User behavior path mining method, user behavior path analysis method, storage medium and electronic equipment

Abstract

The embodiment of the invention discloses a user behavior path mining method and device, a user behavior path analysis method and device, a computer readable storage medium and electronic equipment. The method comprises the steps of obtaining a character information base, wherein preset characters corresponding to a plurality of operation objects and a plurality of operation behaviors associated with a target application are stored in the character information base; a plurality of to-be-analyzed nodes are obtained, corresponding preset characters are matched from a character information base according to target operation objects and target operation behaviors associated with the different to-be-analyzed nodes, character processing is carried out on the to-be-analyzed nodes, and node character strings associated with the to-be-analyzed nodes are obtained; grouping the plurality of node character strings, and identifying starting and ending point pairs included in different groups according to the starting point character and the ending point character; and extracting a path node sequence from the corresponding group according to the starting and ending point pairs. The path mining processing process is simplified, processing resources needed by path mining are saved, and the path mining response speed is increased.

Description

Technical Field

[0001] The present application relates to the field of information processing technology, and in particular to a user behavior path mining method and device, a user behavior path analysis method and device, a computer-readable storage medium, and an electronic device. Background Art

[0002] The user path is the user's behavioral path in application products such as websites and apps. Through path mining and path analysis, we can understand the user flow trend of application products and provide a basis for decision-making in improving product design, optimizing user experience, and increasing user satisfaction.

[0003] The existing path mining solution needs to maintain multiple data tables, match and split in the node mapping table, and associate the node mapping table with the node sample table to obtain the path node set. Such a solution will consume a lot of computing resources for ultra-large-scale node sample tables, resulting in a long time for path mining. It can only be processed offline, and it is difficult to transplant the solution to real-time computing scenarios.

[0004] How to reduce computing resource consumption and improve path mining response speed has become a technical problem that needs to be solved by technical personnel in this field. Summary of the invention

[0005] The present application provides a user behavior path mining method and device, a user behavior path analysis method and device, a computer-readable storage medium, and an electronic device, which help to simplify the path mining processing process, save the processing resources required for path mining, and improve the path mining response speed.

[0006] This application provides the following solutions:

[0007] A user behavior path mining method, comprising:

[0008] Obtaining a character information library, wherein the character information library stores preset characters corresponding to a plurality of operation objects and a plurality of operation behaviors associated with the target application;

[0009] Obtaining a plurality of nodes to be analyzed, and matching corresponding preset characters from the character information library according to target operation objects and target operation behaviors respectively associated with different nodes to be analyzed, so as to perform character processing on the plurality of nodes to be analyzed, and obtaining node strings respectively associated with the plurality of nodes to be analyzed;

[0010] Group the plurality of node character strings, and identify the start and end point pairs included in different groups according to the start character and the end character:

[0011] According to the start-end point pair, a path node sequence is extracted from the corresponding group.

[0012] The step of performing character processing on the plurality of nodes to be analyzed to obtain node character strings associated with the plurality of nodes to be analyzed includes:

[0013] The preset characters corresponding to the target operation object associated with the current node to be analyzed and the preset characters corresponding to the target operation behavior associated with the current node to be analyzed are merged, and the character strings are separated by the first identifier to obtain the node character string associated with the current node to be analyzed.

[0014] The step of grouping the plurality of node strings comprises:

[0015] According to the node attributes of the multiple nodes to be analyzed, the node character strings associated with each of the multiple nodes to be analyzed are divided into multiple groups, each group including at least one node character string;

[0016] According to the operation time corresponding to the operation behaviors associated with the multiple nodes to be analyzed, the node character strings included in each group are sorted and merged, and the character strings are separated by the second identifier.

[0017] The step of identifying the start and end point pairs of different groups according to the start character and the end character includes:

[0018] Obtaining the sorted and merged node character strings included in the current group, and identifying the starting character position and the ending character position therefrom according to the starting character and the ending character;

[0019] According to the start-end point pair matching method, a start point separator is inserted before at least one start point character position, and an end point separator is inserted after at least one end point character position, so as to obtain at least one start-end point pair included in the current group.

[0020] Wherein, the method further comprises:

[0021] Path screening information is obtained, and a target path matching the path screening information is determined from paths corresponding to different path node sequences.

[0022] Wherein, the method further comprises:

[0023] Obtaining node aggregation information, wherein the node aggregation information includes identification information of at least two nodes to be aggregated, and associated aggregation characters;

[0024] The characterization processing of the plurality of nodes to be analyzed to obtain node character strings associated with the plurality of nodes to be analyzed includes:

[0025] When it is determined that the current node to be analyzed is the node to be aggregated, character processing is performed on the current node to be analyzed according to the aggregated character, so that the at least two nodes to be aggregated are associated with the same node character string.

[0026] A user behavior path mining method, comprising:

[0027] The client provides a first operation option for submitting a start point node and an end point node;

[0028] The starting point node and the ending point node are obtained through the first operation option, so as to match the starting point character associated with the starting point node and the ending point character associated with the ending point node from the character information library, and then when a plurality of nodes to be analyzed are obtained, character processing is performed to obtain node character strings associated with each of the plurality of nodes to be analyzed, and the plurality of node character strings are grouped, and the start and end point pairs respectively included in different groups are identified according to the starting point character and the end point character, and the path node sequence is extracted from the corresponding group according to the start and end point pairs, wherein the character information library stores preset characters corresponding to each of the plurality of operation objects and the plurality of operation behaviors associated with the target application;

[0029] A path mining display page is provided, the page including a first area, wherein the first area is used to display the node transformation process of different path node sequences.

[0030] Wherein, the page further includes a second area, and the method further includes:

[0031] Sort the paths corresponding to different path node sequences according to the number of path completions from high to low, and select some paths to be displayed;

[0032] The relevant information of the path to be displayed is displayed through the second area.

[0033] Wherein, the method further comprises:

[0034] Providing a second operation option for submitting the path screening information;

[0035] The path screening information is obtained through the second operation option, so as to match a target path from paths corresponding to different path node sequences according to the path screening information.

[0036] Wherein, the method further comprises:

[0037] Providing a third operation option for submitting node aggregation information, wherein the node aggregation information includes identification information of at least two nodes to be aggregated, and associated aggregation characters;

[0038] The node aggregation information is obtained through the third operation option, so that during characterization processing, the at least two nodes to be aggregated are associated with the same node string according to the node aggregation information.

[0039] Wherein, the method further comprises:

[0040] Providing a fourth operation option for submitting a start-end point pair matching method;

[0041] The start-end point pair matching method is obtained through the fourth operation option, so as to identify the start-end point pairs included in different groups according to the start-end point pair matching method.

[0042] A user behavior path analysis method, comprising:

[0043] A plurality of paths are obtained and path analysis is performed, wherein the path node sequences corresponding to the plurality of paths are extracted from the grouping of node character strings obtained by characterizing the plurality of nodes to be analyzed according to the starting character and the ending character, wherein the characterization is achieved by matching a character information library, wherein the character information library stores preset characters corresponding to each of a plurality of operation objects and a plurality of operation behaviors associated with the target application.

[0044] A user behavior path mining device, comprising:

[0045] An information library obtaining unit, used to obtain a character information library, wherein the character information library stores preset characters corresponding to a plurality of operation objects and a plurality of operation behaviors associated with a target application;

[0046] A node character string obtaining unit is used to obtain a plurality of nodes to be analyzed, and to match corresponding preset characters from the character information library according to target operation objects and target operation behaviors respectively associated with different nodes to be analyzed, so as to characterize the plurality of nodes to be analyzed, and obtain node character strings respectively associated with the plurality of nodes to be analyzed;

[0047] A start-end point pair identification unit, used for grouping the plurality of node character strings, and identifying the start-end point pairs included in different groups according to the start character and the end character;

[0048] The path node sequence extraction unit is used to extract the path node sequence from the corresponding group according to the start and end point pairs.

[0049] A user behavior path mining device, applied to a client, comprising:

[0050] A first operation option providing unit, used to provide a first operation option for submitting a start point node and an end point node;

[0051] a node obtaining unit, configured to obtain the starting point node and the ending point node through the first operation option, so as to match the starting point character associated with the starting point node and the ending point character associated with the ending point node from a character information library, and then, when a plurality of nodes to be analyzed are obtained, perform character processing to obtain node character strings associated with each of the plurality of nodes to be analyzed, and group the plurality of node character strings, identify the start and end point pairs respectively included in different groups according to the starting point character and the ending point character, and extract a path node sequence from the corresponding group according to the start and end point pairs, wherein the character information library stores preset characters corresponding to each of a plurality of operation objects and a plurality of operation behaviors associated with a target application;

[0052] The display page providing unit is used to provide a path mining display page, wherein the page includes a first area, and the first area is used to display the node transformation process of different path node sequences.

[0053] A user behavior path analysis device, comprising:

[0054] A path analysis unit is used to obtain multiple paths and perform path analysis. The path node sequences corresponding to the multiple paths are extracted from the grouping of node character strings obtained by characterizing multiple nodes to be analyzed based on the starting character and the end character. The characterization is achieved by matching a character information library, which stores preset characters corresponding to multiple operation objects and multiple operation behaviors associated with the target application.

[0055] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of any of the methods described above.

[0056] An electronic device, comprising:

[0057] one or more processors; and

[0058] A memory associated with the one or more processors, the memory being used to store program instructions, wherein the program instructions, when read and executed by the one or more processors, execute the steps of any of the aforementioned methods.

[0059] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0060] In an embodiment of the present application, character serialization processing can be performed on multiple nodes to be analyzed using preset characters, and the node information expressed by the data structure can be serialized into a node string. In this way, the subsequent path mining processing process is all implemented based on string operations, which can give full play to the low computational consumption of string operations, thereby saving processing resources required for path mining.

[0061] In addition, when identifying the start and end point pairs included in the group, path segmentation can be achieved by marking them with the start point separator and the end point separator, without the need for complex table associations, and can also save processing resources required for path mining.

[0062] In summary, the path mining solution provided in the embodiment of the present application helps to simplify the path mining process, save the processing resources required for path mining, improve the response speed of path mining, and provide a technical basis for the application of path mining in real-time computing scenarios.

[0063] Of course, any product implementing the present application does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0065] Figure 1 is a flow chart of a user behavior path mining method provided by an embodiment of the present application;

[0066] Figure 2 is a schematic diagram of a user behavior path mining system provided by an embodiment of the present application;

[0067] Figure 3 is a schematic diagram of a leave application form page provided in an embodiment of the present application;

[0068] Figure 4 is a schematic diagram of a configuration page provided in an embodiment of the present application;

[0069] Figure 5 is a schematic diagram of a path mining display page provided in an embodiment of the present application;

[0070] Figure 6 is a schematic diagram of a user behavior path mining device provided in an embodiment of the present application;

[0071] Figure 7 is a schematic diagram of another user behavior path mining device provided in an embodiment of the present application;

[0072] Figure 8 is a schematic diagram of a user behavior path analysis device provided in an embodiment of the present application;

[0073] Fig. 9 It is a schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0074] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0075] Before introducing the embodiments of the present application, the technical terms involved are explained first.

[0076] User behavior refers to the operation behavior of the user in the application product, such as access, click, slide, close, etc. In actual applications, the application product can at least be embodied as a website, an application APP (Application), a small program, an operating system, etc. The embodiment of the present application does not specifically limit the type of application product and the type of operation behavior.

[0077] Process Mining is a data analysis technology that tracks and analyzes the user's behavior path in the application product, identifies patterns and trends in user operations, and discovers potential problems and opportunities. Process Mining can help service providers of application products understand how users use their products or services, thereby improving product design, optimizing user experience, and increasing user satisfaction.

[0078] The following is an example to illustrate the implementation process of the existing path mining solution.

[0079] First, based on the global filtering conditions, samples can be extracted from the original event table to obtain the nodes to be analyzed and saved in the node sample table. The original event table can be used to record all user behaviors and page exposures according to timestamps. For example, the original event table can be a log table based on Event.

[0080] The node sample table stores node information of multiple nodes to be analyzed, such as identification information of the operator user, operation behavior, operation object, operation time, etc. For example, when user A clicks the submit button of page A at time 1, the node information of the node to be analyzed associated with the operation can be reflected as: user A, click, submit button of page A, time 1. This information can be stored in a structured manner in the node sample table.

[0081] Secondly, the nodes to be analyzed are grouped and sorted, and the starting and ending nodes included in each group are extracted and saved in the node mapping table. For example, the nodes to be analyzed are grouped according to the identification information of the operator user, and different operator users can correspond to different groups. Each group includes all the nodes to be analyzed associated with the corresponding operator user, and all the nodes to be analyzed in the group are sorted according to the operation time corresponding to the operation behavior. The obtained groups can be used to represent the operation behaviors performed by the operator user in time sequence.

[0082] For example, group 1 corresponding to user A includes the following nodes: xx, yy, xx, start, start, xx, xx, end, xx, end, xx, start, end. The following information of the starting node and the ending node can be extracted and saved in the node mapping table: (start, 4), (start, 5), (end, 8), (end, 10), (start, 12), (end, 13). Taking (start, 4) as an example, it can be used to indicate that the 4th node of group 1 is the starting node specified for path mining; taking (end, 8) as an example, it can be used to indicate that the 8th node of group 1 is the ending node specified for path mining.

[0083] Next, according to a preset start-end point pair matching method, the start-end point pairs are extracted from the node mapping table, and each start-end point pair is numbered and distinguished.

[0084] In the example given above, the following two start-end point pairs can be extracted: [(start, 5), (end, 8)], [(start, 12), (end, 13)], and the corresponding numbers are 1 and 2 respectively, so the following numbered start-end point pairs can be obtained: [(start, 5, 1), (end, 8, 1)], [(start, 12, 2), (end, 13, 2)].

[0085] Finally, the node sample table and the node mapping table are associated to obtain the node set in the path associated with each start and end point pair.

[0086] In the example of group 1, the nodes included in the sample table are: xx, yy, xx, start, start, xx, xx, end, xx, end, xx, start, end, and the start-end point pairs included in the mapping table are: [(start, 5, 1), (end, 8, 1)], [(start, 12, 2), (end, 13, 2)]. Through table association, 2 paths can be mined. Among them, the node set in the path associated with the start-end point pair numbered 1 is: (start, 5, 1), (xx, 6, 1), (xx, 7, 1), (end, 8, 1), and the node set in the path associated with the start-end point pair numbered 2 is: (start, 12, 2), (end, 13, 2).

[0087] By aggregating the path nodes in the two sets, we can obtain the following two path node sequences: ([start, xx, xx, end], 1), ([start, end, 2]).

[0088] In the above path mining scheme, multiple data tables need to be maintained. For example, the matching and segmentation of the path is completed in a separate node mapping table. After the matching and segmentation is completed, it is necessary to associate it back to the node sample table again to obtain the path node set, resulting in multiple map, reduce, and join processes in the path mining process. For ultra-large-scale node sample tables, it will bring huge computing resource consumption. The path mining process requires a long wait time (usually tens of minutes to several hours), making it difficult to transplant the solution to real-time computing scenarios for real-time path mining.

[0089] The map process can be used to distribute tasks, splitting large computing tasks into small tasks and distributing them to different machines for execution. For example, steps such as sample filtering and extraction, and extracting starting and ending nodes can be implemented in the map process.

[0090] The reduce process can be used to merge small tasks and merge the data obtained by executing small tasks on different machines. For example, steps such as identifying the start and end point pairs according to the start and end point pair matching method can be implemented in the reduce process.

[0091] The join process can be used to merge tables and associate data from different tables. For example, steps such as associating the sample table and the mapping table to obtain a path node set can be implemented in the join process.

[0092] For massive node data, the interactions between machines and the associations between tables consume a lot of processing resources and take a long time to process, which means that existing path mining solutions can only be processed offline.

[0093] In addition, due to the complexity of offline processing scheduling, the existing path mining solutions have many limitations in supporting task customization capabilities, making it difficult to flexibly customize the analysis process of path mining. For example, if you want to filter out specific paths that meet customized attributes during the path mining process, you need to modify the code for the customized task. Especially when performing path mining on massive node data, most of them are submitted to the big data analysis platform for implementation. If you modify the code for the customized task, you need to resubmit it to the big data analysis platform to run the code, which makes the process of implementing personalized customization based on the existing path mining solution complicated and inflexible.

[0094] Correspondingly, the embodiment of the present application provides a new path mining solution. For massive node data, path mining can achieve a TB (Terabyte) level data response in seconds, can quickly mine user paths, and then discover bottlenecks in the path through path analysis, and provide product designers with timely decision-making suggestions for process optimization.

[0095] The following is a detailed description of the specific implementation process of the user behavior path mining method provided in the embodiment of the present application with reference to examples. Figure 1 The flow chart shown may include:

[0096] S101: Obtain a character information library, wherein the character information library stores preset characters corresponding to a plurality of operation objects and a plurality of operation behaviors associated with a target application.

[0097] The embodiment of the present application provides a solution for serializing nodes to be analyzed by using preset characters, which helps to simplify the path mining process, save the processing resources required for path mining, and improve the response speed of path mining.

[0098] In practical applications, corresponding preset characters can be configured in advance for the operation objects and operation behaviors associated with the target application, so that in the process of path mining, the node to be analyzed can be serialized according to the target operation objects and target operation behaviors associated with the node to be analyzed. The embodiment of the present application does not limit the operation objects and operation behaviors associated with the target application, the preset characters associated with the operation objects, the preset characters associated with the operation behaviors, etc., which can be determined according to the use requirements.

[0099] Taking the target application APP1 as an example, the character information library may store the association relationship shown in Table 1 below.

[0100]

[0101] Table 1

[0102] In one implementation, the path mining solution of the embodiment of the present application can be implemented by a client, for example, a client associated with an analyst user performing path mining. The character information library can be stored on a terminal device deployed by the client, and the client obtains the character information library by reading local information, or the character information library can be stored on a cloud server, and the client obtains the character information library by remote reading.

[0103] In another implementation method, the path mining solution of the embodiment of the present application can be implemented by a server deployed on a cloud server, and the server can be triggered to perform path mining processing through a path mining request submitted by a client or through a scheduled task of path mining.

[0104] For example, you can use Figure 2 The user behavior path mining system shown in the figure implements the path mining process. Among them, the path mining system may include: a client associated with the analyst user (i.e., the user research client shown in the figure) and a server for implementing path mining (i.e., the analysis server shown in the figure). The user research client can obtain the path mining request submitted by the analyst user and send it to the analysis server. After the analysis server obtains the path mining request, it can perform path mining on the massive sample data, and obtain the path mining results and return them to the user research client for visual display. Among them, the massive sample data can be collected by the collection server from the behavior log of the operator user reported by the user client (i.e., the client associated with the operator user), and the data is maintained through the node sample table.

[0105] The embodiment of the present application does not limit the execution entity, triggering timing, etc. of the path mining solution, which can be determined based on actual usage requirements.

[0106] S102: obtaining a plurality of nodes to be analyzed, and matching corresponding preset characters from the character information library according to target operation objects and target operation behaviors respectively associated with different nodes to be analyzed, so as to characterize the plurality of nodes to be analyzed and obtain node strings respectively associated with the plurality of nodes to be analyzed.

[0107] As an example, the embodiment of the present application can also extract samples from the log table according to the global screening condition to obtain multiple nodes to be analyzed. The nodes to be analyzed can be used to represent the operation behavior performed by the operator user on the operation object at the operation time.

[0108] For example, the global filter criteria can be the target analysis page, such as Figure 3 For the leave application form page shown, path mining can be performed on the operator user's operations on the page; and / or, the global filtering condition can be the type of the operator user, such as Figure 3For the leave application form shown, path mining can be performed on the operations of regular employees and interns on this page; and / or, the global filtering condition can be the type of terminal device, such as Figure 3 The leave request form shown can perform path mining for operations on the mobile page and the computer page; and / or, the global filtering condition can be the log field type, such as Figure 3 The leave request form shown can perform path mining for page visits, clicks and other operations; and / or, the global filtering conditions can be multiple dimensions such as browser type, operating system type, geographic location, etc. The global filtering conditions can be configured according to usage requirements, and the nodes to be analyzed can be sampled accordingly.

[0109] As an example, a client could provide Figure 4 The configuration page shown is for analyst users to customize the global filtering conditions.

[0110] In an embodiment of the present application, character processing of the node to be analyzed based on the character information library can be reflected as follows: merging the preset characters corresponding to the target operation object associated with the current node to be analyzed and the preset characters corresponding to the target operation behavior associated with the current node to be analyzed, and separating the character strings through the first identifier to obtain the node string associated with the current node to be analyzed.

[0111] Any one of the multiple nodes to be analyzed can be used as the current node to be analyzed, and the node character string associated with the current node to be analyzed is obtained through the above characterization process.

[0112] For example, if the node 1 to be analyzed is a page visit to the homepage of APP1 by user A, it can be determined that the associated target operation object is the homepage, and the operation behavior is the page visit. According to the examples of preset characters listed in Table 1, the preset characters corresponding to the target operation object associated with node 1 are home, and the preset characters corresponding to the target operation behavior associated with node 1 are pv. If the first identifier is \001, the two are merged, and the node string obtained by separating them by the first identifier can be reflected as: pv\001home. Among them, the first identifier can be set according to the use requirements, and the embodiments of the present application do not limit this.

[0113] For another example, the node 2 to be analyzed is user A clicking the submit button of the details page of APP1. It can be determined that the associated target operation object is the submit button of the details page, and the operation behavior is click. According to the examples of preset characters listed in Table 1, the preset characters corresponding to the target operation object associated with node 2 are detail, submit, and the preset characters corresponding to the target operation behavior associated with node 2 is click. The merged node string can be reflected as: click\001detail\001submit.

[0114] For another example, the node 3 to be analyzed is user A clicking the save button on the details page of APP1. It can be determined that the associated target operation object is the save button on the details page, and the operation behavior is click. According to the examples of preset characters listed in Table 1, the preset characters corresponding to the target operation object associated with node 3 are detail, save, and the preset characters corresponding to the target operation behavior associated with node 3 is click. The merged node string can be reflected as: click\001detail\001save.

[0115] In summary, the node information expressed by the data structure can be serialized into a node string, providing a technical basis for simplifying the path mining process.

[0116] In addition, path mining based on string operations is more flexible than table operations in existing path mining solutions. Therefore, personalized customized attributes can be added during the path mining process by submitting configuration information.

[0117] As a preferred solution, the personalized customization task can be embodied as multi-node aggregation, that is, mapping at least two types of nodes with different characteristics to one type of node with the same characteristics. Among them, the operation objects and operation behaviors associated with the nodes can all be used as node characteristics.

[0118] Figure 3 In the example given, if the operator user clicks the Submit button and clicks the Save button, a database save event will be triggered. Then, in the path mining process, the two types of nodes to be analyzed, representing clicking the Submit button and clicking the Save button, can be aggregated according to usage requirements.

[0119] Specifically, the client associated with the analyst user may provide a third operation option for submitting node aggregation information, wherein the node aggregation information includes identification information of at least two nodes to be aggregated, and associated aggregation characters; the node aggregation information is obtained through the third operation option, so that during characterization processing, the at least two nodes to be aggregated are associated to the same node string according to the node aggregation information.

[0120] That is to say, when character processing is performed on the node to be analyzed, if it is determined that the current node to be analyzed is a node to be aggregated, preset character matching can be performed without using the character information library. Instead, the current node to be analyzed is characterized based on the aggregated characters in the node aggregation information, so as to associate at least two nodes to be aggregated to the same node string.

[0121] As an example, the aggregation character may be determined from preset characters associated with at least two nodes to be aggregated; or, a new character may be set as the aggregation character, which is not limited in the embodiments of the present application.

[0122] In the example above, if the nodes to be analyzed that represent clicking the submit button and clicking the save button are aggregated, that is, these two types of nodes are nodes to be aggregated, and the aggregated character is the character "save" associated with the two operation behaviors of "submit" and "save", then in the examples of nodes to be analyzed 2 and 3 to be analyzed above, the two nodes are associated with the same node string click\001detail\001save after characterization. In other words, in the subsequent path mining process, these two nodes with two characteristics can be identified as two nodes with one characteristic.

[0123] As an example, if the end node of the path mining is "click the save button on the details page of APP1", the node strings associated with nodes 2 and 3 can be represented as xx, end before the aggregation process, and can be represented as end, end after the aggregation process. Such a change may affect the subsequent steps of determining the start and end point pairs, and thus affect the path mining results. Among them, end is used to represent the end point character, and xx is used to represent the node string associated with other nodes except the start node and the end node.

[0124] S103: Grouping the plurality of node character strings, and identifying the start and end point pairs included in different groups according to the start point character and the end point character.

[0125] After completing the characterization processing of the nodes to be analyzed, the obtained node strings can be grouped, and the path node sequences can be extracted from the groups to complete the path mining process.

[0126] Specifically, according to the node attributes of the multiple nodes to be analyzed, the node strings associated with each of the multiple nodes to be analyzed can be divided into multiple groups, each group including at least one node string; according to the operation time corresponding to the operation behavior associated with the multiple nodes to be analyzed, the node strings included in each group are sorted and merged, and the strings are separated by a second identifier.

[0127] Among them, the node attribute of the node to be analyzed can be the identifier of the operator user, that is, different groups are divided for different operator users; or the node attribute can be the session identifier, that is, different groups are divided for different sessions; or the node attribute can be the identifier of the terminal device, that is, different groups are divided for different terminal devices; or the node attribute can be the identifier of the browser, that is, different groups are divided for different browsers; or the node attribute can be the identifier of the application product, that is, different groups are divided for different application products. In actual applications, node attributes can be configured through different dimensional information according to usage requirements, and group processing can be performed accordingly.

[0128] After completing the node string grouping, the node strings in the group are also sorted. The order of the node strings is used to reflect the event changes in the time series.

[0129] Taking the node attribute as the identification of the operator user as an example, the nodes associated with the operations performed by the same operator user in the target application can be divided into a group, and the node strings included in the group can be sorted according to the operation time corresponding to the operation behavior. In this way, the event changes in the time series are the actual operation path of the operator user in the target application.

[0130] In an embodiment of the present application, the node strings included in the group are merged, and the strings are separated by a second identifier. This can be reflected as follows: when it is determined that the current group includes at least two node strings, the at least two node strings can be merged and separated by the second identifier to obtain the sorted and merged node strings included in the current group.

[0131] The nodes 1 to 3 to be analyzed mentioned above are all operations performed by user A. The three nodes are grouped into one group according to the operator user ID. If the second ID is \002, the three nodes are merged and separated by the second ID. The resulting string can be embodied as: pv\001home\002click\001detail\001submit\002click\001detail\001save. The second ID can be set according to usage requirements, and this embodiment of the application does not limit this.

[0132] For example, the second identifier may also be represented by “,”, and the character string obtained by separation by the second identifier may be represented by: pv\001home, click\001detail\001submit, click\001detail\001save.

[0133] In an embodiment of the present application, identifying the start and end point pairs included in different groups based on the start character and the end character can include: obtaining a sorted and merged node string included in the current group, and identifying the start character position and the end character position therefrom based on the start character and the end character; according to the start and end point pair matching method, inserting a start point separator before at least one start character position, and inserting an end point separator after at least one end character position, to obtain at least one start and end point pair included in the current group.

[0134] In this example, according to the start-end point pair matching method, the start point separator and the end point separator can be inserted into the sorted and merged node strings included in each of the different groups to obtain the start-end point pairs included in each of the different groups.

[0135] In practical applications, path mining mainly targets the path from a specified starting point to a specified end point. The starting point node and the end point node can be predetermined by default configuration, or can be submitted by the analyst user through the client. For example, the client can Figure 4 The configuration page shown provides a first operation option for submitting a starting point node and an end point node; the starting point node and the end point node are obtained through the first operation option to match the starting point character associated with the starting point node and the end point character associated with the end point node from a character information library.

[0136] As an example, when the client acts as the executor of path mining, after obtaining the starting point node and the end point node submitted by the analyst user, it can directly match the corresponding starting point character and the end point character from the character information library; when the server acts as the executor of path mining, after obtaining the starting point node and the end point node submitted by the analyst user, it can send them to the server, and the server will match the corresponding starting point character and the end point character from the character information library.

[0137] against Figure 3 When performing path mining on the page shown, it can be determined that the starting node is a page visit to the leave form page, and the end node is clicking the submit button on the leave form page. The starting node and the end node can be characterized as described above to obtain the starting character and the end character.

[0138] After obtaining the starting character and the ending character, the position of the starting character and the position of the ending character can be identified in the sorted and merged node string included in the group, and then the starting point separator and the ending point separator are inserted to obtain the starting and ending point pair included in the group. In practical applications, the starting point separator and the ending point separator can be the same or different, which can be determined according to actual usage requirements. For example, both can be expressed as ","

[0139] For example, for the sorted and merged node strings included in the current group, the starting character position and the ending character position are identified and represented as: xx, yy, xx, start, start, xx, xx, end, xx, end, xx, where start is used to represent the starting character, end is used to represent the ending character, and xx and yy are used to represent the node strings associated with other nodes except the starting node and the ending node. From the above representation, it can be seen that the 4th and 5th positions are the starting character positions, and the 8th and 10th positions are the ending character positions.

[0140] If the start-end point pair matching method is greedy matching, that is, to ensure that the number of nodes between the start-end point pair is as large as possible, the start delimiter can be inserted before the 4th position and the end delimiter can be inserted after the 10th position. The node string after the above sorting and merging can be expressed as: xx, yy, xx,, start, start, xx, xx, end, xx, end,, xx. In this example, the current group includes 1 start-end point pair: the start character at the 4th position and the end character at the 10th position. If the node sequence is segmented, it can be expressed as: [xx, yy, xx], [start, start, xx, xx, end, xx, end], [xx].

[0141] If the start-end point pair matching method is minimum matching, that is, to ensure that the number of nodes between the start-end point pair is as small as possible, the start point separator can be inserted before the 4th position, the start point separator can be inserted before the 5th position, the end point separator can be inserted after the 8th position, and the end point separator can be inserted after the 10th position. The node string after the above sorting and merging can be expressed as: xx, yy, xx,, start,, start, xx, xx, end,, xx, end,, xx. In this example, the current group includes 1 start-end point pair: the start character at the 5th position and the end character at the 8th position. If the node sequence is segmented, it can be expressed as: [xx, yy, xx], [start], [start, xx, xx, end], [xx, end], [xx].

[0142] From the above examples, it can be seen that different start-end point pair matching methods may result in different numbers of start-end point pairs included in the grouping, nodes between the start-end point pairs, etc. Therefore, a suitable start-end point pair matching method can be selected according to usage requirements.

[0143] As a preferred solution, the embodiment of the present application can also provide a solution for flexibly configuring the start-end point pair matching method, that is, the personalized customization task can be reflected in configuring different start-end point pair matching methods. Specifically, the client can provide a fourth operation option for submitting the start-end point pair matching method; the start-end point pair matching method is obtained through the fourth operation option, so as to identify the start-end point pairs included in different groups according to the start-end point pair matching method.

[0144] It is understandable that in addition to the examples of greedy matching and minimum matching given above, the analyst user can also customize the personalized matching method and submit the matching method through the fourth operation option. For example, the customized matching method can be: when there are 3 consecutive starting characters, the position of the second starting character is determined as the starting point of the start-end point pair.

[0145] S104: Extracting a path node sequence from a corresponding group according to the start and end point pairs.

[0146] After determining the start-end point pair included in the group, a node sequence whose first node is the starting point of the start-end point pair and whose last node is the end point of the start-end point pair can be filtered out from the sorted and merged node string included in the group as the path node sequence extracted from the group.

[0147] In the example given above, for the start-end point pair determined by the greedy matching method, one path can be extracted in the current group, and the corresponding path node sequence is: start, start, xx, xx, end, xx, end; for the start-end point pair determined by the minimum matching method, one path can also be extracted in the current group, and the corresponding path node sequence is: start, xx, xx, end.

[0148] As a preferred solution, the embodiment of the present application can also provide a path screening solution, that is, the personalized customization task can be embodied as multi-dimensional path filtering. Specifically, the client can provide a second operation option for submitting path screening information; the path screening information is obtained through the second operation option to determine the target path that matches the path screening information from the paths corresponding to different path node sequences.

[0149] For example, path filtering information can be reflected as node attributes. For example, the paths extracted by grouping the operator user ID can be further configured with path filtering conditions in combination with dimensional information such as terminal device type, browser type, and operating system type, and the target path can be filtered accordingly. For example, the target path extracted for the operation performed on the computer page can be filtered.

[0150] And / or, the path screening information may be reflected as the characteristics of the extracted path. For example, the path screening conditions may be further configured in combination with characteristics such as path duration and path step length, and the target path may be screened accordingly. For example, the target path with a step length not greater than 7 may be screened.

[0151] The path duration can be determined by the operation time between two adjacent nodes in the path node sequence. Figure 5 The time between the start node and the start date node is 4.8s, indicating that the operation time taken by the operator from the start node to the start date node is 4.8s. By accumulating the operation time, the path duration corresponding to the path node sequence can be obtained. The path step length can be determined by the number of nodes included in the path node sequence. Taking the path node sequence start, xx, xx, end as an example, the path step length is 4.

[0152] Correspondingly, an embodiment of the present application may also provide a method for mining a user behavior path on a client side, which may specifically include: the client provides a first operation option for submitting a starting point node and an end point node; the starting point node and the end point node are obtained through the first operation option to match the starting character associated with the starting point node and the end point character associated with the end point node from a character information library, and then when multiple nodes to be analyzed are obtained, character processing is performed to obtain node strings associated with each of the multiple nodes to be analyzed, and the multiple node strings are grouped, and the start and end point pairs included in different groups are identified according to the starting character and the end point character, and path node sequences are extracted from the corresponding groups according to the start and end point pairs, and the character information library stores preset characters corresponding to multiple operation objects and multiple operation behaviors associated with the target application; a path mining display page is provided, and the page includes a first area, and the first area is used to display the node conversion process of different path node sequences.

[0153] As an example, a client could provide Figure 4 The configuration page shown may include a first operation option for the analyst user to submit the starting node and the end node for path mining.

[0154] Optionally, according to usage requirements, the configuration page may also include other operation options. For example, an operation option for submitting global filtering conditions, a second operation option for submitting path filtering information, a third operation option for submitting node aggregation information, a fourth operation option for submitting the start and end point pair matching method, an operation option for submitting the first identifier, an operation option for submitting the second identifier, an operation option for submitting the start point separator and the end point separator, etc., which can be determined by actual usage requirements and are not limited in the embodiments of the present application.

[0155] After the analyst user makes relevant configurations according to the usage requirements, click the "Query" button to trigger the client or server to Figure 1 The method shown in the figure performs online path mining. Correspondingly, after obtaining the path node sequence, the client can also provide the analyst user with Figure 5 The path mining display page shown is for users to view the path mining results. The path mining display page may include a first area, which is used to display the node transformation process of different path node sequences for the analyst user to view the jump between nodes.

[0156] Optionally, the path mining display page may also include a second area, which may sort the paths corresponding to different path node sequences from high to low according to the number of path completions, select some paths to be displayed, and display relevant information of the paths to be displayed through the second area.

[0157] As an example, the first N paths with the highest number of path completions can be selected as the paths to be displayed; or the number of paths to be displayed can be determined based on the total number of path mining and the preset proportion, and then the paths to be displayed that meet the number can be selected in descending order of the number of path completions. The number of path completions can be used to represent the number of people who follow the current path from the starting node to the end node. Figure 5 In the example given, the number of path completions for path 1 is 85, which means that 85 people completed the operation from the starting node to the end node along path 1.

[0158] It can be understood that the relevant information of the path to be displayed in the second area may include at least one of the following information: the number of path completions, the proportion of path completions, the path duration, the path step length, and the operations associated with the path (i.e., the sequence of nodes included in the path). The specific content of the relevant information that needs to be displayed to the analyst user can be determined based on usage requirements.

[0159] In addition, it should be noted that for the solution of matching the target path through path filtering information, the first area can be used to display the node transformation process of the node sequence corresponding to the target path, and the path to be displayed can be selected from the target path according to the number of path completions from high to low, and its related information can be displayed in the second area.

[0160] In summary, the path mining solution provided in the embodiment of the present application can bring the following beneficial effects:

[0161] Existing path mining solutions cannot distinguish different starting and ending points in the database table, so they can only create a node mapping table, distinguish them by numbering in the table, match and split them in the table, and then obtain the path node sequence by associating the table with the node sample table. Correspondingly, the path mining solution provided in the embodiment of the present application performs string operations, and can achieve path segmentation by identifying the starting point delimiter and the end point delimiter. It only needs to maintain the node sample table and perform single table operations. In other words, the string operation itself has the characteristic of low computational consumption, and no complex table association is required during the path mining process, so it can save path mining processing resources, improve the path mining response speed, and provide a technical basis for the application of path mining in real-time computing scenarios.

[0162] In addition, in terms of the computational process involved in path mining, existing solutions will generate multiple map, reduce, and join processes. The embodiment of the present application only needs to execute a reduce process once when the entire path node sequence is extracted, and the other steps are all implemented in the map process, which also helps to save path mining processing resources and improve the path mining response speed.

[0163] In addition, it should be noted that because the embodiment of the present application has the characteristics of low consumption and high performance, real-time calculation can be achieved even in the face of ultra-large-scale samples (that is, massive node data), so it can support the execution of the embodiment of the present application in a big data interactive analysis database (such as the Hologres database, etc.).

[0164] In addition, the embodiment of the present application can also provide a Figure 1 Specifically, multiple paths can be obtained for path analysis, and the path node sequences corresponding to the multiple paths are extracted from the grouping of node strings obtained by characterizing multiple nodes to be analyzed according to the starting character and the end character, and the characterization is achieved by matching a character information library, and the character information library stores preset characters corresponding to multiple operation objects and multiple operation behaviors associated with the target application.

[0165] As an example, the path analysis solution of the embodiment of the present application can be implemented by a client or server that performs path mining, that is, after completing path mining, it jumps to perform path analysis. Alternatively, it can be implemented by a client or server dedicated to path analysis, and the embodiment of the present application does not specifically limit this.

[0166] Through path analysis, it is possible to determine the hot spots, stuck points, and conversion or loss information in the user behavior path from the starting node to the end node. The specific implementation process of path analysis can be referred to the relevant technology, and the embodiments of this application do not limit this.

[0167] pass Figure 1 The scheme shown in the figure is used for path mining, which can improve the response speed of path mining and realize online calculation of path mining. Based on this, path analysis can also improve the overall response speed of path analysis, timely discover bottlenecks in the path, and provide product designers with decision-making suggestions for process optimization.

[0168] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0169] Corresponding to the above method embodiment, the present application embodiment also provides a user behavior path mining device, see Figure 6 , the device may include:

[0170] The information library obtaining unit 601 is used to obtain a character information library, wherein the character information library stores preset characters corresponding to a plurality of operation objects and a plurality of operation behaviors associated with the target application;

[0171] The node character string obtaining unit 602 is used to obtain a plurality of nodes to be analyzed, and match corresponding preset characters from the character information library according to target operation objects and target operation behaviors respectively associated with different nodes to be analyzed, so as to characterize the plurality of nodes to be analyzed and obtain node character strings respectively associated with the plurality of nodes to be analyzed;

[0172] A start-end point pair identification unit 603, configured to group the plurality of node character strings, and identify the start-end point pairs included in different groups according to the start character and the end character;

[0173] The path node sequence extraction unit 604 is used to extract the path node sequence from the corresponding group according to the start and end point pairs.

[0174] Among them, the node string acquisition unit can be specifically used to: merge the preset characters corresponding to the target operation object associated with the current node to be analyzed, and the preset characters corresponding to the target operation behavior associated with the current node to be analyzed, and separate the strings through the first identifier to obtain the node string associated with the current node to be analyzed.

[0175] Among them, the start and end point pair identification unit can be specifically used to: divide the node strings associated with each of the multiple nodes to be analyzed into multiple groups according to the node attributes of the multiple nodes to be analyzed, each group including at least one node string; sort and merge the node strings included in each group according to the operation time corresponding to the operation behavior associated with the multiple nodes to be analyzed, and separate the strings through a second identifier.

[0176] Among them, the start-end point pair identification unit can be specifically used to: obtain the sorted and merged node string included in the current group, and identify the starting character position and the ending character position therefrom according to the starting character and the ending character; according to the start-end point pair matching method, insert a starting point separator before at least one starting character position, and insert an ending point separator after at least one ending character position, to obtain at least one start-end point pair included in the current group.

[0177] Wherein, the device further comprises:

[0178] The target path determination unit is used to obtain path screening information and determine a target path matching the path screening information from paths corresponding to different path node sequences.

[0179] Wherein, the device further comprises:

[0180] A node aggregation information obtaining unit, used to obtain node aggregation information, wherein the node aggregation information includes identification information of at least two nodes to be aggregated, and associated aggregation characters;

[0181] The node character string obtaining unit may be specifically configured to: when determining that the current node to be analyzed is the node to be aggregated, characterize the current node to be analyzed according to the aggregated character, so that the at least two nodes to be aggregated are associated with the same node character string.

[0182] Corresponding to the above method embodiment, the present application embodiment also provides a user behavior path mining device, which is applied to the client, see Figure 7 , the device may include:

[0183] A first operation option providing unit 701 is used to provide a first operation option for submitting a start point node and an end point node;

[0184] The node obtaining unit 702 is used to obtain the starting point node and the ending point node through the first operation option, so as to match the starting point character associated with the starting point node and the ending point character associated with the ending point node from the character information library, and then, when a plurality of nodes to be analyzed are obtained, character processing is performed to obtain node strings associated with each of the plurality of nodes to be analyzed, and the plurality of node strings are grouped, and the start and end point pairs respectively included in different groups are identified according to the starting point character and the ending point character, and the path node sequence is extracted from the corresponding group according to the start and end point pairs, wherein the character information library stores preset characters corresponding to each of the plurality of operation objects and the plurality of operation behaviors associated with the target application;

[0185] The display page providing unit 703 is used to provide a path mining display page, and the page includes a first area, and the first area is used to display the node transformation process of different path node sequences.

[0186] The page further includes a second area, and the device further includes:

[0187] The path selection unit is used to sort the paths corresponding to different path node sequences according to the number of path completions from high to low, and select some paths to be displayed; and display the relevant information of the paths to be displayed through the second area.

[0188] Wherein, the device further comprises:

[0189] A second operation option providing unit, configured to provide a second operation option for submitting the path screening information;

[0190] A path screening information obtaining unit is used to obtain the path screening information through the second operation option, so as to match a target path from paths corresponding to different path node sequences according to the path screening information.

[0191] Wherein, the device further comprises:

[0192] A third operation option providing unit, configured to provide a third operation option for submitting node aggregation information, wherein the node aggregation information includes identification information of at least two nodes to be aggregated, and associated aggregation characters;

[0193] The node aggregation information obtaining unit is used to obtain the node aggregation information through the third operation option, so as to associate the at least two nodes to be aggregated with the same node string according to the node aggregation information during characterization processing.

[0194] Wherein, the device further comprises:

[0195] A fourth operation option providing unit, used to provide a fourth operation option for submitting a start-end point pair matching mode;

[0196] The matching mode obtaining unit is used to obtain the start-end point pair matching mode through the fourth operation option, so as to identify the start-end point pairs included in different groups according to the start-end point pair matching mode.

[0197] Corresponding to the above method embodiment, the present application embodiment also provides a user behavior path analysis device, see Figure 8 , the device may include:

[0198] The path analysis unit 801 is used to obtain multiple paths and perform path analysis. The path node sequences corresponding to the multiple paths are extracted from the grouping of node strings obtained by characterizing multiple nodes to be analyzed based on the starting character and the end character. The characterization is achieved by matching a character information library, and the character information library stores preset characters corresponding to multiple operation objects and multiple operation behaviors associated with the target application.

[0199] In addition, an embodiment of the present application further provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of any one of the methods in the aforementioned method embodiments are implemented.

[0200] And an electronic device, comprising:

[0201] one or more processors; and

[0202] A memory associated with the one or more processors, the memory being used to store program instructions, wherein the program instructions, when read and executed by the one or more processors, execute the steps of the method described in any one of the aforementioned method embodiments.

[0203] in, Fig. 9 The architecture of an electronic device is exemplarily shown. For example, device 900 can be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, an aircraft, etc.

[0204] Reference Fig. 9 , device 900 may include one or more of the following components: a processing component 902 , a memory 904 , a power component 906 , a multimedia component 908 , an audio component 910 , an input / output (I / O) interface 912 , a sensor component 914 , and a communication component 916 .

[0205] The processing component 902 generally controls the overall operation of the device 900, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 902 may include one or more processors 920 to execute instructions to complete all or part of the steps of the method provided by the technical solution of the present disclosure. In addition, the processing component 902 may include one or more modules to facilitate the interaction between the processing component 902 and other components. For example, the processing component 902 may include a multimedia module to facilitate the interaction between the multimedia component 908 and the processing component 902.

[0206] The memory 904 is configured to store various types of data to support operations on the device 900. Examples of such data include instructions for any application or method operating on the device 900, contact data, phone book data, messages, pictures, videos, etc. The memory 904 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, 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 disk or optical disk.

[0207] The power supply component 906 provides power to the various components of the device 900. The power supply component 906 can include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device 900.

[0208] The multimedia component 908 includes a screen that provides an output interface between the device 900 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 908 includes a front camera and / or a rear camera. When the device 900 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0209] The audio component 910 is configured to output and / or input audio signals. For example, the audio component 910 includes a microphone (MIC), and when the device 900 is in an operating mode, such as a call mode, a recording mode, and a speech recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 904 or sent via the communication component 916. In some embodiments, the audio component 910 also includes a speaker for outputting audio signals.

[0210] The input / output (I / O) interface 912 provides an interface between the processing component 902 and the peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0211] The sensor assembly 914 includes one or more sensors for providing various aspects of status assessment for the device 900. For example, the sensor assembly 914 can detect the open / closed state of the device 900, the relative positioning of components, such as the display and keypad of the device 900, and the sensor assembly 914 can also detect the position change of the device 900 or a component of the device 900, the presence or absence of user contact with the device 900, the orientation or acceleration / deceleration of the device 900, and the temperature change of the device 900. The sensor assembly 914 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 914 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 914 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0212] The communication component 916 is configured to facilitate wired or wireless communication between the device 900 and other devices. The device 900 can access a wireless network based on a communication standard, such as WiFi, or a mobile communication network such as 2G, 3G, 4G / LTE, 5G, etc. In an exemplary embodiment, the communication component 916 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 916 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0213] In an exemplary embodiment, the device 900 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above methods.

[0214] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 904 including instructions, and the above instructions can be executed by a processor 920 of the device 900 to complete the method provided by the technical solution of the present disclosure. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0215] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be essentially or partly contributed to the prior art in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present application or certain parts of the embodiments.

[0216] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can refer to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiment. The system and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0217] The above is a detailed introduction to the scheme provided by the present application. The principles and implementation methods of the present application are described in detail using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for those skilled in the art, according to the idea of ​​the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present application.

Claims

1. A user behavior path mining method, characterized in that: include: Obtaining a character information library, wherein the character information library stores preset characters corresponding to a plurality of operation objects and a plurality of operation behaviors associated with the target application; Obtaining a plurality of nodes to be analyzed, and matching corresponding preset characters from the character information library according to target operation objects and target operation behaviors respectively associated with different nodes to be analyzed, so as to perform character processing on the plurality of nodes to be analyzed, and obtaining node strings respectively associated with the plurality of nodes to be analyzed; Grouping the plurality of node character strings, and identifying the start and end point pairs included in different groups according to the start character and the end character; According to the start-end point pair, a path node sequence is extracted from the corresponding group.

2. The method according to claim 1, characterized in that The characterization processing of the plurality of nodes to be analyzed to obtain node character strings associated with the plurality of nodes to be analyzed includes: The preset characters corresponding to the target operation object associated with the current node to be analyzed and the preset characters corresponding to the target operation behavior associated with the current node to be analyzed are merged, and the character strings are separated by the first identifier to obtain the node character string associated with the current node to be analyzed.

3. The method according to claim 1, characterized in that The grouping of the plurality of node strings comprises: According to the node attributes of the multiple nodes to be analyzed, the node character strings associated with each of the multiple nodes to be analyzed are divided into multiple groups, each group including at least one node character string; According to the operation time corresponding to the operation behaviors associated with the multiple nodes to be analyzed, the node character strings included in each group are sorted and merged, and the character strings are separated by the second identifier.

4. The method according to claim 3, characterized in that The step of identifying the start and end point pairs respectively included in different groups according to the start point character and the end point character includes: Obtaining the sorted and merged node character strings included in the current group, and identifying the starting character position and the ending character position therefrom according to the starting character and the ending character; According to the start-end point pair matching method, a start point separator is inserted before at least one start point character position, and an end point separator is inserted after at least one end point character position, so as to obtain at least one start-end point pair included in the current group.

5. The method according to any one of claims 1 to 4, characterized in that: Also includes: Path screening information is obtained, and a target path matching the path screening information is determined from paths corresponding to different path node sequences.

6. The method according to any one of claims 1 to 4, characterized in that: Also includes: Obtaining node aggregation information, wherein the node aggregation information includes identification information of at least two nodes to be aggregated, and associated aggregation characters; The characterization processing of the plurality of nodes to be analyzed to obtain node character strings associated with the plurality of nodes to be analyzed includes: When it is determined that the current node to be analyzed is the node to be aggregated, character processing is performed on the current node to be analyzed according to the aggregated character, so that the at least two nodes to be aggregated are associated with the same node character string.

7. A user behavior path mining method, characterized in that: include: The client provides a first operation option for submitting a start point node and an end point node; The starting point node and the ending point node are obtained through the first operation option, so as to match the starting point character associated with the starting point node and the ending point character associated with the ending point node from the character information library, and then when a plurality of nodes to be analyzed are obtained, character processing is performed to obtain node character strings associated with each of the plurality of nodes to be analyzed, and the plurality of node character strings are grouped, and the start and end point pairs respectively included in different groups are identified according to the starting point character and the end point character, and the path node sequence is extracted from the corresponding group according to the start and end point pairs, wherein the character information library stores preset characters corresponding to each of the plurality of operation objects and the plurality of operation behaviors associated with the target application; A path mining display page is provided, the page including a first area, wherein the first area is used to display the node transformation process of different path node sequences.

8. The method according to claim 7, characterized in that The page also includes a second area, and the method further includes: Sort the paths corresponding to different path node sequences according to the number of path completions from high to low, and select some paths to be displayed; The relevant information of the path to be displayed is displayed through the second area.

9. The method according to claim 7 or 8, characterized in that: Also includes: Providing a second operation option for submitting the path screening information; The path screening information is obtained through the second operation option, so as to match a target path from paths corresponding to different path node sequences according to the path screening information.

10. The method according to claim 7 or 8, characterized in that: Also includes: Providing a third operation option for submitting node aggregation information, wherein the node aggregation information includes identification information of at least two nodes to be aggregated, and associated aggregation characters; The node aggregation information is obtained through the third operation option, so that during characterization processing, the at least two nodes to be aggregated are associated with the same node string according to the node aggregation information.

11. The method according to claim 7 or 8, characterized in that: Also includes: Providing a fourth operation option for submitting a start-end point pair matching method; The start-end point pair matching method is obtained through the fourth operation option, so as to identify the start-end point pairs included in different groups according to the start-end point pair matching method.

12. A user behavior path analysis method, characterized in that: include: A plurality of paths are obtained and path analysis is performed, wherein the path node sequences corresponding to the plurality of paths are extracted from the grouping of node character strings obtained by characterizing the plurality of nodes to be analyzed according to the starting character and the ending character, wherein the characterization is achieved by matching a character information library, wherein the character information library stores preset characters corresponding to each of a plurality of operation objects and a plurality of operation behaviors associated with the target application.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method described in any one of claims 1 to 12 are implemented.

14. An electronic device, characterized in that: include: one or more processors; as well as A memory associated with the one or more processors, the memory being used to store program instructions, wherein the program instructions, when read and executed by the one or more processors, execute the steps of the method according to any one of claims 1 to 12.

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