embedding points.

The time interval (or referred to as session interval time, etc.) refers to a time threshold set for dividing (splitting) a user path, where a time difference between two adjacent behavior events in the user path is greater than or equal to a specified time interval, and the user path may split between the two adjacent behavior events, for example, if the behavior events included in the user path are in turn: a1- > a2- > a3- > a4- > a5, wherein the time difference between a2 and a3 is greater than the time interval, the user path is divided into two path segments: a1 > a2, a3 > a4 > a5.

It should be noted that, for convenience of description, in the technical solution provided in the present application, the behavior events are expressed as "events", and the two are essentially the same, but different expressions are adopted in different description scenarios.

In order to enable those skilled in the art to better understand the present application, the following description will make clear and complete descriptions of the technical solutions according to the embodiments of the present application with reference to the accompanying drawings.

In some of the flows described in the description of the application, the claims, and the figures described above, a number of operations occurring in a particular order are included, and the operations may be performed out of order or concurrently with respect to the order in which they occur. The sequence numbers of operations such as 101, 102, etc. are merely used to distinguish between the various operations, and the sequence numbers themselves do not represent any order of execution. In addition, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first" and "second" herein are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, and are not limited to the "first" and the "second" being different types. The term "or/and" in the present application is merely an association relationship describing the association object, which means that three relationships may exist, for example: a and/or B are three cases that A can exist alone, A and B exist together and B exists alone; the character "/" in the present application generally indicates that the front and rear associated objects are an "or" relationship. It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a product or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such product or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a commodity or system comprising such elements. Furthermore, the embodiments described below are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

The following details the technical solutions provided by the embodiments of the present application.

Fig. 1 is a flow chart illustrating a behavior path analysis method according to an embodiment of the present application, where the method is applicable to a storage terminal, and event data generated by different user behaviors are stored in the storage terminal. The storage end may be, but not limited to, a database, for example, a Doris database (which is an open-source high-performance, simple and easy-to-use MPP architecture analysis type database supporting real-time; briefly, an interactive SQL data warehouse based on the MPP architecture), but may also be other types of databases, and the present application is preferably, for example, a Doris database, and the sources of the preferred Doris database will be described in detail below. In addition, the storage end can adopt a corresponding data collection method to collect and store event data, such as buried points. The purpose of the present application is to meet the requirements of fast, efficient and rich data applications, such as user behavior process and result record, and the data collected by the embedded point can be used for analyzing the use condition of websites or APP, user behavior habit, etc., so as to establish the basis of data products such as user behavior path.

For example, referring to fig. 2, after event data generated by clicking, browsing, etc. performed by a user1 on an APP or a applet is collected by a buried point manner, the collected event data may be reported, specifically, the event data may be reported to a corresponding server, and the server may preprocess the event data and store the event data in a corresponding data table (such as a Doris table) in a storage end. The preprocessing operation performed by the server on the event data may include, but is not limited to, data format conversion, so as to convert the data format of the event data into a data format that can be identified and stored by the storage end; the manner in which the preprocessing operation is performed may be, but is not limited to, by using an open source processing platform such as Kafka, flink, etc.

For example, a data format of event data collected by a buried point is generally a json format, a name format of the event data in the json format is a name, and a name format required by a storage end is an event_name, so that in order to ensure that the storage end can identify and store the event data, a server end needs to convert the name format of the received event data into the required event_name format, and then transmit the converted event data to the storage end for recording and storing. When the event data is stored in the storage end in the form of a table, the stored event field values may include, but are not limited to: user identification (e.g., user_id), event name (event_name), timestamp of event (event_time), etc.

Referring back to fig. 1, the behavior path analysis method provided by the embodiment of the present application includes the following steps:

101. receiving a program statement sent by a server; the program statement comprises a first statement and a second statement; the first statement is used for inquiring the event, and the second statement is used for processing the inquired event;

102. inquiring a plurality of events from the event data according to the first statement;

103. executing the second sentence to process the plurality of events to obtain an event tree;

104. and sending the event tree to a server, and generating a behavior path analysis chart by the server according to the event tree.

In the above 101, the server may be a server, a virtual server, a cloud end, or the like, which is communicatively connected to the storage end. The program statement may be generated by the server by means of a program statement template preset in itself. Specifically, as referring to fig. 3, the process of generating a program statement by the server and sending the program statement to the storage may include the following steps:

s201, receiving an analysis request sent by a client, wherein the analysis request carries a plurality of request parameters;

s202, filling the plurality of request parameters into the adaptive positions in the program statement template to generate a program statement;

S203, the program statement is sent to a storage end, and the storage end inquires a plurality of events from event data generated by different stored user behaviors through executing the program statement, and processes the events to obtain an event tree. That is, the steps 102 to 103 are simply executed by the storage terminal based on the received program statement;

in S201, the analysis request may be triggered by the corresponding analyst through the interactive interface provided by the client. The client may be a smart phone, tablet computer, wearable device, etc. As shown in fig. 4a and fig. 4b in combination, a plurality of input boxes for inputting parameters by a user can be displayed on an interactive interface (analysis interactive interface) provided by the client, and an analyst can input corresponding parameters in the input boxes by means of manual or voice and the like and input a click completion "confirm" control; the client responds to the clicking operation of the analyst, and can acquire an analysis form, wherein the analysis form comprises a plurality of input boxes for inputting corresponding parameters by the analyst, so that the parameters in the analysis form are determined as request parameters, and an analysis request is generated according to the request parameters.

The analyst may be, but is not limited to, a marketer, a data analyst, etc. The plurality of request parameters included in the analysis request may be, but are not limited to, at least one of the following: inquiring conditions and time intervals; wherein the query conditions may include, but are not limited to, at least one of: a start event (e.g., an entry event generated by entering a shopping page), an event date, a type of product used by the user (e.g., a client device, APP application, or website, etc.), etc.

In the steps S202 to S203, the server may analyze the received analysis request to obtain a plurality of request parameters; the plurality of request parameters are then sent to a program statement generator (e.g., sql generator) that is built into itself. The program statement generator is internally provided with a preset program statement template (such as an sql statement template), after receiving a plurality of request parameters, the program statement template can be called, and corresponding placeholders in the program statement template are replaced by using the plurality of request parameters, so that the plurality of request parameters are filled in the adaptive positions in the program statement template, a program statement is finally generated, and the program statement is transmitted to the storage end.

The program statement template is the core of a program statement generator, and two types of statements are arranged in the template: one class is a first statement that functions as a query event and the other class is a second statement that functions to process the queried event. The number of the first sentences and the second sentences is at least one, and the at least one first sentence and the at least one second sentence are combined into the program sentence template through a corresponding nesting mode (such as an sql nesting mode) according to a certain logic sequence. In implementation, the at least one first statement may include a statement configured to facilitate a function having a query function, such as a select function, and the at least one second statement may include: statements constructed using window functions such as doris partition by functions, LEAD functions, sum functions, row_number functions, and the like. The respective roles of the at least one second sentence will be described in more detail below. When the program statement template is filled with the plurality of request parameters, the corresponding placeholders in the first statement contained in the program statement template can be replaced by using the query conditions in the plurality of request parameters, and the corresponding placeholders in the second statement contained in the program statement template can be replaced by using the time intervals in the plurality of request parameters. The time interval is used as a subsequent corresponding slicing process.

It should be noted that, if the analyst does not specify the time interval through the interactive interface, the time interval is a default value (e.g., 5 hours, 2 days, 1 month, etc.).

If the storage end is a doris database, various program statement codes required to be written in the development stage for realizing the behavior path analysis function are the program statement templates, most of the program statement templates based on doris can be directly realized by using some built-in functions of doris, a developer does not need to write codes to realize some functions, the implementation flow of the behavior path analysis function can be greatly simplified, the quality of the function codes can be ensured, and the performance of the behavior path analysis function can be improved. For example, when the second statement in the program statement template is written, the implementation can be directly realized by using a doris window function. This is why the preferred storage side of the present application is the doris database. Based on this, since the doris database is an interactive SQL data warehouse based on the MPP architecture, in order to enable the generated program statements to be recognized and executed by the doris database, the program statement template described above is preferably an SQL statement template, and the program statements generated by the program statement template are SQL program statements (i.e., structured query language statements) accordingly.

It should be noted here that the doris window function (also called an analysis function) is a doris built-in function, which is similar to an aggregation function, and calculates a data value for a plurality of input rows, except that the window function calculates a separate value for each row of the result set in a specific window. The result set refers to a queried event set, and includes a plurality of events. In addition, as referring to fig. 4b, the server may also perform a request preprocessing for the analysis request before invoking the program statement generator for generating a program statement for the analysis request, the preprocessing operations including but not limited to: authentication and request parameter verification. The authentication is used for verifying whether an analyst has the authority to access the storage end, such as: the analysis request can also carry the identification (such as an account number and the like) of the analysis personnel, and the identification of the analysis personnel can be compared with the stored identification of the analysis personnel with authority, so that the authentication can be realized according to the comparison result. The request parameter check is used to check whether the request parameter has a problem, such as: whether an abnormality occurs in units of time intervals, whether the time span is within a required range (e.g., the time span may be 1 week, 1 month, etc., but not too long as 1 year); and the following steps: whether the number of events that need to be queried exceeds the required range, and so on. Through the request preprocessing, the execution of the step S202 is triggered only when authentication is successful, the request parameters are not problematic, and the like.

Returning to look-ahead 102, with the above description, the plurality of queried events are events that satisfy the query condition requirements contained in the first statement. Namely, the first sentence contains a query condition; and correspondingly, 102 "query a plurality of events from the event data according to the first sentence" may specifically include:

1021. executing the first statement, and querying a plurality of events meeting the query condition from the event data.

Details of the query conditions may be found in the above related content, and will not be described here.

In 103, by executing the second sentence, the following series of processing can be sequentially performed on the plurality of events: packet processing, splitting event groups obtained by the packet processing according to specified time intervals, determining the corresponding level of each event, and the like, so as to generate event trees corresponding to the events. Thus, the second sentence includes a time interval; accordingly, in an implementation technical solution, 103 "execute the second sentence to process the plurality of events to obtain an event tree" may specifically include:

1031. performing first grouping processing on the events based on attribute information of the events to obtain a plurality of first event groups; each event contained in each first event group has the same user identification and is ordered according to a time stamp, and each event in each first event group is correspondingly associated with the next adjacent event in the group;

1032. Dividing each first event group according to the time interval, and respectively adding a first number for at least one group segment corresponding to each divided first event group in an incremental way to obtain a first number of each group segment to which each of the plurality of events belongs;

1033. performing a second grouping process on the events according to the first numbers of the group segments to which the events belong and the attribute information corresponding to the first numbers, so as to obtain a plurality of second event groups; the first numbers of the group sections of each event in each second event group are the same, but different user identifications are provided;

1034. determining the corresponding level of each event in each second event group in the event tree; wherein the levels corresponding to the events in each second event group are the same;

1035. and generating the event tree according to the level corresponding to each event in each second event group.

In 1031, the events with the same user identifier may be divided into a group according to the user identifiers and time stamps of the plurality of events in sequence, and then the group is ordered; after the ordering is completed, the next event adjacent to each event in the group is determined. That is, in a specific implementation manner, the step 1031 "performs the first grouping processing on the events based on the attribute information of the events to obtain a plurality of first event groups" may be implemented by:

10311. Dividing the events with the same user identification into a group according to the user identifications of the events to obtain a plurality of groups;

10312. sorting the events contained in each group in a group according to the time stamps of the events;

10313. after the sorting is finished, respectively determining adjacent next events for all events in the group in each group, and establishing corresponding association, so as to obtain the plurality of groups after the establishment to determine the plurality of first event groups.

To facilitate an understanding of step 1031, an example is given below.

Assume that a number of events are queried as shown in Table 1 below:

TABLE 1

Processing the plurality of event groupings shown in table 1 to obtain a plurality of first event groups may specifically include the following processing steps:

step 11, firstly, dividing the events (such as event1, start_event0 and event 3) with the same user identification into a group according to the user_id field values (user identifications) of a plurality of events by executing a second sentence constructed by doris partition by function to obtain a plurality of groups;

step 12, performing intra-group ordering on the events in each group according to the event_time field values (time stamps) of the events, for example: the earlier the time stamp of an event, the earlier the ordering within the corresponding group;

Step 13, newly adding a next event field (next_event_name) to each event by executing a second sentence constructed by utilizing the LEAD function; and querying the next event adjacent to each event in each group as the field value of the next_event_name field correspondingly added, thereby establishing the corresponding association between each event in each group and the next event adjacent to each event in each group. The plurality of packets after the establishment are determined as a plurality of first event groups. It should be noted that, if an event has no next event in the corresponding group, the next event of the event may be set to a default value, such as null. The corresponding association is established for each event in each group and the next event adjacent to each event in each group, so that the events in each group are linked together, and the corresponding event and the next event can be known in the same row, thereby facilitating the subsequent analysis (the segmentation analysis of the first event group is described below).

The first event groups obtained in steps 11 to 13 can be seen in the following table 2:

TABLE 2

Note that: each of the first event groups shown in table 2 may also be understood as a "user path" for a user as described above, such as: the first event group 11 may be the user path of the user 1.

In 1032, the second sentence constructed by using the sum function may be executed, where each first event group is segmented into at least one group segment according to the time interval, and the first number is added to each group segment obtained by segmentation in a self-increasing manner after or during the segmentation. Specifically, the slicing may be implemented for each first event group according to the target adjacent two events in which the difference of the time stamps in each first event group is greater than or equal to the time interval. Based on this, in a specific implementation, the above 1032 "splitting each event group according to the time interval" may be implemented by the following steps:

10321. determining target adjacent two events in which the difference value of the time stamps in each first event group is greater than or equal to the time interval;

10322. determining the segmentation position of each first event group according to the two adjacent events of the target;

10323. and cutting each first event group according to the cutting position to obtain at least one group segment corresponding to each first event group.

The difference is an absolute difference.

For example, taking the first event group 11 shown in table 2 as an example, if the difference between the time stamp of event start _ event0 and the time stamp of its next event1, and the difference between the time stamp of event1 and the time stamp of its next event3, all are larger than the time interval, if the event3 has no next event in the group, the first event group 11 needs to be split once at the event start_event0 and the event1, so as to obtain the following three corresponding group segments: the group segment including the event start_event0, the group segment including the event_event1, and the group segment including the event3 may be sequentially numbered as follows: 1. 2, 3. The numbering may be, but is not limited to, by executing a second statement that facilitates the construction of the row number function. The row_number function is a function that is numbered consecutively for the corresponding object starting from 1. In the two adjacent numbered group segments, each event in the first numbered group segment is correspondingly associated with a corresponding event in the second numbered group segment. For example, taking the above example and combining table 2, taking the above example of the group segment 1 containing the event start_event0 and the group segment 2 containing the event_event1 in the three group segments obtained by dividing the first event group 11, the group segments 1 and the group segment 2 are numbered adjacent, and the event1 in the group segment 2 numbered later has a correspondence relationship with the event start_event0 in the group segment 1 numbered earlier.

By analogy, other first event groups shown in table 2, such as the first event group 12, the first event group 13, the first event group 14, the first event group 15, and the like, respectively corresponding to at least one group segment can also be obtained, and the addition of the number is completed. It should be noted that, when numbering at least one group segment corresponding to each first event group, the start number is fixed, and as described above, the start numbers are all 1 by using the row_number function as an example.

Assuming that the time stamp of each event in each first event group shown in table 2 and the time stamp of the next event associated with each event are all greater than the time interval, the number of at least one group segment and group segment corresponding to each first event group can be seen as shown in the following table 3:

TABLE 3 Table 3

As can be readily understood from the above table 3, among the plurality of events such as the start_event0, event1, event2, event3, event4, and event5, the event start_event0 identified as user1 is taken as an example, and the first number of the group segment to which the event start_event0 identified as user1 belongs is 1.

In 1033, the second grouping process of the plurality of events can be implemented by executing another second sentence constructed by using the doris partition by function and dividing the plurality of events into a group according to the first numbers of the group segments and the corresponding user identifications, wherein the first numbers of the group segments are the same but the user identifications are different.

For example, with continued reference to table 3, when the above-mentioned plurality of events such as the start_event0, event1, event2, event3, event4, and event5 are subjected to the second grouping process, for example, the first number of the group segment to which each event start_event0 in the second event group 21 belongs is 1 and has a different user identifier, the event start_event0 in the user identifier of user1, the event start_event0 in the user identifier of user2, the event start_event0 in the user identifier of user3, the event start_event0 in the user identifier of user4, and the event start_event0 in the user identifier of user5 are all divided into a second event group 21. By analogy, the resulting plurality of second event groups are ultimately partitioned, as shown in Table 4 below:

TABLE 4 Table 4

In 1034, the plurality of second event groups may be ordered first; after the sorting is finished, the corresponding group numbers are respectively added to the plurality of second events after the sorting is finished in a number self-increasing mode by executing a second sentence constructed by a row_number function, so that the group numbers of each second event group are determined as the corresponding levels of the events in each event group. Specifically, during the sorting, the method can be implemented according to the next event corresponding to each event in each second event group, so that each event in the second event group after the sorting is the next event of the corresponding event in the second event group before the sorting in the plurality of second events after the sorting is completed. For example, the plurality of second event group orderings shown with reference to table 4 above are, in order: second event group 21- > second event group 22- > second event group 23, taking two second event groups, second event group 21 and second event group 22, that are adjacent in order as an example, then: all events in the second, subsequent event group 22 are respectively the next event associated with the corresponding event in the second event group 21.

In one specific implementation, the 1034 "determines a level of each event in each second event group corresponding to the event tree" includes:

10341. sorting the plurality of second event groups to obtain sorted second event groups; wherein, any one of the plurality of second event groups after sequencing sequences adjacent two second event groups, each event in one second event group after sequencing is correspondingly associated with a corresponding event in another second event group before sequencing;

10342. incrementally adding a second number to the ordered plurality of second event groups;

10343, after the addition is completed, determining the second number corresponding to each second event group as the corresponding level of each event in each corresponding second event group in the event tree.

In 1035, an event result table may be obtained according to the level corresponding to each event in each second event group and the corresponding associated event; then, an event tree is generated according to the event result table. In the process of generating the event result table, if the two events with the corresponding association relationship are found to be generated by the behaviors of at least one user, the number of the at least one user can be determined to be the weight corresponding to the user behavior path reflected by the two events with the corresponding association relationship. Based on the foregoing, in a specific implementation technical solution, the step of generating the event tree "by the above 1035" according to the level corresponding to each event in each second event group may be implemented as follows:

10351. Determining an event result table according to the level corresponding to each event in each second event group and the corresponding associated event; each row in the event result table contains two events which are associated correspondingly, and the level and the weight corresponding to one event with an early time stamp in the two events; the weight reflects the number of users whose behavior is generated from one event to the other event of the two events whose time stamps are early;

10352. and generating the event tree with the weight according to the event result table.

To facilitate an intuitive understanding of the steps 10351 to 10352, table 5 below shows an example of an event result table determined based on the levels and corresponding associated events corresponding to the events in the second event groups shown in table 4.

TABLE 5

Accordingly, fig. 5a shows a schematic diagram of an event tree with weights generated based on the above table 5, in other words, fig. 5 shows that the behavior paths of the five users, user1 to user5, shown in the above table 5 are combined into one weighted tree (i.e. event tree) represented by an adjacency table.

With continued reference to fig. 5a, in the event tree, event start_event0 (which is the start event) corresponding to level 1 is used as the root node, and events corresponding to other levels, such as event1 to event5, are used as leaf nodes. The path from the root node start_event0 to any one leaf node represents one behavior path. For example, the path from root node start_event0 to leaf node event1 represents one behavior path, and the path from root node start_event0 to leaf node event2 represents another behavior path. The path from the root node start_event0 to the leaf node event2 corresponds to a weight of 3, i.e. it means that 3 users are recorded from start_event0 to event2 for user behavior, as in connection with table 4, the 3 users comprise: user2, user3, and user4. The weight corresponding to the path from the root node start_event0 to the leaf node null is 1, that is, it means that 1 user is recorded from start_event0 to null for user behavior, for example, in combination with table 4, the 1 user is user5, and the behavior reflecting the user5 generates only one start event start_event0.

After generating the event tree, the storage end may send the event tree to the server end by executing the step 104, and the server end generates a corresponding behavior path analysis chart according to the event tree, so that an analyst may perform behavior path analysis on the user according to the behavior path analysis chart.

In specific implementation, as shown in fig. 4b, the server may parse the event tree first, and then perform conversion processing on the parsed event tree to convert the event tree into a graph data structure composed of nodes and edges, and send the graph data structure to the client, where the client renders and generates a corresponding behavior path analysis graph for display. Based on the above, that is, when the server generates a corresponding behavior path analysis graph according to the event tree, the following steps may be adopted to implement:

s204, converting the event tree into a corresponding graph data structure, sending the corresponding graph data structure to a client, and generating and displaying a corresponding behavior path analysis graph by the client according to the graph data structure;

each node in the graph data structure represents an event, each side represents a corresponding association relationship between two nodes, and the attribute of each side contains a weight, wherein the weight reflects the number of users from one node to the other node in the two nodes corresponding to the corresponding side.

In the foregoing, each edge in the graph data structure may be a directed edge, and accordingly, the weight reflection behavior described above generates the number of users from one node to the other node of the two nodes corresponding to the corresponding edge, as viewed according to the scheme indicated by the corresponding edge.

Fig. 5b shows a schematic diagram of a graph data structure transformed from the event tree shown in fig. 5 a. Taking the edge P as an example, the attribute of the edge P includes a weight value of 3, that is, reflects the number of users from node 0 to node 2 in which the behavior is generated, and the nodes 0 and 2 represent events start_event0 and event2, respectively.

The generated behavior path analysis chart may be Sang Jitu (Sankey diagram), but may be other types of analysis charts currently, which is not limited in this embodiment. According to the use habit of the industry analysis, sang Jitu is preferred.

In summary, according to the technical scheme provided by the embodiment, the storage end queries a plurality of events from event data generated by different user behaviors stored in the storage end according to the first statement on the basis of receiving a program statement including the first statement (used for querying the event) and the second statement (used for processing the queried event) sent by the server end; and then, through executing a second sentence, processing the plurality of events to obtain an event tree, sending the event tree to a server, and generating a corresponding behavior path analysis graph by the server according to the event tree so as to enable an analyst to perform user behavior path analysis according to the behavior path analysis graph. The program statement sent by the server to the storage terminal in the scheme has the functions of inquiring the event and processing the inquired event, so that the storage terminal can directly process the inquired event without returning the inquired event to the server for processing by the server, and finally, only the corresponding processing result (event tree) is returned to the server for processing by the server, thereby effectively reducing the data volume of communication transmission between the storage terminal and the server, being beneficial to quickly returning a behavior path analysis chart to an analyst so as to accelerate the realization of user behavior path analysis, and having more remarkable beneficial effects especially under the condition that the inquired event data volume is larger or the analysis request is large and concurrent. For example, in the existing scheme, the program statement sent by the server to the storage end generally only has a query function, after the storage end queries a plurality of events according to the received program statement, the plurality of events need to be transmitted back to the server end to be submitted to the server end for a subsequent series of processing, if the number of the plurality of events is large, because of the limitation of transmission bandwidth, only a small amount of event data can be generally transmitted by one transmission, which definitely takes a long time to completely transmit all queried events to the server end, and further the period of analysis of the whole user behavior path is also prolonged; in addition, if the analysis request of the user behavior path analysis is large and concurrent, the storage end needs to transmit a large amount of corresponding event data to the server end respectively aiming at the large and concurrent analysis request, which is liable to cause the occurrence of phenomena such as analysis service breakdown. The storage end of the scheme directly processes the queried event data, and then only a corresponding processing result (event tree) is returned to the server end, and after the processing, the data volume occupied by the event tree is always smaller, so that the event tree can be quickly transmitted to the server end, and the server end can also quickly process the event tree to generate a corresponding behavior path analysis chart and return the corresponding behavior path analysis chart to corresponding analysts, thereby effectively shortening the period of user behavior path analysis; in addition, due to the small data size of the event tree, even if the analysis request is large and concurrent, the event tree can well cope with the situation, and the occurrence rate of adverse phenomena such as analysis service breakdown is low. With the above, the scheme rapidly realizes the analysis of the user behavior path in a query mode, and can simplify the flow of the analysis of the user behavior path.

The application further provides a behavior path analysis method. The behavior path analysis method is suitable for the server, and detailed description of the server can be found in the related content, and in addition, the specific flow of the method can be found in fig. 3. As shown in fig. 3, the behavior path analysis method according to another embodiment of the present application includes the following steps:

s203, the program statement is sent to a storage end, and the storage end inquires a plurality of events from event data generated by different stored user behaviors by executing the program statement, and processes the events to obtain an event tree;

In the above 201, the plurality of request parameters includes at least one of: query conditions, time interval.

Further, the method provided in this embodiment may further include the following steps:

s204, receiving the event tree returned by the storage end;

s205, converting the event tree into a corresponding graph data structure, sending the corresponding graph data structure to a client, and generating and displaying a corresponding behavior path analysis graph by the client according to the graph data structure;

It should be noted that, in addition to the steps shown in the foregoing, the behavior path analysis method provided in the present embodiment may further include other steps, and for the specific possible other steps, reference may be made to each step in the other behavior path analysis method provided in the foregoing, which is not described herein.

In the foregoing, the technical solution provided by the embodiment of the present application is mainly described in terms of "program statement", and the following description describes the solution of the present application in combination with other aspects. In particular, the method comprises the steps of,

Fig. 6 is a flow chart of a behavior path analysis method according to another embodiment of the present application, where the method is applicable to a storage side, and details of the storage side can be found in the foregoing related embodiments. As shown in fig. 6, the behavior path analysis method provided by the embodiment of the application includes the following steps:

301. performing first grouping processing on a plurality of events inquired from the event data to obtain a plurality of first event groups; wherein each event in each first event group has the same user identification and is ordered according to the time stamp, and each event is respectively associated with the next adjacent event in the group;

303. dividing each first event group according to the time interval to determine the corresponding level in the event tree for the plurality of events according to the dividing result;

304. generating an event tree according to the levels corresponding to the events;

305. and sending the event tree to a server, and generating a behavior path analysis chart by the server according to the event tree.

In an implementation technical solution, the step 302 of dividing each first event group according to the time interval to determine the respective corresponding level in the event tree for the plurality of events according to the division result may specifically include:

3031. Dividing each first event group according to the time interval, and respectively adding a first number for at least one group segment corresponding to each divided first event group in an incremental way to obtain a first number of each group segment to which each of the plurality of events belongs;

3032. performing a second grouping process on the events according to the first numbers of the group segments to which the events belong and the attribute information corresponding to the first numbers, so as to obtain a plurality of second event groups; the first numbers of the group sections of each event in each second event group are the same but have different user identifications;

3033. determining the corresponding level of each event in each second event group in the event tree; wherein the levels corresponding to the events in each second event group are the same.

Correspondingly, the above 304 "generates an event tree according to the levels corresponding to the events", and the sentence-changing expression is: generating an event tree according to the corresponding level of each event in each second event group in the event tree.

300a, receiving a program statement sent by the server, wherein the program statement comprises a first statement for inquiring an event and a second statement for processing the inquired event; the second sentence contains the time interval;

300b, executing the first sentence to query a plurality of events from the event data;

300c, executing the second statement, and triggering the step of executing the first grouping processing on the events.

According to the technical scheme provided by the embodiment, after a storage end queries a plurality of events in event data stored by the storage end, a plurality of first event groups can be obtained by carrying out first grouping processing on the plurality of events, each event in each first event group has the same user identification and is ordered according to a timestamp, and each event is respectively associated with the next adjacent event in the group; further, each first event group may be further segmented according to a time interval, so as to determine levels corresponding to each of the event trees for the plurality of events according to a segmentation result, generate event trees according to the levels corresponding to the plurality of events, send the event trees to a server, and generate a behavior path analysis graph according to the event trees by the server. According to the method and the device, the first event groups (or the called user paths) corresponding to the users are segmented by using the time intervals, the user paths are supported to be segmented by any time interval, the problem that the application range is limited due to the fact that the user identifiers are used for segmenting the user paths in the existing scheme can be effectively solved, and the application range is wide.

It should be noted that, in addition to the steps shown in the foregoing, the behavior path analysis method provided in the present embodiment may further include other steps, and for the specific possible other steps, reference may be made to each step in the behavior path analysis method provided in the other embodiments of the present application, which is not described herein. As well as details concerning the specific implementation of the steps in this embodiment, reference may also be made to the details of the other embodiments above.

Fig. 7 is a flowchart of a behavior path analysis method according to another embodiment of the present application, where the method is applicable to a client, and details about the client may be found in the foregoing related embodiments. As shown in fig. 7, the behavior path analysis method provided by the embodiment of the application includes the following steps:

401. displaying an interactive interface;

402. responding to the query condition and the time interval input through the interactive interface, generating a program statement through a server according to the query condition and the time interval, and sending the program statement to a storage end;

403. receiving a graph data structure sent by the server;

404. generating and displaying a behavior path analysis chart on the interactive interface according to the chart data structure;

For details regarding the implementation of 401-404, reference may be made to the description of other embodiments.

It should be noted that, in addition to the steps shown in the foregoing, the behavior path analysis method provided in the present embodiment may further include other steps, and for the specific possible other steps, reference may be made to each step in the behavior path analysis method provided in the other embodiments of the present application, which is not described herein.

The application also provides a behavior path analysis system corresponding to each method embodiment provided by the application. As shown in fig. 4a to 4c, the behavior path analysis system provided by the embodiment of the present application includes: a server 20 and a storage 10; wherein, the liquid crystal display device comprises a liquid crystal display device,

The server 20 is configured to send a program sentence to the storage, where the program sentence includes a first sentence and a second sentence; the first statement is used for inquiring the event, and the second statement is used for processing the inquired event;

the storage end 10 stores event data of different user behaviors, and is used for inquiring a plurality of events from the event data according to the first statement; executing the second sentence to process the plurality of events to obtain an event tree; and sending the event tree to a server, and generating a behavior path analysis chart by the server according to the event tree.

Further, the system provided by the embodiment of the application further comprises: a client 30;

the server 20, when configured to generate a behavioral path analysis graph according to the event tree, is specifically configured to: converting the event tree into a corresponding graph data structure and sending the corresponding graph data structure to a client; each node in the graph data structure represents an event, each side represents a corresponding association relationship between two nodes, and the attribute of each side contains a weight, wherein the weight reflects the number of users from one node to the other node in the two nodes corresponding to the corresponding side.

The client 30 is configured to generate and display the behavior path analysis graph according to the graph data structure.

The behavior path analysis system provided in the above embodiment is a user behavior analysis platform, which may be, but not limited to, CDP (Customer Data Platform, user data platform).

For details of each of the above-mentioned server, storage, client, etc., reference should be made to the related content in the other embodiments.

It should be noted that, in addition to the steps shown above, each end in the behavior path analysis system provided in this embodiment may also implement other steps, and for specific implementation, reference may be made to each step in the behavior path analysis method provided in other embodiments of the present application, which is not described herein.

Fig. 8 is a schematic structural diagram of a behavior path analysis device according to an embodiment of the present application, where the behavior path analysis device is disposed at a storage end, and event data generated by different user behaviors are stored in the storage end. As shown in fig. 8, the behavior path analysis device provided in the present embodiment includes: a receiving module 51, a query module 52, an executing module 53 and a transmitting module 54; wherein, the liquid crystal display device comprises a liquid crystal display device,

A receiving module 51, configured to receive a program statement sent by a server; the program statement comprises a first statement and a second statement; the first statement is used for inquiring the event, and the second statement is used for processing the inquired event;

a query module 52, configured to query a plurality of events from the event data according to the first sentence;

an execution module 53, configured to execute the second sentence to process the plurality of events to obtain an event tree;

and the sending module 54 is configured to send the event tree to a server, and the server generates a behavior path analysis graph according to the event tree.

Further, the second sentence includes a time interval; and the execution module 53, when configured to execute the second sentence to process the plurality of events to obtain an event tree, is specifically configured to:

performing first grouping processing on the events based on attribute information of the events to obtain a plurality of first event groups; wherein each event in each first event group has the same user identification and is ordered according to the time stamp, and each event is respectively associated with the next adjacent event in the group;

Dividing each first event group according to the time interval, and respectively adding a first number for at least one group segment corresponding to each divided first event group in an incremental way to obtain a first number of each group segment to which each of the plurality of events belongs;

performing a second grouping process on the events according to the first numbers of the group segments to which the events belong and the attribute information corresponding to the first numbers, so as to obtain a plurality of second event groups; the first numbers of the group sections of each event in each second event group are the same but have different user identifications;

determining the corresponding level of each event in each second event group in the event tree; wherein the levels corresponding to the events in each second event group are the same;

and generating the event tree according to the level corresponding to each event in each second event group.

Further, the executing module 53, when configured to segment each first event group according to the time interval, is specifically configured to: determining target adjacent two events in which the difference value of the time stamps in each first event group is greater than or equal to the time interval; determining the segmentation position of each first event group according to the two adjacent events of the target; and cutting each first event group according to the cutting position to obtain at least one group segment corresponding to each first event group.

Further, the executing module 53, when used for determining the corresponding level of each event in each second event group in the event tree, is specifically configured to: sorting the plurality of second event groups to obtain sorted second event groups; wherein, any one of the plurality of second event groups after sequencing is adjacent to two second event groups after sequencing, each event in one second event group after sequencing is correspondingly associated with a corresponding event in another second event group before sequencing; incrementally adding a second number to the ordered plurality of second event groups; after the addition is completed, determining the second number corresponding to each second event group as the corresponding level of each event in each corresponding second event group in the event tree.

Further, the executing module 53 is specifically configured to, when generating the event tree according to the level corresponding to each event in each second event group: determining an event result table according to the level corresponding to each event in each second event group and the corresponding associated event; each row in the event result table contains two events which are associated correspondingly, and the level and the weight corresponding to one event with an early time stamp in the two events; the weight reflects the number of users whose behavior is generated from one event to the other event of the two events whose time stamps are early; and generating the event tree with the weight according to the event result table.

Further, the execution module 53 is specifically configured to, when performing a first grouping process on the plurality of events based on attribute information of the plurality of events: dividing the events with the same user identification into a group according to the user identifications of the events to obtain a plurality of groups; sorting the events contained in each group in a group according to the time stamps of the events; after the sorting is finished, respectively determining adjacent next events for all events in the group in each group, and establishing corresponding association, so as to obtain the plurality of groups after the establishment to determine the plurality of first event groups.

Further, the first sentence contains a query condition; and the query module 52, when configured to query a plurality of events from the event data according to the first sentence, is specifically configured to: executing the first statement, and querying a plurality of events meeting the query condition from the event data.

What needs to be explained here is: the details of each step in the behavior path analysis device provided in this embodiment may be referred to the corresponding content in each embodiment, and will not be described herein. In addition, the behavior path analysis device provided in this embodiment may further include other part or all of the steps in the foregoing embodiments, and specific reference may be made to the corresponding content in the foregoing embodiments, which is not repeated herein.

Fig. 9 is a schematic structural diagram of a behavior path analysis device according to another embodiment of the present application, where the behavior path analysis device is disposed at a server. As shown in fig. 9, the behavior path analysis device provided in the present embodiment includes: a receiving module 61, a filling module 62 and a transmitting module 63; wherein, the liquid crystal display device comprises a liquid crystal display device,

a receiving module 61, configured to receive an analysis request sent by a client, where the analysis request includes a plurality of request parameters;

a filling module 62, configured to fill the plurality of request parameters into the adapted positions in the program statement template, so as to generate a program statement;

a sending module 63, configured to send the program statement to a storage side, where the storage side queries a plurality of events from event data generated by different stored user behaviors by executing the program statement, and processes the plurality of events to obtain an event tree;

Further, the receiving module 61 is further configured to: receiving the event tree returned by the storage end; converting the event tree into a corresponding graph data structure, sending the corresponding graph data structure to a client, and generating and displaying a corresponding behavior path analysis graph by the client according to the graph data structure; each node in the graph data structure represents an event, each side represents a corresponding association relationship between two nodes, and the attribute of each side contains a weight, wherein the weight reflects the number of users from one node to the other node in the two nodes corresponding to the corresponding side.

Further, the plurality of request parameters includes at least one of: query conditions, time interval.

Fig. 10 shows a schematic structural diagram of a behavior path analysis device according to still another embodiment of the present application, where the behavior path analysis device is provided on a storage side, and event data of different user behaviors are stored in the storage side. As shown in fig. 10, the behavior path analysis device provided in the present embodiment includes: a processing module 71, a segmentation determining module 72, a generating module 73 and a transmitting module 74; wherein, the liquid crystal display device comprises a liquid crystal display device,

a processing module 71, configured to perform a first grouping process on a plurality of events queried from the event data, to obtain a plurality of first event groups; wherein each event in each first event group has the same user identification and is ordered according to the time stamp, and each event is respectively associated with the next adjacent event in the group;

A segmentation determining module 72, configured to segment each first event group according to a time interval, so as to determine respective corresponding levels in the event tree for the plurality of events according to a segmentation result;

a generating module 73, configured to generate an event tree according to the levels corresponding to the events;

and the sending module 74 is used for sending the event tree to a server, and generating a behavior path analysis chart according to the event tree by the server.

Further, the segmentation determining module 72, when configured to segment each first event group according to a time interval, to determine respective levels corresponding to the plurality of events in the event tree according to a segmentation result, is specifically configured to:

Determining the corresponding level of each event in each second event group in the event tree; wherein the levels corresponding to the events in each second event group are the same.

Further, the behavior path analysis device provided in this embodiment further includes: a receiving module and an executing module, wherein,

the receiving module is used for receiving the program statement sent by the server, wherein the program statement comprises a first statement used for inquiring the event and a second statement used for processing the inquired event; the second sentence contains the time interval;

an execution module, configured to execute the first sentence to query a plurality of events from the event data; and the step of executing the second sentence, triggering and executing the first grouping processing of the events.

Fig. 11 shows a schematic structural diagram of a behavior path analysis device according to still another embodiment of the present application, which is provided to a client. As shown in fig. 11, the behavior path analysis apparatus provided in the present embodiment includes: a display module 81, a response module 82, a receiving module 83, and a generating module 84; wherein, the liquid crystal display device comprises a liquid crystal display device,

a display module 81 for displaying an interactive interface;

the response module 82 is configured to respond to a query condition and a time interval input through the interactive interface, generate a program statement according to the query condition and the time interval through a server, and send the program statement to a storage end;

a receiving module 83, configured to receive a graph data structure sent by the server;

a generating module 84, configured to generate a behavior path analysis graph according to the graph data structure; and a display module 81, further configured to display a behavior path analysis chart on the interactive interface;

Fig. 12 is a schematic diagram of a structure of a storage terminal according to an embodiment of the application. As shown in fig. 12, the storage terminal includes: a memory 91 and a processor 92. Wherein the memory 91 is used for storing event data generated by different user actions and is also used for storing one or more computer programs; the processor 92 is coupled to the memory 91, and is configured to execute the one or more computer programs stored in the memory (e.g., a computer program implementing logic such as data storage, processing, etc.) to implement the steps in the embodiments of the behavior path analysis method shown in fig. 1 and 6.

The memory 91 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.

Further, as shown in fig. 12, the storage end may further include: communication component 93, power component 94, audio component 95, display 96, and other components. Only some of the components are schematically shown in fig. 12, which does not mean that the storage side only includes the components shown in fig. 12.

Another embodiment of the present application also provides a server, which has a structure similar to that of the storage terminal shown in fig. 12. Specifically, the server side includes: a memory and a processor. Wherein the memory is for storing one or more computer programs; the processor is coupled to the memory, and is configured to execute the one or more computer programs stored in the memory (e.g., a computer program implementing data storage, processing logic) to implement the steps in the embodiment of the behavior path analysis method shown in fig. 3.

For details of the memory, reference may be made to the relevant content of the other embodiments described above.

Further, the server may further include: communication components, power components, audio components, displays, and other components.

Yet another embodiment of the present application provides a client having a structure similar to that of the storage terminal shown in fig. 12. Specifically, the client includes: a display, a memory, and a processor. The display is used for displaying the interactive interface and displaying the behavior path analysis chart on the interactive interface; the memory is used for storing one or more computer programs; the processor is coupled to the memory, and is configured to execute the one or more computer programs stored in the memory (e.g., a computer program implementing logic for data storage, processing, etc.) to implement the steps in the embodiment of the behavior path analysis method shown in fig. 7.

Further, the client may further include: communication components, power components, audio components, and the like.

Accordingly, the embodiments of the present application further provide a computer readable storage medium storing a computer program, where the computer program when executed by a computer can implement the steps or corresponding functions in the behavior path analysis method provided in each of the embodiments above.

The methods of the present application may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. Fig. 13 schematically shows a block diagram of a computer program product provided by the application. The computer program product comprises one or more computer programs/instructions 1001 which, when loaded and executed on a computer, perform all or part of the processes or functions of the vehicle control method according to the application. The computer may be a general purpose computer, a special purpose computer, a computer network, a network device, a user device, a core network device, an OAM, or other programmable apparatus.

The computer program or instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer program or instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired or wireless means. The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that integrates one or more available media. The usable medium may be a magnetic medium, e.g., floppy disk, hard disk, tape; but also optical media such as digital video discs; but also semiconductor media such as solid state disks. The computer readable storage medium may be volatile or nonvolatile storage medium, or may include both volatile and nonvolatile types of storage medium.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the application 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 scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. The behavior path analysis method is characterized by being suitable for a storage end, wherein event data generated by different user behaviors are stored in the storage end; the method comprises the following steps:

executing the second sentence to generate an event tree according to the incidence relation among the events of the plurality of events and the corresponding level of each event;

the event tree is sent to a server, and a behavior path analysis chart is generated by the server according to the event tree;

wherein determining the level corresponding to each event includes:

performing first grouping processing on the events to obtain a plurality of first event groups;

dividing each first event group into at least one group segment according to the time interval included in the second sentence, and adding a first number to each group segment obtained by dividing; grouping the events with the same first number and different user identifications of the belonging group segments into a group to obtain a plurality of second event groups; and sequencing the plurality of second event groups, adding a second number to each second event group according to the sequencing result, wherein the corresponding level of each event is the second number of the second event group to which the corresponding level belongs.

2. The method of claim 1, wherein the first grouping of the plurality of events results in a plurality of first event groups, comprising:

performing first grouping processing on the events based on attribute information of the events to obtain a plurality of first event groups; wherein each event in each first event group has the same user identification and is ordered according to the time stamp, and each event is respectively associated with the next adjacent event in the group.

3. The method of claim 1, wherein slicing each first event group into at least one group segment at intervals comprised by the second statement comprises:

determining target adjacent two events in which the difference value of the time stamps in each first event group is greater than or equal to the time interval;

determining the segmentation position of each first event group according to the two adjacent events of the target;

and cutting each first event group according to the cutting position to obtain at least one group segment corresponding to each first event group.

4. The method of claim 1, wherein sorting the plurality of second event groups and adding a second number to each second event group according to the sorting result comprises:

Sorting the plurality of second event groups to obtain sorted second event groups; wherein, any one of the plurality of second event groups after sequencing is adjacent to two second event groups after sequencing, each event in one second event group after sequencing is correspondingly associated with a corresponding event in another second event group before sequencing;

and incrementally adding a second number to the ordered plurality of second event groups.

5. The method of claim 1, wherein generating an event tree according to the association relationship between the events of the plurality of events and the level corresponding to each event comprises:

determining an event result table according to the levels corresponding to the events and the association relation among the events; each row in the event result table comprises two events with corresponding association relations, and the level and the weight corresponding to one event with early time stamp in the two events; the weight reflects the number of users whose behavior is generated from one event to the other event of the two events whose time stamps are early;

and generating the event tree with the weight according to the event result table.

6. The method according to any one of claims 2 to 5, wherein performing a first grouping process on the plurality of events based on attribute information of the plurality of events, comprises:

Dividing the events with the same user identification into a group according to the user identifications of the events to obtain a plurality of groups;

sorting the events contained in each group in a group according to the time stamps of the events;

after the sorting is finished, respectively determining adjacent next events for all events in the group in each group, and establishing corresponding association, so as to obtain the plurality of groups after the establishment to determine the plurality of first event groups.

7. The method according to any one of claims 1 to 5, wherein the first sentence contains a query condition;

and according to the first statement, inquiring a plurality of events from the event data, wherein the method comprises the following steps:

executing the first statement, and querying a plurality of events meeting the query condition from the event data.

8. A behavioral path analysis method, suitable for a server, comprising:

receiving an analysis request sent by a client, wherein the analysis request carries a plurality of request parameters;

The program statement comprises a first statement and a second statement; the first statement is used for inquiring the event, and the second statement is used for processing the inquired event;

the storage end generates an event tree according to the incidence relation among the events of the plurality of events and the corresponding level of each event by executing the second statement; wherein determining the level corresponding to each event comprises: performing first grouping processing on the events to obtain a plurality of first event groups; dividing each first event group into at least one group segment according to the time interval included in the second sentence, and adding a first number to each group segment obtained by dividing; grouping the events with the same first number and different user identifications of the belonging group segments into a group to obtain a plurality of second event groups; and sequencing the plurality of second event groups, adding a second number to each second event group according to the sequencing result, wherein the corresponding level of each event is the second number of the second event group to which the corresponding level belongs.

9. The method as recited in claim 8, further comprising:

receiving the event tree returned by the storage end;

converting the event tree into a corresponding graph data structure, sending the corresponding graph data structure to a client, and generating and displaying a corresponding behavior path analysis graph by the client according to the graph data structure;

10. The method of claim 8 or 9, wherein the plurality of request parameters includes at least one of: query conditions, time interval.

11. A behavioral path analysis system, comprising:

the storage end is used for storing event data of different user behaviors and inquiring a plurality of events from the event data according to the first statement; executing the second sentence to generate an event tree according to the incidence relation among the events of the plurality of events and the corresponding level of each event; the event tree is sent to a server, and a behavior path analysis chart is generated by the server according to the event tree;

Wherein determining the level corresponding to each event includes:

12. The system of claim 11, further comprising: a client;

the server side is specifically configured to, when configured to generate a behavior path analysis graph according to the event tree: converting the event tree into a corresponding graph data structure and sending the corresponding graph data structure to a client; each node in the graph data structure represents an event, each side represents a corresponding association relationship between two nodes, and the attribute of each side comprises a weight, wherein the weight reflects the number of users from one node to the other node in the two nodes corresponding to the corresponding side;

And the client is used for generating and displaying the behavior path analysis graph according to the graph data structure.

13. A storage terminal, comprising: a memory and a processor; wherein, the liquid crystal display device comprises a liquid crystal display device,

the memory is used for storing event data generated by different user behaviors;

the processor, coupled to the memory, for implementing the steps in the behavioral path analysis method of any one of the above claims 1 to 7.

14. A server, comprising: a memory and a processor; wherein, the liquid crystal display device comprises a liquid crystal display device,

the memory is used for storing a computer program;

the processor, coupled to the memory, for executing the computer program stored in the memory for implementing the steps in the behavioral path analysis method according to any one of the preceding claims 8 to 10.