Method for determining a behavior model, method for determining an event, medium, device and product

Abstract

The embodiment of the specification provides a behavior model determination method, an event determination method, a behavior model determination device, an event determination device, a computer readable storage medium, an electronic device and a computer program product, and the scheme comprises the following steps: determining a plurality of nodes with a sequence relationship according to a behavior characteristic sequence of a preset event, and determining a moving line between adjacent nodes satisfying the sequence relationship, wherein the node corresponds to at least one matching condition, the matching condition comprises a matching feature, a matching mode and a matching value, the moving line comprises a restriction condition to be satisfied between the adjacent nodes, and further, the behavior model of the preset event can be determined according to the node and the moving line. The behavior model composed of the above-mentioned node and the moving line can be used for comparing with operation data of a user, so as to determine whether the operation data of the user conforms to the behavior characteristic sequence corresponding to the behavior model, and further determine whether the operation data corresponds to the above-mentioned preset event.

Description

Technical Field

[0001] This specification relates to the field of data processing technology, and in particular to a method for determining a behavior model, a method for determining an event, a device for determining a behavior model, a device for determining an event, a computer-readable storage medium, an electronic device, and a computer program product. Background Technology

[0002] In the context of internet platformization, relevant workers (such as customer service representatives) receive requests for help or complaints from internet users. For example, if a user encounters fraud while shopping online, they might request help via telephone or online chat. In this scenario, when customer service representatives receive such requests, they often can only understand the user's situation through verbal or written descriptions. However, this method heavily relies on the user's good memory and expressive abilities, which is not conducive to accurately identifying the specific incident and thus failing to precisely address the user's needs.

[0003] Therefore, there is an urgent need in related technologies for a solution that can accurately determine the events encountered by users.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this specification, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] This specification provides a method for determining a behavior model, a method for determining an event, a device for determining a behavior model, a device for determining an event, a computer-readable storage medium, an electronic device, and a computer program product, which can provide a solution for accurately determining events encountered by a user.

[0006] Other features and advantages of this specification will become apparent from the following detailed description, or may be learned in part by practice of this specification.

[0007] According to one aspect of the embodiments of this specification, a method for determining a behavior model is provided. The method includes: determining a sequence of behavior features related to a preset event; determining a plurality of nodes having a sequence relationship based on the sequence of behavior features, and determining a movement path between adjacent nodes that satisfy the sequence relationship, wherein the nodes correspond to at least one matching condition, the matching condition includes a matching feature, a matching method, and a matching value, and the movement path includes a constraint condition that must be satisfied between the adjacent nodes; and determining a behavior model of the preset event based on the nodes and the movement path.

[0008] According to another aspect of the embodiments of this specification, a method for determining an event is also provided. The method includes: obtaining an identity identifier of a target user, and obtaining operation data of the target user during a target time period based on the identity identifier; comparing the operation data with a behavior model, wherein the behavior model is a model determined by the method of the behavior model described above; and determining the event corresponding to the target user during the target time period based on the behavior model when the comparison result meets preset conditions.

[0009] In an exemplary embodiment, based on the above scheme, comparing the above operation data with the behavior model includes: determining a target node group in the behavior model, wherein the target node group includes a first target node and a second target node that are adjacent based on sequence relationship; comparing the above operation data with the matching conditions corresponding to the first target node and the second target node respectively; and comparing the above operation data with a target movement line when the above operation data satisfies the matching conditions corresponding to the first target node and the second target node respectively, wherein the target movement line is the movement line between the first target node and the second target node.

[0010] In an exemplary embodiment, based on the above scheme, comparing the above operation data with the matching conditions corresponding to the first target node and the second target node respectively includes: comparing the above operation data with the first matching condition corresponding to the first target node; and if the above operation data satisfies the first matching condition, comparing the above operation data with the second matching condition corresponding to the second target node.

[0011] The above-mentioned comparison of the operation data with the target movement line when the operation data satisfies the matching conditions corresponding to the first target node and the second target node respectively includes: comparing the operation data with the target movement line when the operation data satisfies the first matching condition and the second matching condition respectively.

[0012] In an exemplary embodiment, based on the above scheme, the method for determining the above event further includes: when the above operation data satisfies the above target movement line, determining the next node adjacent to the above second target node in the above behavior model based on the above sequence relationship, obtaining the updated second target node, and performing the above step of comparing the above operation data with the second matching condition corresponding to the above second target node.

[0013] In an exemplary embodiment, based on the above scheme, the method for determining the above event further includes: if the above operation data satisfies the above target movement line, and the second target node is the last node in the above behavior model, then the comparison result is determined to satisfy the above preset conditions.

[0014] In an exemplary embodiment, based on the above scheme, the comparison of the above operation data with the behavior model includes: determining a thread for each of the above behavior models, and comparing the above operation data with the multiple above behavior models in parallel through multiple threads;

[0015] The above-mentioned determination of the events corresponding to the target user in the target time period based on the behavior model when the comparison results meet the preset conditions includes: obtaining multiple target behavior models corresponding to the comparison results that meet the preset conditions; determining the events corresponding to the target user in the target time period based on the priorities of the multiple target behavior models; or, performing aggregation processing based on the aggregation coefficients of the multiple target behavior models to obtain the events corresponding to the target user in the target time period.

[0016] In an exemplary embodiment, based on the above scheme, the behavior model is encapsulated as a diagnostic service; after obtaining the target user's operation data in the target time period based on the identity identifier, the method for determining the event further includes: calling the diagnostic service to compare the operation data with the behavior model provided by the diagnostic service.

[0017] According to another aspect of the embodiments of this specification, a behavior model determination apparatus is provided, the apparatus comprising: a first determination module, a second determination module, and a third determination module.

[0018] The first determining module is used to determine a sequence of behavioral features related to a preset event; the second determining module is used to determine multiple nodes with a sequence relationship based on the behavioral feature sequence, and to determine the movement path between adjacent nodes that satisfy the sequence relationship, wherein each node corresponds to at least one matching condition, the matching condition includes a matching feature, a matching method, and a matching value, and the movement path includes the constraints that must be satisfied between the adjacent nodes; the third determining module is used to determine the behavioral model of the preset event based on the nodes and the movement path.

[0019] According to another aspect of the embodiments of this specification, an event determination apparatus is provided, the apparatus comprising: an acquisition module, a comparison module, and a determination module.

[0020] The acquisition module is used to acquire the identity identifier of the target user and acquire the operation data of the target user in the target time period based on the identity identifier; the comparison module is used to compare the operation data with the behavior model, wherein the behavior model is determined according to the behavior model determination method provided above; the determination module determines, if the comparison result meets the preset conditions, the event corresponding to the target user in the target time period based on the behavior model.

[0021] According to another aspect of the embodiments of this specification, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method for determining a behavior model as described in the above embodiments, or when it executes the computer program, it implements the method for determining an event as described in the above embodiments.

[0022] According to one aspect of an embodiment of this specification, a computer-readable storage medium is provided, wherein instructions are stored therein, which, when executed on a computer or processor, cause the computer or processor to perform a method for determining a behavior model as described in the above embodiments, or cause the computer or processor to perform a method for determining an event as described in the above embodiments.

[0023] According to another aspect of the embodiments of this specification, a computer program product comprising instructions is provided that, when the computer program product is run on a computer or processor, causes the computer or processor to perform a method for determining a behavior model as described in the above embodiments, or causes the computer or processor to perform a method for determining an event as described in the above embodiments.

[0024] The methods for determining behavior models, methods for determining events, apparatus for determining behavior models, apparatus for determining events, computer-readable storage media, electronic devices, and computer program products provided in the embodiments of this specification have the following technical effects:

[0025] In the solution provided by the embodiments of this specification, a sequence of behavioral features related to a preset event is determined. Then, multiple nodes with sequential relationships are determined based on the behavioral feature sequence, and movement paths between adjacent nodes that satisfy the aforementioned sequential relationships are determined. Each node corresponds to at least one matching condition, which includes matching features, matching methods, and matching values. Movement paths include constraints that must be satisfied between adjacent nodes. Furthermore, based on the nodes and movement paths, a behavioral model for the preset event can be determined. By processing the behavioral feature sequence into nodes and movement paths for matching, the behavioral model composed of these nodes and movement paths can be compared with the user's operation data to determine whether the user's operation data conforms to the behavioral feature sequence corresponding to the behavioral model, thereby accurately determining whether the user's operation data corresponds to the preset event. Therefore, the embodiments of this specification can provide a solution for accurately determining events encountered by a user.

[0026] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this specification. Attached Figure Description

[0027] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this specification and, together with the description, serve to explain the principles of this specification. It is obvious that the drawings described below are merely some embodiments of this specification, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0028] Figure 1 This is a flowchart illustrating the method for determining the behavior model provided in the embodiments of this specification.

[0029] Figure 2 A schematic diagram of the behavioral model provided in the embodiments of this specification.

[0030] Figure 3 This is a schematic diagram of the adaptation method for the behavioral model provided in the embodiments of this specification.

[0031] Figure 4 This is a flowchart illustrating the event determination method provided in the embodiments of this specification.

[0032] Figure 5 This is a flowchart illustrating the comparison operation data and behavior model method provided in the embodiments of this specification.

[0033] Figure 6 This is a flowchart illustrating the comparison operation data and behavior model method provided in the embodiments of this specification.

[0034] Figure 7This is a schematic diagram illustrating the determination of target node groups in a behavioral model, provided as an embodiment of this specification.

[0035] Figure 8 This is an interactive schematic diagram of the event determination method provided in the embodiments of this specification.

[0036] Figure 9 This is a schematic diagram of the interface for the event determination method provided in the embodiments of this specification.

[0037] Figure 10 This is a schematic diagram of the structure of the device for determining the behavior model provided in the embodiments of this specification.

[0038] Figure 11 This is a schematic diagram of the structure of the event determination device provided in the embodiments of this specification.

[0039] Figure 12 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this specification. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of this specification clearer, the embodiments of this specification will be described in further detail below with reference to the accompanying drawings.

[0041] In the following description, when referring to the accompanying drawings, the same numbers in different drawings denote the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this specification. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this specification as detailed in the appended claims.

[0042] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this specification more comprehensive and complete, and to fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of the embodiments described herein. However, those skilled in the art will recognize that the technical solutions described herein may be practiced with one or more of the specific details omitted, or other methods, components, apparatus, steps, etc., may be employed. In other instances, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of this specification.

[0043] Furthermore, the accompanying drawings are merely illustrative diagrams of this specification and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0044] This specification provides embodiments of a method for determining a behavior model, a method for determining an event, a device for determining a behavior model, a device for determining an event, a computer-readable storage medium, an electronic device, and a computer program product, addressing problems existing in related technologies.

[0045] The following is passed Figures 1 to 3 The embodiments of the method for determining the behavioral model provided in this specification are described in detail:

[0046] in, Figure 1 This is a flowchart illustrating the method for determining the behavior model provided in the embodiments of this specification. (Reference) Figure 1 The embodiment shown in the figure includes S110-S130.

[0047] In S110, a sequence of behavioral characteristics for a preset event is determined.

[0048] The aforementioned preset events refer to events that users may encounter while browsing an application platform or webpage. Examples include encountering fraud during online shopping or experiencing service unavailability. It is understood that the preset events involved in the embodiments of this specification can also be other events that users may encounter while browsing an application platform or webpage, and this specification does not limit these events.

[0049] The aforementioned behavioral feature sequence refers to multiple actions that have a sequential relationship. For example, the behavioral feature sequence corresponding to a user ordering a book could be: clicking a link to open the book's details page, browsing the details page, clicking a preset control to initiate an order for the book, and performing other actions on the order page, etc.

[0050] In S120, based on the behavioral feature sequence, multiple nodes with a sequence relationship are determined, and the movement lines between adjacent nodes that satisfy the above sequence relationship are determined, wherein each node corresponds to at least one matching condition, the matching condition includes matching features, matching methods and matching values, and the movement lines include the constraints that must be satisfied between the adjacent nodes.

[0051] in, Figure 2A schematic diagram illustrating the behavioral model provided in the embodiments of this specification. (See reference) Figure 2 In the embodiment shown in the figure, the behavior model 200 includes three nodes: node 1, node 2, and node 3. For example, each node represents the behavioral characteristics of a user entering an application platform or a web page. For instance, node 1 represents the user scanning a QR code on a page, and node 2 represents the user clicking on the page after scanning.

[0052] Specifically, each node corresponds to at least one matching condition. Each matching condition includes a matching feature, a matching method, and a matching value. The matching method can be a comparison operator such as equality, inclusion, exclusion, greater than, or less than. For example, in... Figure 2 When the behavioral characteristic corresponding to node 2 is a click behavior, the matching condition for this node regarding the click behavior can be expressed as: {Matching characteristic: Number of clicks; Matching method: Not less than; Matching value: 20 times}. For example, multiple matching conditions can be set for the same node. For instance, another matching condition can be set for node 2 above: {Matching characteristic: Total click duration; Matching method: Not less than; Matching value: 30 seconds}. It is evident that by setting matching conditions for each node, user behavior can be easily matched with that node, thereby quickly and accurately determining whether the user's behavior conforms to the behavioral model for a certain event.

[0053] Continue to refer to Figure 2 In the embodiment shown in the figure, the behavior model 200 includes three nodes, as well as movement lines 1 and 2. For example, each movement line provides conditions that need to be met between related nodes, specifically representing the conditions that need to be met between the behavioral characteristics of a user entering an application platform or a web page. For instance, movement line 1 between node 1 and node 2 provides conditions that need to be met between the behaviors of node 1 and node 2. For example, it specifies the time interval between nodes (e.g., the operation time interval must not exceed xx milliseconds) and / or the node interval (e.g., whether other nodes are allowed in between nodes, and the number of nodes in the interval). For example, the condition provided by movement line 1 can be described as: the time from node 1 to node 2 does not exceed 25 seconds, etc.

[0054] It should be noted that the embodiments in this specification will characterize the necessary features of the corresponding events, which will be determined as the matching conditions for each node and each movement path. Therefore, if the user's operation data meets the necessary features of a certain event, it will be determined that the user encountered the event characterized by the behavior model within the corresponding time period. For example... Figure 2 or Figure 3In the behavioral model shown above, each node constitutes a linear structure. It is understood that the nodes in the behavioral model provided in the embodiments of this specification are not limited to a linear structure, and may also constitute a branching structure, etc.

[0055] Continue to refer to Figure 1 In S130, the behavior model of the preset event is determined based on the above nodes and the above movement lines.

[0056] For example, Figure 3 This is a schematic flowchart illustrating the adaptation method of the behavioral model provided in the embodiments of this specification. The process of constructing the behavioral model 200 for a preset event may further include setting the input parameters and output parameters of the model, as shown in the reference. Figure 3 The input parameters of a certain behavior model include a user identity document (ID) and a time interval. The time interval is used to determine the user's operational data, which is then matched against various nodes and movement patterns within the behavior model. After matching, the output parameters have two possibilities. For example, if the user's operational data within the specified time period successfully matches the behavior model, the output parameter is "1," indicating that the user encountered event A (the event described by behavior model 200) during that time period. If the user's operational data within the specified time period does not successfully match the behavior model, the output parameter is "0," indicating that the user did not encounter event A (the event described by behavior model 200) during that time period.

[0057] In an exemplary embodiment, the constructed behavioral model can be encapsulated as a diagnostic service, so that the diagnostic service can be directly called in relevant scenarios where event diagnosis is required. Then, through the matching and comparison process between the operational data and the behavioral model, the diagnostic result can be finally determined, as shown above: the user encountered event A in the relevant time period, or the user did not encounter event A in the relevant time period.

[0058] Examples of this specification Figure 1The provided scheme for determining the behavior model first determines a sequence of behavioral features related to a preset event. Then, based on the behavioral feature sequence, it determines multiple nodes with sequential relationships, and the movement paths between adjacent nodes that satisfy the aforementioned sequential relationships. Each node corresponds to at least one matching condition, which includes matching features, matching methods, and matching values. The movement paths include constraints that must be satisfied between adjacent nodes. Furthermore, based on the aforementioned nodes and movement paths, the behavior model for the preset event can be determined. By processing the behavioral feature sequence into nodes and movement paths for matching, the behavior model composed of these nodes and movement paths can be compared with the user's operation data to determine whether the user's operation data conforms to the behavioral feature sequence corresponding to the behavior model, thereby accurately determining whether the user's operation data corresponds to the preset event.

[0059] The following is passed Figures 4 to 9 The embodiments of the event determination method provided in this specification are described in detail:

[0060] in, Figure 4 This is a flowchart illustrating the event determination method provided in the embodiments of this specification. (See reference...) Figure 4 The embodiment shown in the figure includes S410-S430.

[0061] In S410, the identity identifier of the target user is obtained, and the operation data of the target user during the target time period is obtained based on the identity identifier.

[0062] For example, after receiving a complaint from a target user, customer service personnel can identify the complainant and determine the time period in which the complaint occurred. For instance, the target user's complaint states that they made an online purchase on platform A between 18:00 and 18:05 on [date] and are dissatisfied with the process. In this embodiment, the target user ID and "18:00-18:05 on [date]" can be used as input parameters. That is, the system can obtain the target user's operation data on platform A during the aforementioned time period (target time period) based on these input parameters. Furthermore, in this embodiment, the operation data is compared with a preset event behavior model to determine the event encountered by the target user during the aforementioned time period.

[0063] The user's actions within the target time period consist of a series of behaviors, each with a corresponding time point. For example, a user might scan a QR code on page A at 18:00:05, or click on control b on page B at 18:00:15, and so on.

[0064] In S420, the operational data is compared with the behavioral model. This behavioral model is based on, for example... Figure 1 The solution provided in the illustrated embodiment is determined.

[0065] In an exemplary embodiment, Figure 5 This is a flowchart illustrating the comparison operation data and behavior model method provided in the embodiments of this specification. The embodiment shown in this figure can be used as a specific implementation of S420. (See reference...) Figure 5 The embodiment shown in the figure includes S4201-S4209.

[0066] In S4201, a target node group is determined in the behavior model, wherein the target node group includes a first target node and a second target node that are adjacent based on sequence relations.

[0067] For example, processing starts from the first node in the node sequence based on the behavioral model. (See reference) Figure 7 The node group containing node 1 and node 2 will be used as the first target node group.

[0068] In S4203, the operation data is compared with the matching conditions corresponding to the first target node and the second target node, respectively. And in S4205, it is determined whether the operation data satisfies the matching conditions corresponding to the first target node and the second target node, respectively.

[0069] For example, refer to Figure 6 S610-S640 can be used as specific implementation methods.

[0070] In S610, the operation data is compared with the first matching condition corresponding to the first target node; in S620, it is determined whether the operation data meets the first matching condition.

[0071] refer to Figure 7 In the first target node group, the first target node is node 1, and the second target node is node 2. For example, the matching condition for the first target node (node ​​1) is: {Matching characteristics: total click duration on target page 1; Matching method: not less than; Matching value: 30 seconds}. Then, the action Q1 is determined from the operation data. Specifically, action Q1 can be expressed as: the total click duration on target page 1. Further, it is determined whether the total click duration on target page 1 is greater than or equal to 30 seconds. If so, then action Q1 in the operation data satisfies the first matching condition.

[0072] If the operation data meets the first matching condition, execute S630: compare the operation data with the second matching condition corresponding to the second target node; and in S640, determine whether the operation data meets the second matching condition.

[0073] Continue to refer to Figure 7For example, the matching condition corresponding to the second target node (node ​​2) is: {Matching feature: Scanning the code on target page 1; Matching method: Equal to; Matching value: Scanning successful and the page redirects to target page 2}. Then, the action Q2 is determined in the operation data. Specifically, action Q2 occurs later than action Q1, and action Q2 can be described as: scanning on target page 1, and after scanning, the page redirects to target page 2. This indicates that action Q2 in the operation data satisfies the second matching condition.

[0074] If the operation data does not meet the first matching condition or the second matching condition, execute S4209 directly: end the comparison process and determine that the target user does not belong to the event corresponding to this behavior model. This effectively saves matching time and improves event determination efficiency.

[0075] If the operation data satisfies the second matching condition, that is, if the operation data simultaneously satisfies the matching conditions corresponding to the first target node and the second target node respectively, then execute S4207: compare the operation data with the target movement line, and determine the next target node group if the operation data satisfies the target movement line. The aforementioned target movement line is the movement line between the aforementioned first target node and the aforementioned second target node.

[0076] As can be seen, for a given target node group, node sequence matching is performed first. That is, from the perspective of node sequence matching, the operation data is matched sequentially against the matching conditions corresponding to the two nodes in the node group. If the matching conditions corresponding to the two nodes in the node group are both satisfied, then the matching data is matched against the movement lines in that node.

[0077] refer to Figure 6 In S650, it is determined whether the operation data meets the target movement line.

[0078] Continue to refer to Figure 7 The target movement path in the first target node group is movement path 1. If movement path 1 contains the matching condition that the time interval between the action corresponding to node 1 and the action corresponding to node 2 is not less than 20 seconds, then the time interval between action Q1 and action Q2 in the operation data is obtained. If it is 10 seconds, then the above operation data is determined to satisfy movement path 1.

[0079] If the operational data does not match the target movement, execute S4209 directly: end the comparison process and determine that the target user does not belong to the event corresponding to this behavior model. This effectively saves matching time and improves event determination efficiency.

[0080] Continue to refer to Figure 6If the operation data satisfies the target movement, execute S660: Based on the sequence relationship in the behavior model, determine the next node adjacent to the second target node to obtain the updated second target node, and execute S630: Compare the operation data with the second matching condition corresponding to the second target node.

[0081] In the embodiments of this specification, incremental detection is used to determine the node combinations that satisfy the node sequence matching pattern during the node matching process. Specifically, refer to... Figure 7 If, through the above embodiments, it is determined that the operational data satisfies the first target node group, then the next target node group is determined based on the sequence feature increment of the behavioral model. For example, according to... Figure 7 The sequence features of the behavioral model are shown. Node 3 is incrementally determined based on the first node group, and nodes 2 and 3 are used as the second target node group. Specifically, node 2 is the first target node in the target node group, and node 3 is the second target node in the same target node group.

[0082] Furthermore, since it has been determined that the operation data satisfies the second matching condition corresponding to node 2, the matching process begins from step S630. Specifically, the operation data is compared with the second matching condition corresponding to node 3 (the second target node in the second target node group). And further, the operation data is compared with movement line 2 (S4207).

[0083] And so on, executing in a loop as follows: Figure 6 The embodiments corresponding to S630-S4207. Until the determined second target node is the last node in the behavior model, if the operation data satisfies the target movement path within the current target node group, then the comparison result can be determined to satisfy the preset conditions described in S430.

[0084] Continue to refer to Figure 4 In step S430, if the comparison result meets preset conditions, the event corresponding to the target user in the target time period is determined based on the behavior model. That is, if the comparison result meets the preset conditions, it means that the sequence of actions of the target user in the target time period is consistent with the sequence of actions of the behavior model, and therefore the event corresponding to the behavior model is the event encountered by the target user in the target time period.

[0085] In an exemplary embodiment, Figure 8 This is an interactive schematic diagram of the event determination method provided in the embodiments of this specification. In the embodiments of this specification, each behavioral model is encapsulated as a diagnostic service. The system then calls the aforementioned diagnostic service to compare the operation data generated by the target user during the target time period with the behavioral model provided by the diagnostic service.

[0086] refer to Figure 8 In S80, the system sends a diagnostic request to the diagnostic service. For example, the diagnostic request includes the target identity and the target time period. The exemplary diagnostic request may also include parameters of the requested behavioral model, see reference [link to relevant documentation]. Figure 9 If a customer wishes to compare operational data with the five behavioral models shown in the figure, the diagnostic request must include service parameters for these five behavioral models. Specifically, the five behavioral models are: Behavioral Model A for event A, Behavioral Model B for event B, Behavioral Model C for event C, Behavioral Model D for event D, and Behavioral Model E for event E.

[0087] In S82, the diagnostic interface service (ServiceconfigFacaed) validates the parameters in the diagnostic request. For example, it determines whether the target user's identity meets preset conditions, and whether the target time period meets corresponding preset conditions. For example, if the diagnostic request includes service parameters related to the behavior model, it also needs to determine whether the service parameters meet preset conditions.

[0088] In S84, authorization information is obtained based on service parameters. For example, authorization information for each behavior model is obtained from database service 1 (ServiceAuthorityDAO) based on the service code to meet subsequent comparison requirements.

[0089] In S86, it verifies whether the requesting system is authorized. If the requesting system is authorized, S88 is executed: request service configuration based on service parameters. For example, the database service 2 (ServiceConfigDAO) requests configuration information about the above behavioral model.

[0090] In S810, the input parameters are validated. Since different behavior models correspond to different input parameters, the parameters carried in the diagnostic request are validated based on the configuration information of each behavior model to determine whether the parameters in the diagnostic request meet the input parameter requirements of each behavior model. If they do, S812 is executed: the model execution script is determined based on the service configuration.

[0091] In S814, the engine matcher generates a Structured Process Language (SPL). In S816, multiple SPL instructions are issued to the executor. Furthermore, the executor performs a comparison process between the operational data and various behavioral models.

[0092] In S818, the executor starts a thread pool; in S820, multiple threads concurrently request user operations on the database. A thread is assigned to each behavior model, and multiple threads compare the operation data with each behavior model in parallel. In S822, response data from each thread is retrieved from the user operation database. In S824, the executor packages the execution response data from multiple threads and sends it to the diagnostic interface service.

[0093] In S826, the diagnostic interface service receives execution response data from each thread. After further comparison of the operation data with multiple behavioral models, there may be behavioral models that do not match the operation data. In this embodiment, the behavioral model that matches the operation data can be referred to as the target behavioral model.

[0094] Further, in S828, the diagnostic interface service determines the diagnostic response based on the result output configuration. This result output configuration includes setting different priorities for different target behavior models, and may also include aggregating the output parameters corresponding to each target behavior model, i.e., setting an aggregation coefficient for each target behavior model. Different diagnostic responses are determined based on different result output configurations. In S830, the diagnostic interface service returns the diagnostic response to the system. Thus, the system can determine the events that the target user may encounter during the target time period.

[0095] The event determination scheme provided in the embodiments of this specification quickly determines the events that a user may encounter during a target time period by comparing the user's operation data with a behavioral model. For example, in a scenario where customer service personnel are dealing with a user seeking assistance, they can accurately and quickly determine the event encountered by the user based on the diagnostic conclusions returned by the system, such as malicious marketing or service unavailability. Furthermore, customer service personnel can provide corresponding guidance and suggestions based on the event encountered, which is beneficial for effectively assisting users in resolving problems.

[0096] It should be noted that the above figures are merely illustrative of the processes included in the methods according to exemplary embodiments of this specification, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may, for example, be executed synchronously or asynchronously in multiple modules.

[0097] The following are embodiments of the apparatus described in this specification, which can be used to execute the embodiments of the methods described in this specification. For details not disclosed in the apparatus embodiments of this specification, please refer to the embodiments of the methods described in this specification.

[0098] in, Figure 10 A schematic diagram of a device for determining a behavior model that can be applied according to an embodiment of this specification is shown. Please refer to... Figure 10The device for determining the behavior model shown in the figure can be implemented as a whole or part of an electronic device through software, hardware, or a combination of both. It can also be integrated as an independent module on a server or as an independent module in an electronic device.

[0099] The device 1000 for determining the behavior model described in the embodiments of this specification includes: a first determining module 1010, a second determining module 1020, and a third determining module 1030.

[0100] The first determining module 1010 is used to determine a sequence of behavioral features related to a preset event; the second determining module 1020 is used to determine multiple nodes with a sequence relationship based on the behavioral feature sequence, and to determine the movement path between adjacent nodes that satisfy the sequence relationship, wherein each node corresponds to at least one matching condition, the matching condition includes matching features, matching methods and matching values, and the movement path includes the constraints that must be satisfied between the adjacent nodes; the third determining module 1030 is used to determine the behavioral model of the preset event based on the nodes and the movement path.

[0101] It should be noted that the behavior model determination device provided in the above embodiments is only illustrated by the division of the above functional modules when executing the behavior model determination method. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0102] Furthermore, the device and method for determining the behavior model provided in the above embodiments belong to the same concept. Therefore, for details not disclosed in the device embodiments of this specification, please refer to the embodiments of the behavior model determination method described above, which will not be repeated here.

[0103] in, Figure 11 A schematic diagram of an event determination apparatus to which an embodiment of this specification can be applied is shown. Please refer to... Figure 11 The event determination device shown in the figure can be implemented as a whole or part of an electronic device through software, hardware, or a combination of both. It can also be integrated as an independent module on a server or as an independent module in an electronic device.

[0104] The device 1100 for determining the above-mentioned events in the embodiments of this specification includes: an acquisition module 1110, a comparison module 1120, and a determination module 1130.

[0105] The acquisition module 1110 is used to acquire the identity identifier of the target user and acquire the operation data of the target user in the target time period based on the identity identifier; the comparison module 1120 is used to compare the operation data with the behavior model, wherein the behavior model is determined according to the behavior model determination method provided above; the determination module 1130 determines, if the comparison result meets the preset conditions, the event corresponding to the target user in the target time period based on the behavior model.

[0106] In an exemplary embodiment, based on the foregoing scheme, the comparison module 1120 includes: a determining unit, a first comparison unit, and a second comparison unit.

[0107] The aforementioned determining unit is used to determine a target node group in the aforementioned behavior model, wherein the target node group includes a first target node and a second target node that are adjacent based on sequence relations; the aforementioned first comparison unit is used to compare the aforementioned operation data with the matching conditions corresponding to the aforementioned first target node and the aforementioned second target node respectively; the aforementioned second comparison unit is used to compare the aforementioned operation data with the target movement line when the aforementioned operation data satisfies the matching conditions corresponding to the aforementioned first target node and the aforementioned second target node respectively, wherein the aforementioned target movement line is the movement line between the aforementioned first target node and the aforementioned second target node.

[0108] In an exemplary embodiment, based on the foregoing scheme, the first comparison unit is specifically used to: compare the operation data with the first matching condition corresponding to the first target node; and if the operation data satisfies the first matching condition, compare the operation data with the second matching condition corresponding to the second target node.

[0109] The second comparison unit is specifically used to compare the operation data with the target movement line when the operation data satisfies the first matching condition and the second matching condition respectively.

[0110] In an exemplary embodiment, based on the foregoing scheme, the determining unit is further configured to: when the operation data satisfies the target movement path, determine the next node adjacent to the second target node in the behavior model based on the sequence relationship, obtain the updated second target node, and perform the step of comparing the operation data with the second matching condition corresponding to the second target node through the first comparison unit.

[0111] In an exemplary embodiment, based on the foregoing scheme, the comparison module 1120 is further configured to: if the operation data satisfies the target movement line when the second target node is the last node in the behavior model, determine that the comparison result satisfies the preset conditions.

[0112] In an exemplary embodiment, based on the foregoing scheme, the comparison module 1120 is further configured to: determine a thread for each of the aforementioned behavioral models, and compare the operation data with the aforementioned behavioral models in parallel through multiple threads; the determination module 1130 is specifically configured to: obtain multiple target behavioral models corresponding to the comparison results that meet preset conditions; determine the events corresponding to the target user in the aforementioned target time period according to the priorities corresponding to the aforementioned multiple target behavioral models; or, perform aggregation processing according to the aggregation coefficients corresponding to the aforementioned multiple target behavioral models to obtain the events corresponding to the target user in the aforementioned target time period.

[0113] In an exemplary embodiment, based on the foregoing scheme, the device for determining the aforementioned event further includes a calling module.

[0114] The aforementioned behavioral model is encapsulated as a diagnostic service. The calling module is used to: after the aforementioned acquisition module obtains the target user's operation data within the target time period based on the aforementioned identity identifier, call the aforementioned diagnostic service to compare the aforementioned operation data with the behavioral model provided by the aforementioned diagnostic service.

[0115] It should be noted that the event determination device provided in the above embodiments is only illustrated by the division of the above functional modules when executing the event determination method. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0116] Furthermore, the event determination apparatus and event determination method embodiments provided in the above embodiments belong to the same concept. Therefore, for details not disclosed in the apparatus embodiments of this specification, please refer to the event determination method embodiments described above in this specification, which will not be repeated here.

[0117] Figure 12 This schematic diagram illustrates the structure of an electronic device according to an exemplary embodiment of this specification. Please refer to... Figure 12 As shown, the electronic device 1200 includes a processor 1201 and a memory 1202.

[0118] In this embodiment, processor 1201 is the control center of the computer system and can be a processor of a physical machine or a processor of a virtual machine. Processor 1201 may include one or more processing cores, such as a 4-core processor or an 8-core processor. Processor 1201 may be implemented using at least one hardware form selected from Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). Processor 1201 may also include a main processor and a coprocessor; the main processor is used to process data in the wake-up state, and the coprocessor is a low-power processor used to process data in the standby state.

[0119] In one embodiment of this specification, the processor 1201 is specifically used for:

[0120] Determine a sequence of behavioral features related to a preset event; based on the sequence of behavioral features, determine multiple nodes with sequential relationships, and determine the movement paths between adjacent nodes that satisfy the sequential relationships, wherein each node corresponds to at least one matching condition, the matching condition includes matching features, matching methods, and matching values, and the movement paths include the constraints that must be satisfied between the adjacent nodes; based on the nodes and the movement paths, determine the behavioral model of the preset event.

[0121] In another embodiment of this specification, the processor 1201 is specifically used for:

[0122] Obtain the identity identifier of the target user, and obtain the operation data of the target user in the target time period based on the identity identifier; compare the operation data with the behavior model, wherein the behavior model is determined according to the behavior model determination method provided above; if the comparison result meets the preset conditions, determine the event corresponding to the target user in the target time period based on the behavior model.

[0123] Furthermore, the comparison of the above-mentioned operational data with the behavioral model includes: identifying a target node group in the behavioral model, wherein the target node group includes a first target node and a second target node that are adjacent based on sequence relations; comparing the above-mentioned operational data with the matching conditions corresponding to the first target node and the second target node respectively; and, if the above-mentioned operational data satisfies the matching conditions corresponding to the first target node and the second target node respectively, comparing the above-mentioned operational data with a target movement line, wherein the target movement line is the movement line between the first target node and the second target node.

[0124] Furthermore, comparing the aforementioned operational data with the matching conditions corresponding to the first target node and the second target node respectively includes: comparing the operational data with the first matching condition corresponding to the first target node; if the operational data satisfies the first matching condition, comparing the operational data with the second matching condition corresponding to the second target node; and comparing the operational data with the target movement line if the operational data satisfies the matching conditions corresponding to the first target node and the second target node respectively includes: if the operational data satisfies both the first and second matching conditions respectively, comparing the operational data with the target movement line.

[0125] Furthermore, the processor 1201 is also configured to: when the operation data satisfies the target movement, determine the next node adjacent to the second target node in the behavior model based on the sequence relationship, obtain the updated second target node, and perform the step of comparing the operation data with the second matching condition corresponding to the second target node.

[0126] Furthermore, the processor 1201 is also used to: when the second target node is the last node in the behavior model, if the operation data satisfies the target movement line, determine that the comparison result satisfies the preset conditions.

[0127] Furthermore, the comparison of the aforementioned operational data with the behavioral models includes: assigning a thread to each behavioral model and comparing the operational data with each of the multiple behavioral models in parallel using multiple threads; determining the events corresponding to the target user in the target time period based on the behavioral models when the comparison results meet preset conditions includes: obtaining multiple target behavioral models corresponding to the comparison results that meet preset conditions; determining the events corresponding to the target user in the target time period based on the priorities of the multiple target behavioral models; or, performing aggregation processing based on the aggregation coefficients of the multiple target behavioral models to obtain the events corresponding to the target user in the target time period.

[0128] Furthermore, the aforementioned behavior model is encapsulated as a diagnostic service; furthermore, the aforementioned processor 1201 is also configured to: after obtaining the target user's operation data during the target time period based on the aforementioned identity identifier, invoke the aforementioned diagnostic service to compare the aforementioned operation data with the behavior model provided by the aforementioned diagnostic service.

[0129] Memory 1202 may include one or more computer-readable storage media, which may be non-transitory. Memory 1202 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments of this specification, the non-transitory computer-readable storage media in memory 1202 is used to store at least one instruction for execution by processor 1201 to implement the methods in the embodiments of this specification.

[0130] In some embodiments, the electronic device 1200 further includes a peripheral device interface 1203 and at least one peripheral device. The processor 1201, memory 1202, and peripheral device interface 1203 can be connected via a bus or signal line. Each peripheral device can be connected to the peripheral device interface 1203 via a bus, signal line, or circuit board. Specifically, the peripheral device includes at least one of a display screen 1204, a camera 1205, and an audio circuit 1206.

[0131] Peripheral interface 1203 can be used to connect at least one input / output (I / O) related peripheral device to processor 1201 and memory 1202. In some embodiments of this specification, processor 1201, memory 1202, and peripheral interface 1203 are integrated on the same chip or circuit board; in some other embodiments of this specification, any one or two of processor 1201, memory 1202, and peripheral interface 1203 can be implemented on separate chips or circuit boards. This specification does not specifically limit the embodiments in this regard.

[0132] Display screen 1204 is used to display a user interface (UI). The UI may include graphics, text, icons, videos, and any combination thereof. When display screen 1204 is a touch display screen, it also has the ability to collect touch signals on or above its surface. These touch signals can be input as control signals to processor 1201 for processing. In this case, display screen 1204 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments of this specification, there may be one display screen 1204, which serves as the front panel of electronic device 1200; in other embodiments, there may be at least two display screens 1204, respectively disposed on different surfaces of electronic device 1200 or in a folded design; in still other embodiments, display screen 1204 may be a flexible display screen, disposed on a curved or folded surface of electronic device 1200. Furthermore, display screen 1204 may also be configured as a non-rectangular irregular shape, i.e., a non-rectangular screen. The display screen 1204 can be made of materials such as liquid crystal display (LCD) and organic light-emitting diode (OLED).

[0133] Camera 1205 is used to capture images or videos. Optionally, camera 1205 includes a front-facing camera and a rear-facing camera. Typically, the front-facing camera is located on the front panel of the electronic device, and the rear-facing camera is located on the back of the electronic device. In some embodiments, there are at least two rear-facing cameras, which are any one of a main camera, a depth-sensing camera, a wide-angle camera, and a telephoto camera, to achieve background blurring by fusion of the main camera and the depth-sensing camera, panoramic shooting by fusion of the main camera and the wide-angle camera, virtual reality (VR) shooting, or other fusion shooting functions. In some embodiments of this specification, camera 1205 may also include a flash. The flash can be a single-color temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm light flash and a cool light flash, which can be used for light compensation at different color temperatures.

[0134] The audio circuit 1206 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, and convert the sound waves into electrical signals that are input to the processor 1201 for processing. For stereo sound acquisition or noise reduction purposes, there may be multiple microphones, each located in a different part of the electronic device 1200. The microphone may also be an array microphone or an omnidirectional microphone.

[0135] Power supply 1207 is used to supply power to various components in electronic device 1200. Power supply 1207 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When power supply 1207 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery that is charged via a wired line, and a wireless rechargeable battery is a battery that is charged via a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0136] The block diagrams of the electronic device shown in the embodiments of this specification do not constitute a limitation on the electronic device 1200. The electronic device 1200 may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0137] In the description of this specification, it should be understood that the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of these terms in this specification based on the specific circumstances. Furthermore, in the description of this specification, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship.

[0138] This specification also provides a computer-readable storage medium storing instructions that, when executed on a computer or processor, cause the computer or processor to perform one or more steps in the above embodiments. If the constituent modules of the above-described event determination device are implemented as software functional units and sold or used as independent products, they can be stored in the above-described computer-readable storage medium.

[0139] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this specification are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The aforementioned available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., Digital Versatile Discs (DVDs)), or semiconductor media (e.g., Solid State Disks (SSDs)).

[0140] It should be noted that the above description describes specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims may be performed in a different order than that shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0141] The above description is merely a specific embodiment of this specification, but the scope of protection of this specification is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this specification should be included within the scope of protection of this specification. Therefore, equivalent variations made in accordance with the claims of this specification are still within the scope of this specification.

Claims

1. A method for determining a behavioral model, wherein, The method includes: Determine the sequence of behavioral characteristics related to the preset event; Based on the behavioral feature sequence, multiple nodes with a sequence relationship are determined, and the movement path between adjacent nodes that satisfy the sequence relationship is determined, wherein the node corresponds to at least one matching condition, the matching condition includes matching features, matching methods and matching values, and the movement path includes the constraints that must be satisfied between the adjacent nodes; Based on the nodes and the movement path, determine the behavior model of the preset event; The behavior model is used to compare with operational data, which is the data of the target user in a target time period obtained based on the target user's identity. When the comparison result meets the preset conditions, the priority corresponding to the behavior model is used to determine the event corresponding to the target user in the target time period; or, the aggregation coefficient corresponding to the behavior model is used to obtain the event corresponding to the target user in the target time period through aggregation processing. The preset conditions are: the operation data satisfies the matching conditions corresponding to the multiple nodes in the behavior model, and satisfies the constraints included in the movement lines between adjacent nodes that satisfy the sequence relationship among the multiple nodes.

2. A method for determining an event, wherein, The method includes: Obtain the identity identifier of the target user, and obtain the operation data of the target user during the target time period based on the identity identifier; The operational data is compared with the behavioral model, wherein the behavioral model is the model determined according to the method of claim 1; If the comparison results meet the preset conditions, the event corresponding to the target user in the target time period is determined according to the behavior model; The step of determining the event corresponding to the target user in the target time period based on the behavior model when the comparison result meets the preset conditions includes: obtaining multiple target behavior models corresponding to the comparison results that meet the preset conditions; determining the event corresponding to the target user in the target time period based on the priority of each of the multiple target behavior models; or, performing aggregation processing based on the aggregation coefficients of each of the multiple target behavior models to obtain the event corresponding to the target user in the target time period. The preset conditions are: the operation data satisfies the matching conditions corresponding to the multiple nodes in the behavior model, and satisfies the constraints included in the movement lines between adjacent nodes that satisfy the sequence relationship among the multiple nodes.

3. The method according to claim 2, wherein, The step of comparing the operational data with the behavioral model includes: In the behavior model, a target node group is identified, wherein the target node group includes a first target node and a second target node that are adjacent based on sequence relations; The operation data is compared with the matching conditions corresponding to the first target node and the second target node, respectively. If the operation data satisfies the matching conditions corresponding to the first target node and the second target node respectively, the operation data is compared with the target movement line, wherein the target movement line is the movement line between the first target node and the second target node.

4. The method according to claim 3, wherein, The step of comparing the operation data with the matching conditions corresponding to the first target node and the second target node includes: The operation data is compared with the first matching condition corresponding to the first target node; If the operation data satisfies the first matching condition, the operation data is compared with the second matching condition corresponding to the second target node; The step of comparing the operation data with the target movement line when the operation data satisfies the matching conditions corresponding to the first target node and the second target node respectively includes: If the operation data satisfies both the first and second matching conditions, the operation data is compared with the target movement line.

5. The method according to claim 4, wherein, The method further includes: When the operation data satisfies the target movement, based on the sequence relationship in the behavior model, the next node adjacent to the second target node is determined, the updated second target node is obtained, and the step of comparing the operation data with the second matching condition corresponding to the second target node is executed.

6. The method according to any one of claims 3 to 5, wherein, The method further includes: If the second target node is the last node in the behavior model, and the operation data satisfies the target movement line, the comparison result is determined to meet the preset condition.

7. The method according to any one of claims 2 to 5, wherein, The step of comparing the operational data with the behavioral model includes: A thread is assigned to each behavior model, and the operation data is compared with each behavior model in parallel using multiple threads.

8. The method according to any one of claims 2 to 4, wherein, The behavioral model is encapsulated as a diagnostic service; After obtaining the target user's operation data within the target time period based on the identity identifier, the method further includes: The diagnostic service is invoked to compare the operational data with the behavioral model provided by the diagnostic service.

9. A device for determining a behavior model, wherein, The device includes: The first determining module is used to determine the sequence of behavioral characteristics related to the preset event; The second determining module is used to determine multiple nodes with a sequence relationship based on the behavioral feature sequence, and to determine the movement path between adjacent nodes that satisfy the sequence relationship, wherein the node corresponds to at least one matching condition, the matching condition includes matching features, matching method and matching value, and the movement path includes the constraint conditions that must be satisfied between the adjacent nodes; The third determining module is used to determine the behavior model of the preset event based on the node and the movement path; The behavior model is used to compare with operational data, which is the data of the target user in a target time period obtained based on the target user's identity. When the comparison result meets the preset conditions, the priority corresponding to the behavior model is used to determine the event corresponding to the target user in the target time period; or, the aggregation coefficient corresponding to the behavior model is used to obtain the event corresponding to the target user in the target time period through aggregation processing. The preset conditions are: the operation data satisfies the matching conditions corresponding to the multiple nodes in the behavior model, and satisfies the constraints included in the movement lines between adjacent nodes that satisfy the sequence relationship among the multiple nodes.

10. An event determination device, wherein, The device includes: The acquisition module is used to acquire the identity identifier of the target user, and to acquire the operation data of the target user during the target time period based on the identity identifier; A comparison module is used to compare the operation data with the behavior model, wherein the behavior model is determined according to the method described in claim 1; The determination module determines the events corresponding to the target user in the target time period based on the behavior model, provided that the comparison results meet preset conditions. The determining module is specifically used to: obtain multiple target behavior models corresponding to comparison results that meet preset conditions; determine the event corresponding to the target user in the target time period according to the priority of the multiple target behavior models respectively; or, perform aggregation processing according to the aggregation coefficients of the multiple target behavior models respectively to obtain the event corresponding to the target user in the target time period. The preset conditions are: the operation data satisfies the matching conditions corresponding to the multiple nodes in the behavior model, and satisfies the constraints included in the movement lines between adjacent nodes that satisfy the sequence relationship among the multiple nodes.

11. A computer-readable storage medium storing instructions, wherein, When the instructions are executed on a computer or processor, the computer or processor performs the method as described in claim 1, or the method as described in any one of claims 2 to 8.

12. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein, When the processor executes the computer program, it implements the method as described in claim 1, or the method as described in any one of claims 2 to 8.

13. A computer program product containing instructions, wherein, When the computer program product is run on a computer or processor, it causes the computer or processor to perform the method as claimed in claim 1, or to perform the method as claimed in any one of claims 2 to 8.

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