User behavior link diagram display method and device, equipment and storage medium

Abstract

The one or more embodiments of the specification provide a user behavior link graph display method, device, electronic equipment and computer readable storage medium. The method comprises: acquiring user behavior link data, and acquiring statistical indicators obtained by statistically processing the user behavior link data; constructing a first user behavior link graph according to the user behavior link data and the statistical indicators; displaying the first user behavior link graph in a display interface; wherein the edges of the links in the first user behavior link graph whose statistical indicators satisfy a preset condition are shorter and / or straighter than the edges of other links. The user can focus on the links whose statistical indicators satisfy the preset condition, so that the user can intuitively and quickly obtain key information from the user behavior link graph.

Description

Technical Field

[0001] This specification relates to the field of computer software technology, and in particular to a method, apparatus, electronic device, and computer-readable storage medium for displaying user behavior chain diagrams. Background Technology

[0002] Data generated during user interaction with a device can be termed user behavior data. User behavior includes, but is not limited to, at least one of the following: accessing, browsing, and operating. Analyzing user behavior data is a crucial method for every application to analyze users. By analyzing the user behavior chain within an application, more valuable information can be extracted; for example, objective user evaluations of the application's content can be obtained, allowing for iterative adjustments to the application's functionality, verification of user stickiness for a particular service segment, and detection of malicious users.

[0003] After identifying user behavior link data, it needs to be displayed to the user. One way to do this is to display it in the form of a link diagram. However, the user behavior link diagram in related technologies has the problem of unreasonable layout, making it difficult for people to observe the link diagram and obtain information. Summary of the Invention

[0004] In view of the above, one or more embodiments of this specification provide a method, apparatus, electronic device, and computer-readable storage medium for displaying user behavior link diagrams.

[0005] To achieve the above objectives, one or more embodiments of this specification provide the following technical solutions: According to a first aspect of one or more embodiments of this specification, a method for displaying a user behavior chain diagram is provided, comprising: Acquire user behavior chain data, and acquire statistical indicators obtained by statistical processing the user behavior chain data; Based on the user behavior link data and the statistical indicators, a first user behavior link diagram is constructed; The first user behavior link graph is displayed in the display interface; wherein, in the first user behavior link graph, the edges of links whose statistical indicators meet preset conditions are shorter than and / or straighter than the edges of other links.

[0006] Optionally, the user behavior link data includes several nodes and parent-child relationships between nodes; any node is used to indicate user behavior generated in the user terminal; The step of constructing a first user behavior link graph based on the user behavior link data and the statistical indicators includes: An initial user behavior link graph is constructed based on several nodes and their parent-child relationships in the user behavior link data; wherein, identical nodes among the several nodes are merged into one node; Using the statistical indicators of the user behavior link data, the display positions of nodes in the initial user behavior link graph are adjusted so that the edges of links in the adjusted first user behavior link graph that meet preset conditions are shorter than and / or straighter than the edges of other links.

[0007] Optionally, in the initial user behavior chain diagram, the nodes are divided into different levels; The step of adjusting the display position of nodes in the initial user behavior link graph using statistical indicators of the user behavior link data includes: Determine the level of the target node in the link where the statistical indicators meet the preset conditions; For each level where the target node is located, the display position of the node in that level is adjusted so that the edges between the target node and nodes in other levels are shorter and / or straighter, with the optimization goal being to make the edges between the target node and nodes in other levels shorter and / or straighter.

[0008] Optionally, the step of constructing an initial user behavior link graph based on several nodes and their parent-child relationships in the user behavior link data includes: Based on several nodes and their parent-child relationships in the user behavior chain data, determine the hierarchy of each node; For each level of nodes, with the goal of reducing the number of intersecting edges in the initial user behavior link graph, the nodes in that level are sorted to determine the display position of the nodes in that level. After determining the display position of the nodes in each level, the display spacing between the nodes is adjusted according to the parent-child relationship of each node to obtain the initial user behavior link diagram.

[0009] Optionally, the statistical indicators include the number of identical nodes in the user behavior link data, the number indicating the frequency of the same user behavior; the preset conditions include a first quantity condition that the number of nodes in the link must satisfy; and / or The statistical indicators include time consumption information from any node to its child nodes; the preset conditions include time consumption conditions that the link consumption must meet; and / or The statistical indicators include the number of each type of link in the user behavior link data; the preset conditions include a second quantity condition that the number of links must meet.

[0010] Optional, also includes: In response to a link addition display command, incremental user behavior link data is acquired, as well as statistical indicators obtained by statistical processing of the incremental user behavior link data; wherein, the link addition display command indicates that other links are added and displayed on the first user behavior link graph; Based on the incremental user behavior link data, the statistical indicators of the incremental user behavior link data, the user behavior link data, the statistical indicators of the user behavior link data, and the first user behavior link diagram, a second user behavior link diagram is constructed; wherein, the first user behavior link diagram is used to enable the user behavior link data to inherit the link layout of the first user behavior link diagram in the second user behavior link diagram.

[0011] Optionally, each node in the first user behavior chain graph corresponds to a heat marker, and the transparency of the heat marker of any node is negatively correlated with the number of times the user behavior indicated by that node occurs; and / or In the first user behavior link graph, each edge corresponds to a time consumption information, which is determined based on the timestamps of the two nodes connected by the edge.

[0012] Optionally, obtaining the user behavior chain data includes: Acquire user behavior data collected from several user terminals, wherein the user behavior data includes several nodes, and any node is used to indicate the user behavior generated by the user in the user terminal; Obtain the pre-specified nodes to be analyzed; the nodes to be analyzed include at least a start node and an end node; Identify user behavior link data from the starting node to the ending node from the user behavior data.

[0013] Optionally, any node carries a timestamp that indicates when the user's action occurred; The step of identifying user behavior link data from the starting node to the ending node from the user behavior data includes: For any user behavior data collected by any user terminal, the session is segmented based on the comparison result of the difference between the timestamps of adjacent nodes and a preset time threshold, and multiple candidate link data are obtained; any candidate link data indicates the set of behaviors generated by the user in the user terminal within a session; the difference between the timestamps of adjacent nodes in the same session does not exceed the preset time threshold. Identify user behavior link data from the starting node to the ending node from all candidate link data of several user terminals.

[0014] Optionally, identifying user behavior link data from the starting node to the ending node from all candidate link data of several user terminals includes: For any candidate link data, the nodes are sequentially labeled according to the timestamps of the nodes in the candidate link data to obtain the first label of the node in the candidate link data. Start and end nodes are selected from the candidate link data, and the start and end nodes are marked to obtain a second mark for the start node and a second mark for the end node; wherein the second mark for the start node and the second mark for the end node satisfy a preset order relationship; and the matching target start node and target end node are determined according to the preset order relationship. Based on the first marker of the node in the candidate link data, extract other nodes between the first marker of the target start node and the first marker of the target end node from the candidate link data.

[0015] Optionally, after identifying the user behavior link data from the start node to the end node from the user behavior data, the method further includes: Identify similar nodes in the user behavior link data and merge them; wherein, the similar nodes indicate that the semantic information of at least two nodes satisfies a similarity threshold and / or at least two nodes are at the same level in the link and are located close to each other in the link; and / or When the user behavior link data includes at least two types of links, similar paths in the user behavior link data are determined and merged; wherein, the similar paths indicate that the difference in the number of nodes of at least two links is less than a preset difference, and the number of identical nodes contained in the at least two links exceeds a preset number.

[0016] Optional, also includes: Data mining and / or behavioral diagnosis are performed based on the user behavior chain data to obtain user behavior analysis results.

[0017] According to a second aspect of one or more embodiments of this specification, a method for displaying a user behavior chain diagram is provided, comprising: In response to a link display command, a first user behavior link diagram is displayed on the display interface; wherein, the first user behavior link diagram is constructed based on the user behavior link data indicated by the link display command and statistical indicators obtained by statistical processing of the user behavior link data, so that the edges of links in the first user behavior link diagram that meet preset conditions are shorter than and / or straighter than the edges of other links.

[0018] According to a third aspect of one or more embodiments of this specification, a user behavior link diagram display device is provided, comprising: The data acquisition module is used to acquire user behavior link data and to acquire statistical indicators obtained by statistical processing of the user behavior link data. The link graph construction module is used to construct a first user behavior link graph based on the user behavior link data and the statistical indicators. The link diagram display module is used to display the first user behavior link diagram in the display interface; wherein, in the first user behavior link diagram, the edges of links whose statistical indicators meet preset conditions are shorter than and / or straighter than the edges of other links.

[0019] According to a fourth aspect of one or more embodiments of this specification, an electronic device is provided, comprising: processor; Memory used to store processor-executable instructions; The processor implements the method as described in any one of the first or second aspects by executing the executable instructions.

[0020] According to a fifth aspect of one or more embodiments of this specification, a computer-readable storage medium is provided that stores computer instructions thereon, which, when executed by a processor, implement the steps of the method as described in any one of the first or second aspects.

[0021] This specification provides one or more embodiments of a user behavior link diagram display method, which can acquire user behavior link data and obtain statistical indicators obtained by statistical processing of the user behavior link data. Then, in the process of constructing a first user behavior link diagram using the user behavior link data, the statistical indicators are referenced to ensure that the edges of links in the first user behavior link diagram that meet preset conditions are shorter than and / or straighter than the edges of other links. When the first user behavior link diagram is displayed on the display interface, the user can focus on the links that meet the preset conditions, making it convenient for the user to intuitively and quickly obtain key information from the user behavior link diagram. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the structure of a display system provided in an exemplary embodiment.

[0023] Figure 2 This is a flowchart illustrating a user behavior chain diagram display method provided in an exemplary embodiment.

[0024] Figure 3 This is a schematic diagram of an initial user behavior link diagram and a first user behavior link diagram provided in an exemplary embodiment.

[0025] Figure 4 This is a schematic diagram of graphic delooping provided in an exemplary embodiment.

[0026] Figure 5 This is a schematic diagram illustrating the introduction of virtual nodes when sorting nodes within the same level, as provided in an exemplary embodiment.

[0027] Figure 6 This is another schematic diagram of node sorting within the same level provided in an exemplary embodiment.

[0028] Figure 7 This is a schematic diagram of a first user behavior chain diagram and statistical indicators provided in an exemplary embodiment.

[0029] Figure 8 This is a schematic diagram of a merge node provided in an exemplary embodiment.

[0030] Figure 9 This is a schematic diagram of the structure of a device provided in an exemplary embodiment.

[0031] Figure 10 This is a block diagram of a user behavior link diagram display device provided in an exemplary embodiment. Detailed Implementation

[0032] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with one or more embodiments of this specification. Rather, they are merely examples of apparatuses and methods consistent with some aspects of one or more embodiments of this specification as detailed in the appended claims.

[0033] It should be noted that the steps of the corresponding methods are not necessarily performed in the order shown and described in this specification in other embodiments. In some other embodiments, the methods may include more or fewer steps than described in this specification. Furthermore, a single step described in this specification may be broken down into multiple steps in other embodiments; and multiple steps described in this specification may be combined into a single step in other embodiments.

[0034] In related technologies, after identifying user behavior link data, it is necessary to display it to the user. One way to do this is to display it in the form of a link diagram. The inventors found that the user behavior link diagram in related technologies is laid out according to the requirements of balance and aesthetics, without taking into account the primary and secondary relationships of each node in the user behavior link diagram in the actual scenario. This results in an unreasonable layout, making it difficult for people to intuitively obtain key information by observing the link diagram.

[0035] To address the problems in related technologies, embodiments of this specification provide a method for displaying a user behavior link diagram. This method acquires user behavior link data and statistical indicators obtained through statistical processing of the user behavior link data. Then, during the construction of a first user behavior link diagram using the user behavior link data, the statistical indicators are referenced to ensure that the edges of links in the first user behavior link diagram whose statistical indicators meet preset conditions are shorter than and / or straighter than the edges of other links. Therefore, when displaying the first user behavior link diagram on the display interface, users can focus on the links whose statistical indicators meet the preset conditions, allowing users to intuitively and quickly obtain key information from the user behavior link diagram.

[0036] For example, please refer to Figure 1 , Figure 1 This specification provides a display system comprising a server 100, a user terminal 200, and an operation terminal 300. Exemplarily, the user terminal 200 is communicatively connected to the server 100, and can access the server 100 via a network to use services provided by the server 100, including but not limited to product delivery services, product purchase services, reading services, audio / video playback services, or search services. Exemplarily, the operation terminal 300 is communicatively connected to the server 100, and is used by application operators to maintain and optimize the services provided by the server 100.

[0037] Server 100 can be a program installed in a background device to provide services to users. For example, such as... Figure 1 As shown, the backend device can be a server, which can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. For example, server 100 can collect user behavior data from several user terminals 200. The user behavior data includes several nodes, any node being used to indicate user behavior generated by the user on the user terminal, and then user behavior link data can be extracted based on the user behavior data.

[0038] Client 200 can be a program installed on a user's device to provide services to the user. Client 200 includes, but is not limited to, applications (APP), web pages, mini-programs, plugins, or components. Figure 1As shown, user devices include, but are not limited to, smartphones, personal digital assistants, tablets, personal computers, laptops, virtual reality terminal devices, and augmented reality terminal devices.

[0039] The "Operation Terminal 300" can be a program installed on the operator's device. The Operation Terminal 300 includes, but is not limited to, applications (APPs), web pages, mini-programs, plugins, or components. For example... Figure 1 As shown, the equipment used by operators includes, but is not limited to, smartphones, personal digital assistants, tablets, personal computers, laptops, virtual reality terminal devices, and augmented reality terminal devices.

[0040] The user behavior link diagram display method provided in this embodiment can be executed by either the server 100 or the operator 300; alternatively, the user behavior link diagram display method can be executed by both the server 100 and the operator 300, with each device executing a portion of it. This embodiment does not impose any limitations on this.

[0041] In one example, the user behavior link diagram display method provided in the embodiments of this specification can be executed independently by the server 100 to obtain the user behavior link diagram and then display it in the interactive interface of the operator 300.

[0042] In another example, server 100 can collect user behavior data from several user terminals 200, extract user behavior link data from the user behavior data, and perform statistical processing on the user behavior link data to obtain statistical indicators. Then, the user behavior link data and the statistical indicators of the user behavior link data are sent to operation terminal 300. Operation terminal 300 constructs a user behavior link diagram based on the received data and displays it.

[0043] The following is an exemplary description of the user behavior link diagram display method provided in the embodiments of this specification: Please refer to Figure 2 , Figure 2 This is a flowchart illustrating a method for displaying a user behavior chain diagram, which can be executed by an electronic device. The method includes: In S101, user behavior link data is acquired, and statistical indicators are obtained by statistical processing of the user behavior link data.

[0044] In S102, a first user behavior link diagram is constructed based on the user behavior link data and the statistical indicators.

[0045] In S103, the first user behavior link diagram is displayed on the display interface; wherein, in the first user behavior link diagram, the edges of links whose statistical indicators meet preset conditions are shorter than and / or straighter than the edges of other links.

[0046] In this embodiment, when constructing the first user behavior link graph, statistical indicators of user behavior link data are referenced, so that the edges of the links whose statistical indicators meet the preset conditions among the at least two links included in the first user behavior link graph are shorter than and / or straighter than the edges of other links; then when the first user behavior link graph is displayed in the display interface, the user can intuitively and quickly focus on the links whose statistical indicators meet the preset conditions, which facilitates the user to quickly obtain key information and improves the efficiency of the user to obtain information from the user behavior link graph.

[0047] In some embodiments, the user behavior link data includes at least one user behavior link data for at least one user, such as one user behavior link data being A→X1→X2→B→C→D, and another user behavior link data being A→X3→X4→B→D. When the user behavior link data includes multiple user behavior link data, some links may be identical, some links may be different, and some links may have partially identical nodes, etc. Therefore, the statistical indicators obtained by statistically processing the user behavior link data may include at least one of the following: the number of link types in the user behavior link data, the time consumption information for each type of link, the number of each type of link, the total number of all links in the user behavior link data, the proportion of each type of link in the total number, the number of identical nodes in the user behavior link data, and the time consumption information from any node in the user behavior link data to its child nodes.

[0048] In this context, any node in a user behavior link data can carry a timestamp, which represents the time when the user behavior indicated by the node occurred. The time taken from any node in the user behavior link data to its child node is the difference between the timestamp of the node and the timestamp of its child node. Assuming a node is A and its child node is B, if multiple user behavior link data include node A and its child node B, then the time taken from node A to its child node B can be a statistical value of the time taken from node A to its child node B in multiple user behavior link data. This statistical value includes, but is not limited to, the average, median, maximum, or minimum value, etc.

[0049] For example, taking the statistical indicator including the number of identical nodes in the user behavior link data (reflecting the number of times the same user behavior occurs) as an example, the preset condition includes a first quantity condition that the number of nodes in the link must meet. For example, the first quantity condition may be that the number of nodes in the link is greater than a preset number, or that there is a node with the largest number in the link, or that the sum of the number of all nodes in the link is the largest, but it is not limited to these, and can be specifically set according to the actual application scenario.

[0050] For example, taking the statistical indicator as an example that includes the time consumption information from any node to its child nodes, the preset conditions include the time consumption conditions that the link's time consumption must meet. For example, the time consumption conditions may be that the total time consumption of all edges in the link is the minimum, or the total time consumption of all edges in the link is less than a preset threshold, or the link includes an edge with the minimum time consumption, or the link includes an edge with a time consumption less than a preset duration, but it is not limited to these, and can be specifically set according to the actual application scenario.

[0051] For example, taking the statistical indicator as including the number of each type of link in the user behavior link data as an example, the preset condition includes a second quantity condition that the number of links must meet. For example, the second quantity condition may be that the number of links is greater than a preset value, or that the number of links is the largest, or that the number of a certain type of link accounts for the largest proportion of the total number of links in all links in the user behavior link data, but it is not limited to these, and can be specifically set according to the actual application scenario.

[0052] In some possible implementations, the user behavior link data includes several nodes and their parent-child relationships. An electronic device can construct an initial user behavior link graph based on these nodes and their parent-child relationships; wherein identical nodes in the user behavior link data are merged into one node; then, using statistical indicators from the user behavior link data, the display positions of the nodes in the initial user behavior link graph are adjusted such that the edges of links in the adjusted first user behavior link graph whose statistical indicators meet preset conditions are shorter than and / or straighter than the edges of other links.

[0053] In one example, taking the statistical metric as an example that includes time consumption information from any node to its child nodes, the preset condition is that the total time consumption of all edges in the link is minimized; please refer to Figure 3 Assuming that we obtain the following... Figure 3 The initial user behavior chain diagram shown ( Figure 3 Following the left figure in the diagram, based on the statistical indicators of the user behavior link data, it can be determined that the total time consumption of the "2-5-6-7" link is minimized. Therefore, the coordinates of nodes 6 and 7 can be adjusted so that the edges of the "2-5-6-7" link are shorter than and / or straighter than the edges of other links, resulting in... Figure 3 The right image in the text.

[0054] In one exemplary embodiment, the process of constructing the initial user behavior link graph involves node layering, node sorting within each layer, and coordinate optimization. First, the electronic device determines the layer of each node based on several nodes in the user behavior link data and their parent-child relationships, wherein identical nodes in the user behavior link data are merged into one node. Next, for each layer of nodes, with the goal of reducing intersecting edges in the initial user behavior link graph, the nodes in that layer are sorted to determine the display position of each node in that layer. After determining the display positions of the nodes in each layer, the display spacing between nodes is adjusted according to the parent-child relationships of each node to obtain a balanced and aesthetically pleasing initial user behavior link graph.

[0055] The node layering process is explained here: The goal of node layering is to arrange all nodes in the directed graph according to a unified edge direction to display a clearer hierarchical structure. The node layering process involves (1) data preprocessing for graph deloop removal; and (2) layer allocation. Initial layers are allocated according to the edge direction, and then the layering result is optimized using the network simplex algorithm.

[0056] For data preprocessing to remove loops in graphs, loop removal is necessary because the presence of loops can lead to variations in the hierarchical results, which is detrimental to hierarchical analysis and can cause instability in the results. Please refer to [link to relevant documentation]. Figure 4 As shown in the diagram, ABCA forms a loop. Therefore, the generated layered graph can have A, B, or C as the top layer, resulting in three different outcomes. As the number of loops increases, the result set may also increase. From a process perspective, loops are detrimental to optimizing the layered results, easily leading to loops during the layering process. From an interactive experience perspective, having different layout results each time results in a poor user experience.

[0057] The loop removal process mainly includes (1) detecting loops and (2) breaking loops. First, detect loops by searching depth-first from any starting point. If a visited node is visited again, it indicates that there is a loop. Second, break loops by reversing the edges in the loops in the graph. In the layout calculation process, since the loops are broken, a fixed layered structure can be generated. In the final rendering process, the reversed edge directions are restored to ensure the accuracy of the information. In the process of breaking loops, edges that exist in multiple loops can be reversed first to minimize the number of reversed edges. When the number of existing loops is equal, in order to ensure the stability of the reversal, the first visited loop edge is reversed first.

[0058] For hierarchy allocation, the electronic device can first assign an initial hierarchy using the longest path algorithm. Then, the network simplex algorithm is used to optimize the hierarchy allocation result. First, the longest path in the graph is found using the longest path algorithm. Then, the ends of the path are aligned, and the hierarchy is calculated by reversing the path, thus generating an initial hierarchy. This initial hierarchy can quickly produce a feasible result, ensuring that all nodes are arranged in different levels according to the direction of the edges. However, it may lead to unnecessary long edges and nodes accumulating at the bottom layer. Therefore, the network simplex algorithm is needed to iteratively cut the edges to generate a more compact graph. Finally, a graph with optimal hierarchy allocation is obtained.

[0059] The following explains the intra-layer sorting process: After obtaining the initial hierarchical data, the nodes within each layer need to be sorted to reduce overlapping edges. Reducing overlaps is usually optimized layer by layer. This step mainly involves two things: cutting long edges and sorting the nodes within each layer.

[0060] Because long edges that span multiple levels will also intersect with nodes within that level. Please refer to [link / reference]. Figure 5 (A) in the diagram illustrates this long-edge intersection problem. As you can see, AL and AE are two long edges. If nodes C and G intersect these two long edges, as shown in the diagram, the long edges need to be cut. Therefore, to ensure that the placement of long edges across levels is considered when sorting nodes within a layer, the graph containing long edges needs to be transformed into a graph where all edges only connect nodes in adjacent levels. This facilitates the optimization of edge intersections.

[0061] To cut long edges that span multiple levels, virtual nodes are introduced and inserted into intermediate levels to reserve space for these cross-level edges, such as... Figure 5 The green node is shown in (B). Then, the edges spanning levels are replaced with multiple unit-length edges connecting the "virtual nodes" (unit length refers to the edge's level being interpolated to 1). Figure 5 In (B), the dashed lines represent the unit-length edges that are cut. It should be noted that these long edges are only cut during the calculation process to facilitate finding the optimal position for drawing the edges. During the drawing process, virtual nodes will serve as the inflection points of these long edges at the corresponding levels, such as... Figure 5 As shown in (C) in the diagram.

[0062] After cutting the long side, to reduce intersecting edges, the nodes within each level need to be sorted. From Figure 6As can be seen in (1), the edges of DB and GH intersect. The edges of FC and HI intersect. Therefore, it is necessary to optimize the order of nodes layer by layer to reduce edge intersections. A heuristic algorithm can be used to gradually optimize edge intersections. This heuristic algorithm sorts the nodes layer by layer from top to bottom to reduce intersections. First, the initial order of nodes in each layer is given; then a series of iterations are performed to try to improve this order; each iteration traverses from the first layer to the last layer, or vice versa. When a certain layer is visited, each vertex in this layer is assigned a weight according to the position of the vertex in the previous layer. Then the vertices in this layer are sorted according to this weight. The sorted node number of this layer is used as the basis for calculating the weight of the next layer of nodes. Finally, the result is as follows: Figure 6 The link graph without cross edges is shown in (2) of the diagram.

[0063] After determining the display positions of nodes in each level, it is necessary to adjust the display spacing between nodes according to the parent-child relationship of each node, based on the principle of balance and aesthetics. For example, an orthogonal layout can be used to calculate node coordinates. The method of orthogonal layout is as follows: (1) Divide the space into regions of equal height along the vertical axis according to the depth of the tree, thereby ensuring the horizontal height and vertical width; (2) Divide the corresponding region into regions of equal width along the horizontal axis according to the number of leaf nodes, and place the node in the center of each region to ensure the balance of the left and right subtrees. After obtaining the coordinates of the points through orthogonal layout, the coordinates of the edges can be generated from the coordinates of the starting node, thus completing all coordinate calculations.

[0064] In an exemplary embodiment, after obtaining the initial behavior link graph, the electronic device can adjust the initial behavior link graph based on the statistical indicators of the user behavior link data. As described above, in the initial user behavior link graph, the nodes are divided into different levels; the electronic device can then determine the level of the target node in the link where the statistical indicators meet preset conditions; for each level where the target node is located, the display position of the node in that level is adjusted with the optimization objective of making the edge between the target node and nodes in other levels shorter and / or straighter. In this embodiment, by adjusting the display position of the node in the level where the target node in the link where the statistical indicators meet preset conditions is located, the edge of the link where the statistical indicators meet preset conditions is shorter and / or straighter than the edge of other links, allowing the user to focus on the links where the statistical indicators meet preset conditions, facilitating the user to intuitively and quickly obtain key information from the user behavior link graph.

[0065] In one example, please refer to Figure 7 , Figure 7 This diagram illustrates a user behavior chain graph constructed based on user behavior chain data, along with statistical indicators obtained through statistical processing of the user behavior chain data. Figure 7 The total number of link completions refers to the total number of links in the user behavior link data; the total number of links refers to the number of link types in the user behavior link data; and the number of link completions refers to the number of each type of link, such as... Figure 7 The list indicates that there are 118 links for #1 and 87 links for #2; the number of links completed / percentage refers to the percentage of each type of link in the total number of links in the user behavior link data; the link duration refers to the sum of the time spent on all edges of that type of link; the number of times in each node in the user behavior link graph refers to the number of times the user behavior indicated by that node has occurred.

[0066] Please refer to Figure 10 In addition to counting from the dimension of the maximum number of completed links (which refers to sorting different types of links in descending order of quantity), statistics can also be collected from other dimensions, such as the minimum number of completed links (which refers to sorting different types of links in ascending order of quantity), the maximum completion time of a link (which refers to sorting different types of links in descending order of link duration), and the minimum completion time of a link (which refers to sorting different types of links in ascending order of link duration), but it is not limited to these.

[0067] Please see Figure 7 In the first user behavior chain graph, each node corresponds to a popularity marker. The transparency of the popularity marker for any node is negatively correlated with the frequency of the user behavior indicated by that node; that is, the more frequent the occurrence, the lower the transparency of the node's popularity marker, and vice versa. This allows users to intuitively understand the popularity of nodes. The frequency of the user behavior indicated by any node can be obtained by counting the number of that node in the user behavior chain data.

[0068] Please see Figure 7 In the first user behavior link graph, each edge corresponds to a time consumption information, which is determined based on the timestamps of the two nodes connected by the edge. If any two nodes connected by an edge appear multiple times in the user behavior link data, the time consumption information of any edge can be determined based on the statistical value (such as the average or median) of the difference between the timestamps of the two nodes connected by the edge. This allows users to intuitively understand the operation time from one node to another.

[0069] Please see Figure 7 It can filter links based on the user's selection, displaying only the links chosen by the user. Figure 7 The system displays the top three links selected by the user based on the number of completions, allowing users to conduct targeted analysis of hot links.

[0070] For example, when a user wants to add other links to the first user behavior link graph, they can select the desired link. The electronic device can then generate a link addition display instruction based on the user's selection, and in response to the instruction, acquire incremental user behavior link data and statistical indicators obtained by statistical processing of the incremental user behavior link data. The link addition display instruction indicates that other links should be added to the first user behavior link graph. Then, based on the incremental user behavior link data, the statistical indicators of the incremental user behavior link data, the user behavior link data, and the first user behavior link graph, a second user behavior link graph is constructed. The first user behavior link graph is used to ensure that the user behavior link data inherits the link layout of the first user behavior link graph in the second user behavior link graph. In this embodiment, when adding other links to the first user behavior link graph, the link layout of the first user behavior link graph can be kept unchanged in the above manner, thereby reducing the user's understanding cost.

[0071] In one example, when constructing the second user behavior link diagram, the electronic device can refer to the hierarchical relationship in the first user behavior link diagram to divide the hierarchy, and when sorting the nodes in the same level, it can refer to the display position of the nodes in each level of the first user behavior link diagram so that the original position of the nodes in the first user behavior link diagram remains unchanged.

[0072] In some embodiments, the user behavior link data can be obtained by: acquiring user behavior data collected from several user terminals, the user behavior data including several nodes, any node being used to indicate user behavior generated by the user on the user terminal; acquiring pre-specified nodes to be analyzed; the nodes to be analyzed including at least a start node and an end node; and identifying user behavior link data from the start node to the end node from the user behavior data. In this embodiment, a task flow to be analyzed, including a start node and an end node, can be pre-specified; thus, in the actual user behavior analysis process, the user behavior link data from the start node to the end node can be identified from the user behavior data collected from several user terminals, thereby focusing on the core links that are of interest to operations personnel.

[0073] In some embodiments, user behavior data can be collected by embedding points in the user's device. User behavior data includes, but is not limited to, access data (such as page visits, page exits, etc.), browsing data (such as page scrolling, page exposure, etc.), and operation data (such as clicks, inputs, long presses, etc.).

[0074] In some embodiments, operators may specify nodes to be analyzed as needed, including at least the start and end nodes of the task flow to be analyzed. In other scenarios, in addition to specifying start and end nodes, users may also specify intermediate nodes in the task flow as needed.

[0075] For example, an electronic device can acquire multiple candidate nodes through recording or search modes and display them on the screen. The user can then select the node to be analyzed from these candidate nodes according to their actual needs. In one example, the node to be analyzed includes a start node and an end node. In another example, the node to be branched includes a start node, at least one intermediate node, and an end node.

[0076] The recording mode instructs the recording of user behavior on the target device to obtain multiple candidate nodes. The search mode instructs the search of a specified page to obtain multiple candidate nodes on the specified page. Of course, in addition to the above two methods, users can also manually add nodes to be analyzed according to actual needs, and this embodiment does not impose any restrictions on this.

[0077] In some embodiments, users can also pre-set filtering conditions according to actual needs. These filtering conditions include, but are not limited to, the time range of the occurrence of user behavior data, the user or user type to which the user behavior data belongs, and the range of the collection location of the user behavior data. The electronic device can select user behavior data that meets the filtering conditions from the user behavior data, and then identify user behavior link data from the starting node to the ending node from the user behavior data that meets the filtering conditions.

[0078] For example, taking the node to be analyzed as including a start node and an end node, for user behavior data collected by any user terminal, the electronic device can segment the session based on the comparison result of the difference in timestamps between adjacent nodes and a preset time threshold, and obtain multiple candidate link data; any candidate link data indicates the set of behaviors generated by the user in the user terminal within a session; the difference in timestamps between adjacent nodes in the same session does not exceed the preset time threshold; thus, the electronic device can identify the user behavior link data from the start node to the end node from all candidate link data of several user terminals. In this embodiment, user behavior data is segmented on a session-by-session basis to realize the analysis of user behavior links within a specified time range, thereby ensuring the accuracy of subsequent analysis results.

[0079] For any candidate link data, the electronic device sequentially tags the nodes according to their timestamps to obtain a first tag for each node. Then, it filters out the start and end nodes from the candidate link data and sequentially tags them to obtain a second tag for each node. The second tags of the start and end nodes satisfy a preset order relationship. Next, it determines the matching target start and target end nodes based on this preset order relationship. Finally, based on the first tags of the nodes in the candidate link data, it extracts other nodes between the first tags of the target start and target end nodes from the candidate link data.

[0080] In one example, using one candidate link data point, assuming the candidate link data is A→X1→X2→X3→B→C→A→…, with the starting node being A and the ending node being B. The electronic device sequentially tags the nodes in the candidate link data based on their timestamps, as shown in Table 1, which illustrates the first tag of each node in the candidate link data.

[0081] Table 1 Then, starting node A and ending node B are selected from the candidate link data, as shown in Table 2. The second mark of starting node A is set to 1 and the second mark of ending node B is set to 2. It can be seen that the preset order relationship between the second mark of the starting node and the second mark of the ending node is: the second mark of starting node A plus 1 equals the second mark of ending node B.

[0082] Table 2 The electronic device determines whether the second mark of each row in Table 2 plus 1 equals the second mark of the next row. If so, the target start node and target end node can be determined. As shown in Table 3, the target end node can be marked with a comma.

[0083] Table 3 Next, by converting multiple rows to one row, Table 3 is transformed into the style shown in Table 4, and adjacent nodes are linked with commas. The data of completed links will then have two comma markers. Please refer to Table 5. Then, by converting one row to multiple rows, the completed links (links from A to B) and incomplete links in the candidate link data are separated. Then, based on the first marker of the target start node and the first marker of the target end node in the completed link, other nodes can be determined. For example, in the first row of Table 5, the first marker of the start node A is 1, and the first marker of the end node B is 5. According to the order of the first markers, the three nodes with the first markers of 2, 3 and 4 in Table 1 can be extracted, thus obtaining the user behavior link data {A→X1→X2→X3→B}.

[0084] Table 4 Table 5 In some embodiments, after acquiring user behavior data, the complexity of user behavior can lead to overly scattered identified links, making it difficult to extract effective data or yielding unclear diagnostic results. Therefore, after acquiring user behavior link data from the starting node to the ending node, nodes or links can be merged based on link similarity, resulting in more convergent user links.

[0085] In one possible implementation, similar nodes in the user behavior link data can be identified and merged. The similar nodes indicate that the semantic information of at least two nodes satisfies a similarity threshold and / or that at least two nodes are at the same level in the link and are close in position. For example, if there are two nodes, one indicating the user behavior of adding a "hamburger" to the shopping cart, and the other indicating the user behavior of adding "fries" to the shopping cart, and both "hamburger" and "fries" are food items, these two nodes can be merged. For instance, if the journey from node A to node B can pass through either node X1 or node X2 (i.e., A→X1→B, A→X2→B), then nodes X1 and X2 are at the same level in the link and are close in position, so nodes X1 and X2 can be merged into one node.

[0086] In another example, see Figure 8As shown in the left image, users may perform multiple actions on the same page, resulting in a relatively scattered identification of the links. Therefore, similar nodes within the links can be identified. For example, the semantic information of the three nodes under "Select Activity Group" can be categorized as "Activity Group," so these three nodes can be merged. Similarly, the two nodes under "Select Discount Type" are at the same level and are close in position within the link, so these two nodes can be merged into a single node. After merging all similar nodes, we can obtain... Figure 8 The user link in the right figure shows that merging nodes with personalized semantics and / or nodes at the same level and in close proximity makes the merged user link more convergent, facilitating problem localization. In another possible implementation, when the user behavior link data includes at least two types of links, the electronic device can determine similar paths in the user behavior link data and merge these similar paths. The similar paths indicate that the difference in the number of nodes between at least two links is less than a preset difference, and the number of identical nodes in the at least two links exceeds a preset number. It is understood that the specific values ​​of the preset difference and preset number can be set according to the actual application scenario, and this embodiment does not impose any restrictions on them. In one example, consider a user behavior link: A→X1→X2→X3→B; another user behavior link: A→X1→X3→X2→B. Since these two user behavior links contain the same number of nodes and the same nodes, they can be merged.

[0087] In some embodiments, after identifying user behavior link data, the electronic device can perform data mining and / or behavior diagnosis based on the user behavior link data to obtain user behavior analysis results.

[0088] In an exemplary embodiment, in terms of data mining, the electronic device can perform statistical processing on the user behavior link data to obtain statistical indicators corresponding to the user behavior link data. For example, the statistical indicators include at least one of the following: the number of link types in the user behavior link data, the time consumption information of each link, the number of each type of link, the total number of all links in the user behavior link data, the proportion of each type of link in the total number, the number of identical nodes in the user behavior link data, and the time consumption information from any node to its child nodes in the user behavior link data; wherein, the number of identical nodes in the user behavior link data indicates the number of times the same user behavior occurs.

[0089] In another exemplary embodiment, in terms of behavior diagnosis, the electronic device can use reference link data to determine user operation in the user behavior link data, identify abnormal link data in the user behavior link data, and then perform statistical processing on the abnormal link data to obtain user behavior analysis results.

[0090] In one possible implementation, matching can be performed using an event stream approach to identify abnormal link data within the user behavior link data. To match user actions, multiple atomic patterns can be defined to describe the behavior. The reference link data includes reference behavior patterns. The user behavior link data can be transformed into an event stream composed of behavior patterns, which indicate the connection relationships between nodes in the user behavior link data, according to predefined atomic patterns used to describe the behavior. Then, the behavior patterns in the event stream are matched against the reference behavior patterns to determine abnormal link data within the user behavior link data based on the matching results.

[0091] In another possible implementation, a graph pattern can be used for matching to identify abnormal link data in the user behavior link data. The reference link data includes a reference link graph. The electronic device can construct a user behavior link graph based on the user behavior link data; then, it can match the user behavior link graph with the reference link graph to determine abnormal link data in the user behavior link data based on the matching result.

[0092] For example, the abnormal link data in the user behavior link data includes at least one of the following: detour link data, operation branch data, and operation-obstructed link data; wherein, an operation-obstructed link refers to a user behavior that should have occurred at a certain node, but that user node did not occur, and therefore the node was not generated; for example, in the payment process, the user should have clicked the payment control at the end, but did not. The electronic device can then perform statistical processing based on the detour link data to obtain the detour degree of the detour link; perform statistical processing based on the operation branch data to obtain the dispersion of the operation branch data; and perform statistical processing based on the operation-obstructed link data to obtain the obstruction distance of the operation-obstructed link data.

[0093] For example, in terms of behavior diagnosis, the electronic device can also perform statistical processing on the user behavior link data to obtain statistical indicators corresponding to the user behavior link data. The specific content of the statistical indicators can be found in the above description. The electronic device then compares the statistical indicators with reference indicators to identify abnormal indicators among the statistical indicators.

[0094] In one exemplary application scenario, the user behavior analysis results can be used to improve the interaction process on the user's end, or to make data recommendations to users, or to identify malicious users, etc.

[0095] The various technical features in the above embodiments can be combined arbitrarily, as long as there is no conflict or contradiction between the combinations of features. However, due to space limitations, they are not described one by one. Therefore, the arbitrary combination of various technical features in the above embodiments is also within the scope of this specification.

[0096] Accordingly, embodiments of this specification also provide a method for displaying a user behavior link diagram, comprising: displaying a first user behavior link diagram in a display interface in response to a link display instruction; wherein the first user behavior link diagram is constructed based on user behavior link data indicated by the link display instruction and statistical indicators obtained by statistical processing of the user behavior link data, such that the edges of links in the first user behavior link diagram whose statistical indicators meet preset conditions are shorter than and / or straighter than the edges of other links.

[0097] For details on the specific implementation process, please refer to the implementation process of the corresponding steps in the above method, which will not be repeated here.

[0098] Figure 9 This is a schematic structural diagram of a device provided in an exemplary embodiment. Please refer to... Figure 9 At the hardware level, the device includes a processor 202, an internal bus 204, a network interface 206, memory 208, and non-volatile memory 210, and may also include other hardware required for business operations. One or more embodiments of this specification can be implemented in software, such as the processor 202 reading the corresponding computer program from the non-volatile memory 210 into memory 208 and then running it. Of course, in addition to software implementation, one or more embodiments of this specification do not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0099] Please refer to Figure 10 User behavior link diagram display devices can be applied to, for example, Figure 9 The device shown implements the technical solution described in this specification. The user behavior link diagram display device may include: The data acquisition module 301 is used to acquire user behavior link data and to acquire statistical indicators obtained by statistical processing of the user behavior link data. Link graph construction module 302 is used to construct a first user behavior link graph based on the user behavior link data and the statistical indicators; The link diagram display module 303 is used to display the first user behavior link diagram in the display interface; wherein, in the first user behavior link diagram, the edges of links whose statistical indicators meet preset conditions are shorter than and / or straighter than the edges of other links.

[0100] In some embodiments, the user behavior link data includes a plurality of nodes and parent-child relationships between nodes; any node is used to indicate user behavior generated in the user terminal. The link graph construction module 302 is specifically used to: construct an initial user behavior link graph based on the plurality of nodes and their parent-child relationships in the user behavior link data; wherein, identical nodes among the plurality of nodes are merged into one node; and adjust the display position of nodes in the initial user behavior link graph using statistical indicators of the user behavior link data, such that the edges of links in the adjusted first user behavior link graph whose statistical indicators meet preset conditions are shorter than and / or straighter than the edges of other links.

[0101] In some embodiments, in the initial user behavior link graph, the plurality of nodes are divided into different levels. The link graph construction module 302 is specifically used to: determine the level of the target node in the link where the statistical indicators meet preset conditions; for each level where the target node is located, adjust the display position of the node in that level with the optimization goal of making the edge between the target node and the nodes in other levels shorter and / or straighter.

[0102] In some embodiments, the link graph construction module 302 is specifically used to: determine the level of each node based on several nodes in the user behavior link data and their parent-child relationships; for each node in each level, sort the nodes in that level with the goal of reducing the intersection edges in the initial user behavior link graph, and determine the display position of the nodes in that level; after determining the display position of the nodes in each level, adjust the display spacing between the nodes according to the parent-child relationships of each node to obtain the initial user behavior link graph.

[0103] In some embodiments, the statistical indicator includes the number of identical nodes in the user behavior link data, the number indicating the number of times the same user behavior occurs; the preset condition includes a first quantity condition that the number of nodes in the link must satisfy; and / or the statistical indicator includes time consumption information from any node to its child nodes; the preset condition includes a time consumption condition that the link consumption must satisfy; and / or the statistical indicator includes the number of each type of link in the user behavior link data; the preset condition includes a second quantity condition that the number of links must satisfy.

[0104] In some embodiments, the link addition module is configured to, in response to a link addition display instruction, acquire incremental user behavior link data and acquire statistical indicators obtained by statistical processing the incremental user behavior link data; wherein, the link addition display instruction indicates that other links be added and displayed on the first user behavior link diagram; and construct a second user behavior link diagram based on the incremental user behavior link data, the statistical indicators of the incremental user behavior link data, the user behavior link data, the statistical indicators of the user behavior link data, and the first user behavior link diagram; wherein, the first user behavior link diagram is used to enable the user behavior link data to inherit the link layout of the first user behavior link diagram in the second user behavior link diagram.

[0105] In some embodiments, each node in the first user behavior link graph corresponds to a heat mark, and the transparency of the heat mark of any node is negatively correlated with the number of times the user behavior indicated by the node occurs; and / or an edge in the first user behavior link graph corresponds to a time consumption information, and the time consumption information is determined based on the timestamps of the two nodes connected by the edge.

[0106] In some embodiments, the system further includes: a link identification module, configured to acquire user behavior data collected by several user terminals, the user behavior data including several nodes, any node being used to indicate user behavior generated by a user in the user terminal; acquire a pre-specified node to be analyzed; the node to be analyzed includes at least a start node and an end node; and identify user behavior link data from the start node to the end node from the user behavior data.

[0107] In some embodiments, any node carries a timestamp that indicates when the user's action occurred.

[0108] The link identification module is specifically used for: for user behavior data collected by any user terminal, performing session segmentation based on the comparison result of the difference between the timestamps of adjacent nodes and a preset time threshold, and obtaining multiple candidate link data; any candidate link data indicates the set of behaviors generated by the user in the user terminal within a session; the difference between the timestamps of adjacent nodes in the same session does not exceed the preset time threshold; and identifying user behavior link data from the starting node to the ending node from all candidate link data of several user terminals.

[0109] In some embodiments, the link identification module is specifically used for: for any candidate link data, sequentially labeling nodes according to the timestamps of the nodes in the candidate link data to obtain a first label of the nodes in the candidate link data; filtering out a start node and an end node from the candidate link data, and labeling the start node and the end node respectively to obtain a second label of the start node and a second label of the end node; wherein the second label of the start node and the second label of the end node satisfy a preset order relationship; determining a matching target start node and a target end node according to the preset order relationship; and extracting other nodes between the first label of the target start node and the first label of the target end node from the candidate link data according to the first label of the nodes in the candidate link data.

[0110] In some embodiments, the system further includes: a link merging module, configured to determine similar nodes in the user behavior link data and merge the similar nodes in the user behavior link data; wherein the similar nodes indicate that the semantic information of at least two nodes satisfies a similarity threshold and / or at least two nodes are at the same level in the link and are located close to each other in the link; and / or, when the user behavior link data includes at least two types of links, to determine similar paths in the user behavior link data and merge the similar paths in the user behavior link data; wherein the similar paths indicate that the difference in the number of nodes of at least two links is less than a preset difference, and the at least two links contain more than a preset number of identical nodes.

[0111] In some embodiments, the system further includes a user behavior analysis module, configured to perform data mining and / or behavior diagnosis based on the user behavior link data to obtain user behavior analysis results.

[0112] The specific implementation process of the functions and roles of each module in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0113] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0114] Accordingly, embodiments of this specification also provide an electronic device, including a processor; Memory used to store processor-executable instructions; The processor implements the method described in any one of the above statements by executing the executable instructions.

[0115] Accordingly, this disclosure also provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of any of the methods described above.

[0116] 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 disclosure are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0117] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer, which can take the form of a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email sending and receiving device, game console, tablet computer, wearable device, or any combination of these devices.

[0118] In a typical configuration, a computer includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0119] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0120] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0121] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0122] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0123] The terminology used in one or more embodiments of this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of one or more embodiments of this specification. The singular forms “a,” “described,” and “the” used in one or more embodiments of this specification and in the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0124] It should be understood that although the terms first, second, third, etc., may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first information may also be referred to as second information without departing from the scope of one or more embodiments of this specification, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "in response to a determination," or "when," or "in the event of a determination."

[0125] The above description is merely a preferred embodiment of one or more embodiments of this specification and is not intended to limit the scope of one or more embodiments of this specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of this specification should be included within the protection scope of one or more embodiments of this specification.

Claims

1. A method for displaying a user behavior chain diagram, comprising: Acquire user behavior chain data, and acquire statistical indicators obtained by statistical processing the user behavior chain data; The user behavior link data includes several nodes and the parent-child relationship between the nodes; any node is used to indicate the user behavior generated in the user terminal. Based on several nodes and their parent-child relationships in the user behavior chain data, determine the hierarchy of each node; For long edges that span multiple levels, virtual nodes are set and inserted into the intermediate levels that the long edge crosses, and the long edge is replaced with multiple unit-length edges connecting the virtual nodes; wherein, during the drawing process, the virtual nodes will serve as the inflection points of the long edge at the corresponding level. For each level of nodes, with the goal of reducing the number of intersecting edges in the initial user behavior link graph, the nodes in that level are sorted to determine the display position of the nodes in that level. After determining the display position of the nodes in each level, the display spacing between the nodes is adjusted according to the parent-child relationship of each node to obtain the initial user behavior link graph. Among these nodes, identical nodes are merged into one node. Determine the level of the target node in the link where the statistical indicators meet the preset conditions; For each level where the target node is located, the display position of the node in that level is adjusted to obtain the first user behavior link graph, with the goal of making the edge between the target node and nodes in other levels shorter and / or straighter. The first user behavior link graph is displayed in the display interface; wherein, in the first user behavior link graph, the edges of links whose statistical indicators meet preset conditions are shorter than and / or straighter than the edges of other links.

2. The method according to claim 1, before determining the hierarchy of each node based on several nodes and their parent-child relationships in the user behavior link data, further includes: A depth-first search is performed on the nodes in the user behavior chain data from any starting point. If a visited node is visited again, a loop is determined to exist. A specified edge in the loop is reversed, and the specified edge includes at least one of the following: an edge that exists in multiple loops simultaneously; or, if the number of existing loops is equal, the edge of the first loop visited is reversed first; wherein, the reversed edge direction of the specified edge is restored during the final rendering process.

3. The method according to claim 1, wherein determining the hierarchy of each node based on a plurality of nodes and their parent-child relationships in the user behavior link data includes: Find the longest path in the user behavior chain data, and calculate the hierarchy by reversing the longest path to determine the hierarchy of each node.

4. The method according to claim 1, wherein adjusting the display spacing between nodes according to the parent-child relationship of each node to obtain the initial user behavior link graph includes: An orthogonal layout is adopted, and the display spacing between nodes is adjusted according to the parent-child relationship of each node to obtain the initial user behavior link diagram; wherein, the orthogonal layout includes: dividing the space into regions of equal height along the vertical axis according to the depth of the tree; dividing the corresponding region into regions of equal width along the horizontal axis according to the number of leaf nodes, and placing the nodes at the center of each region.

5. The method according to any one of claims 1 to 4, wherein the statistical indicator includes the number of identical nodes in the user behavior link data, the number indicating the number of times the same user behavior occurs; the preset condition includes a first quantity condition that the number of nodes in the link must satisfy; and / or The statistical indicators include time consumption information from any node to its child nodes; the preset conditions include time consumption conditions that the link consumption time must meet. and / or The statistical indicators include the number of each type of link in the user behavior link data; the preset conditions include a second quantity condition that the number of links must meet.

6. The method according to claim 1, further comprising: In response to a link addition display command, incremental user behavior link data is acquired, as well as statistical indicators obtained by statistical processing of the incremental user behavior link data; wherein, the link addition display command indicates that other links are added and displayed on the first user behavior link graph; Based on the incremental user behavior link data, the statistical indicators of the incremental user behavior link data, the user behavior link data, the statistical indicators of the user behavior link data, and the first user behavior link diagram, a second user behavior link diagram is constructed; wherein, the first user behavior link diagram is used to enable the user behavior link data to inherit the link layout of the first user behavior link diagram in the second user behavior link diagram.

7. The method according to claim 1, wherein each node in the first user behavior link graph corresponds to a heat mark, and the transparency of the heat mark of any node is negatively correlated with the number of occurrences of the user behavior indicated by that node; and / or In the first user behavior link graph, each edge corresponds to a time consumption information, which is determined based on the timestamps of the two nodes connected by the edge.

8. The method according to claim 1, wherein obtaining the user behavior link data includes: Acquire user behavior data collected from several user terminals, wherein the user behavior data includes several nodes, and any node is used to indicate the user behavior generated by the user in the user terminal; Obtain the pre-specified nodes to be analyzed; the nodes to be analyzed include at least a start node and an end node; Identify user behavior link data from the starting node to the ending node from the user behavior data.

9. The method according to claim 8, wherein any node carries a timestamp representing the time when the user action occurred; The step of identifying user behavior link data from the starting node to the ending node from the user behavior data includes: For any user behavior data collected by any user terminal, the session is segmented based on the comparison result of the difference between the timestamps of adjacent nodes and a preset time threshold, and multiple candidate link data are obtained; any candidate link data indicates the set of behaviors generated by the user in the user terminal within a session; the difference between the timestamps of adjacent nodes in the same session does not exceed the preset time threshold. Identify user behavior link data from the starting node to the ending node from all candidate link data of several user terminals.

10. The method according to claim 9, wherein identifying user behavior link data from the starting node to the ending node from all candidate link data of a plurality of user terminals comprises: For any candidate link data, the nodes are sequentially labeled according to the timestamps of the nodes in the candidate link data to obtain the first label of the node in the candidate link data. Start and end nodes are selected from the candidate link data, and the start and end nodes are marked to obtain a second mark for the start node and a second mark for the end node; wherein the second mark for the start node and the second mark for the end node satisfy a preset order relationship; and the matching target start node and target end node are determined according to the preset order relationship. Based on the first marker of the node in the candidate link data, extract other nodes between the first marker of the target start node and the first marker of the target end node from the candidate link data.

11. The method of claim 8, further comprising, after identifying user behavior link data from the user behavior data from the starting node to the ending node: Identify similar nodes in the user behavior link data and merge them; wherein, the similar nodes indicate that the semantic information of at least two nodes satisfies a similarity threshold and / or at least two nodes are at the same level in the link and are located close to each other in the link; and / or When the user behavior link data includes at least two types of links, similar paths in the user behavior link data are determined and merged; wherein, the similar paths indicate that the difference in the number of nodes of at least two links is less than a preset difference, and the number of identical nodes contained in the at least two links exceeds a preset number.

12. The method according to claim 1, further comprising: Data mining and / or behavioral diagnosis are performed based on the user behavior chain data to obtain user behavior analysis results.

13. The method according to claim 12, wherein the step of performing data mining and / or behavior diagnosis based on the user behavior link data to obtain user behavior analysis results includes: Statistical processing is performed on the user behavior link data to obtain statistical indicators of the user behavior link data. The statistical indicators are compared with reference indicators to identify abnormal indicators in the statistical indicators. And / or, using reference link data to determine user operations in the user behavior link data, identifying abnormal link data in the user behavior link data, performing statistical processing on the abnormal link data, and obtaining user behavior analysis results.

14. The method of claim 13, wherein the reference link data includes a reference behavior pattern; The step of using reference link data to determine user operation in the user behavior link data, and identifying abnormal link data in the user behavior link data, includes: The user behavior chain data is transformed into an event stream composed of behavior patterns, wherein the behavior patterns are used to indicate the connection relationship between nodes in the user behavior chain data; The behavior patterns in the event stream are matched with the reference behavior patterns to determine abnormal link data in the user behavior link data based on the matching results.

15. The method according to claim 13, wherein the reference link data includes a reference link diagram; The step of using reference link data to determine user operation in the user behavior link data, and identifying abnormal link data in the user behavior link data, includes: Construct a user behavior link graph based on the aforementioned user behavior link data; The user behavior link graph is matched with the reference link graph to determine abnormal link data in the user behavior link data based on the matching result; wherein, the abnormal link data includes at least one of the following: detour link data, operation branch data, and operation obstruction link data.

16. A user behavior chain diagram display device, comprising: The data acquisition module is used to acquire user behavior link data and to acquire statistical indicators obtained by statistical processing of the user behavior link data; the user behavior link data includes several nodes and the parent-child relationship of the nodes; any node is used to indicate the user behavior generated in the user terminal; The link graph construction module is used to determine the hierarchy of each node based on several nodes and their parent-child relationships in the user behavior link data. For long edges that span multiple levels, virtual nodes are inserted into the intermediate levels crossed by the long edge, replacing the long edge with multiple unit-length edges connecting the virtual nodes. During the drawing process, these virtual nodes serve as inflection points of the long edge at the corresponding level. For nodes in each level, with the goal of reducing intersecting edges in the initial user behavior link diagram, the nodes in that level are sorted to determine their display positions. After determining the display positions of the nodes in each level, the display spacing between nodes is adjusted according to their parent-child relationships to obtain the initial user behavior link diagram. Identical nodes among these nodes are merged into one node. The level of the target node in the link that satisfies preset statistical indicators is determined. For each level of the target node, with the optimization goal of making the edges between the target node and nodes in other levels shorter and / or straighter, the display positions of the nodes in that level are adjusted to obtain the first user behavior link diagram. The link diagram display module is used to display the first user behavior link diagram in the display interface; wherein, in the first user behavior link diagram, the edges of links whose statistical indicators meet preset conditions are shorter than and / or straighter than the edges of other links.

17. An electronic device comprising: processor; Memory used to store processor-executable instructions; The processor implements the method as described in any one of claims 1 to 15 by executing the executable instructions.

18. A computer-readable storage medium having stored thereon computer instructions that, when executed by a processor, implement the steps of the method as claimed in any one of claims 1 to 15.

Images