Intelligent guiding method and device based on user behavior, electronic equipment and system

Abstract

The application relates to the technical field of computers, and discloses an intelligent guiding method and device based on user behavior, electronic equipment and a system, the method comprising the following steps: monitoring operation sequence information of a user in an application program and related event information of the application program; analyzing a behavior characteristic model of the user in the application program according to the operation sequence information of the user; judging whether a current condition of the application program satisfies a preset guiding trigger condition according to the behavior characteristic model and the related event information of the application program; and when it is judged that the current condition of the application program satisfies the guiding trigger condition, rendering a guiding interface corresponding to a target element in the application program by analyzing element characteristic information of the target element. It can be seen that the application can accurately analyze user behavior in an application program, so that intelligent guidance can be realized for the user according to the accurately analyzed user behavior.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to an intelligent guidance method, device, electronic device, and system based on user behavior. Background Technology

[0002] The application interface contains various interface elements such as buttons and text, and each interface element has its corresponding function. Usually, a pre-set user guide is needed to help users quickly understand these functions.

[0003] However, existing user onboarding methods typically only detect whether a user is new. If so, they highlight pre-defined UI elements and add corresponding text descriptions to guide the new user through the steps of understanding the functions. This approach fails to accurately analyze user behavior, thus hindering intelligent user guidance based on precise behavioral analysis. Therefore, proposing a technical solution to improve the accuracy of user behavior analysis and achieve intelligent user guidance is of paramount importance. Summary of the Invention

[0004] This invention provides a user behavior-based intelligent guidance method, device, electronic device, and system, which can improve the accuracy of user behavior analysis and facilitate intelligent guidance for users.

[0005] The first aspect of this invention discloses an intelligent guidance method based on user behavior. The method is applied to an electronic device with an application installed, and the electronic device is communicatively connected to a network attached storage device (NETS). The electronic device accesses the NETS through the application. The method is used to guide any application function in the electronic device or the NETS; wherein the method includes: Monitor user operation sequence information and related event information in the application; and analyze the user's behavioral characteristic model in the application based on the user's operation sequence information. Based on the behavioral feature model and the relevant event information of the application, determine whether the current conditions of the application meet the preset guidance triggering conditions; When it is determined that the current conditions of the application meet the boot triggering conditions, the target element for booting is obtained from all elements of the application; Analyze the element feature information of the target element, wherein the element feature information includes one or more combinations of element shape information, highlight area information and highlight effect information; Based on the element feature information of the target element, generate the guidance content for the target element; and based on the guidance content of the target element, render the guidance interface corresponding to the target element to complete the guidance display operation for the user.

[0006] As an optional implementation, in a first aspect of the present invention, the operation sequence information includes historical operation information and current operation sequence information, wherein the operation sequence information includes at least one of the following: the number of times the user performs an operation in the application, the operation type of each operation, the operation interval between each operation, and the location information of each operation. And, the step of analyzing the user's behavioral characteristic model in the application based on the user's operation sequence information includes: Based on the user's historical operation information, the user type of the user is determined, and the initial behavior model corresponding to the user type is determined. The initial behavior model includes multiple dimension parameters, and all the dimension parameters of the initial behavior model include at least two of the following: proficiency rating parameter, learning ability index parameter, and preference setting parameter. Based on the user's current operation sequence information, identify abnormal operation information in all of the user's operations within the application. The abnormal operation information includes at least one of repeated operation information, operation pause information, and erroneous operation information. Based on the operation sequence information and the abnormal operation information, the initial behavior model is updated to obtain the user's behavior feature model in the application.

[0007] As an optional implementation, in a first aspect of the present invention, updating the initial behavior model based on the operation sequence information and the abnormal operation information to obtain the user's behavior feature model in the application includes: Based on the operation sequence information and the abnormal operation information, calculate the user's operation proficiency within one or more preset operation time periods; and determine the user's proficiency change trend based on the user's operation proficiency across all the operation time periods. Based on the proficiency change trend, analyze the user's learning progress curve; and based on the user's operational proficiency and learning progress curve across all operational time periods, update the user's user type to obtain the user's current user type. Based on the operation sequence information, the user's operation needs and preferences are identified; Based on the proficiency change trend, the learning progress curve, the current user type, and the operation requirements and preferences information, the initial behavior model is updated to obtain the user's behavior feature model in the application.

[0008] As an optional implementation, in the first aspect of the present invention, the related events indicated by the related event information include at least one of page switching events, function area entry events, and preset operation completion events; And, the step of determining whether the current conditions of the application meet the preset guidance triggering conditions based on the behavioral feature model and the relevant event information of the application includes: Based on the behavioral feature model, relevant event information of the application, and at least one pre-set detection condition, it is determined whether the application meets any of the detection conditions. When it is determined that the application meets any of the detection conditions, it is determined that the current condition of the application meets a preset guidance trigger condition; and / or, Based on the behavioral feature model and the relevant event information of the application, the guidance priority of each of the one or more functions of the application is calculated; and it is determined whether the guidance priority of any function is higher than or equal to a preset priority. When it is determined that the guidance priority of the function is higher than or equal to the preset priority, it is determined that the current condition of the application satisfies the guidance triggering condition.

[0009] As an optional implementation, in a first aspect of the present invention, determining whether the application satisfies any of the detection conditions based on the behavioral feature model, relevant event information of the application, and at least one pre-set detection condition includes: Based on the user's operation sequence information, analyze the user's usage of the application, including first-time use and / or boot interval information, and determine whether the application meets preset time detection conditions based on the usage information; and / or, Based on the relevant event information of the application, analyze the current state information of the application, which includes the current page of the application and the operating environment state of the current page. Based on the current state information, determine whether the application meets the preset state detection conditions; and / or, Based on the behavioral feature model and the user's operation sequence information, it is determined whether the application meets the preset user behavior detection conditions; Specifically, when at least one of the time detection condition, the state detection condition, and the user behavior detection condition is met, the application is determined to meet the corresponding detection condition.

[0010] As an optional implementation, in a first aspect of the present invention, the method further includes: Monitor the user's response information to the guided display operation; and assess the user's level of understanding of the guided information displayed in the guided display operation based on the guided response information. Based on the user's level of understanding, determine the user's operation type based on the guided display operation; and execute the management operation corresponding to the user's operation type. The user operation type includes a correct operation type or an incorrect operation type, and the management operation includes a positive feedback operation based on the correct operation type or a guided update operation based on the incorrect operation type.

[0011] A second aspect of the present invention discloses a user behavior-based intelligent guidance device, which is applied to an electronic device with an application installed, and the electronic device is communicatively connected to a network attached storage device (NETS). The electronic device accesses the NETS via the application, and the device is used to guide any application function in the electronic device or the NETS; wherein the device includes: The monitoring module is used to monitor the user's operation sequence information and related event information in the application; The analysis module is used to analyze the user's behavioral characteristic model in the application based on the user's operation sequence information; The judgment module is used to determine whether the current conditions of the application meet the preset guidance triggering conditions based on the behavior feature model and the relevant event information of the application. The acquisition module is used to acquire the target element for booting from all elements of the application when the judgment module determines that the current conditions of the application meet the boot triggering conditions; The analysis module is also used to analyze the element feature information of the target element, the element feature information including one or more combinations of element shape information, highlight area information and highlight effect information; The generation module is used to generate the guiding content of the target element based on the element feature information of the target element; The guidance module is used to render the guidance interface corresponding to the target element based on the guidance content of the target element, so as to complete the guidance display operation for the user.

[0012] As an optional implementation, in a second aspect of the present invention, the operation sequence information includes historical operation information and current operation sequence information, wherein the operation sequence information includes at least one of the following: the number of times the user performs an operation in the application, the operation type of each operation, the operation interval between each operation, and the location information of each operation. Furthermore, the method by which the analysis module analyzes the user's behavioral characteristic model in the application based on the user's operation sequence information specifically includes: Based on the user's historical operation information, the user type of the user is determined, and the initial behavior model corresponding to the user type is determined. The initial behavior model includes multiple dimension parameters, and all the dimension parameters of the initial behavior model include at least two of the following: proficiency rating parameter, learning ability index parameter, and preference setting parameter. Based on the user's current operation sequence information, identify abnormal operation information in all of the user's operations within the application. The abnormal operation information includes at least one of repeated operation information, operation pause information, and erroneous operation information. Based on the operation sequence information and the abnormal operation information, the initial behavior model is updated to obtain the user's behavior feature model in the application.

[0013] As an optional implementation, in a second aspect of the present invention, the analysis module updates the initial behavior model based on the operation sequence information and the abnormal operation information to obtain the user's behavior feature model in the application, specifically including: Based on the operation sequence information and the abnormal operation information, calculate the user's operation proficiency within one or more preset operation time periods; and determine the user's proficiency change trend based on the user's operation proficiency across all the operation time periods. Based on the proficiency change trend, analyze the user's learning progress curve; and based on the user's operational proficiency and learning progress curve across all operational time periods, update the user's user type to obtain the user's current user type. Based on the operation sequence information, the user's operation needs and preferences are identified; Based on the proficiency change trend, the learning progress curve, the current user type, and the operation requirements and preferences information, the initial behavior model is updated to obtain the user's behavior feature model in the application.

[0014] As an optional implementation, in a second aspect of the present invention, the related events indicated by the related event information include at least one of page switching events, function area entry events, and preset operation completion events; Furthermore, the method by which the judgment module determines whether the current conditions of the application meet the preset guidance triggering conditions based on the behavioral feature model and the relevant event information of the application specifically includes: Based on the behavioral feature model, relevant event information of the application, and at least one pre-set detection condition, it is determined whether the application meets any of the detection conditions. When it is determined that the application meets any of the detection conditions, it is determined that the current condition of the application meets a preset guidance trigger condition; and / or, Based on the behavioral feature model and the relevant event information of the application, the guidance priority of each of the one or more functions of the application is calculated; and it is determined whether the guidance priority of any function is higher than or equal to a preset priority. When it is determined that the guidance priority of the function is higher than or equal to the preset priority, it is determined that the current condition of the application satisfies the guidance triggering condition.

[0015] As an optional implementation, in a second aspect of the present invention, the method by which the judging module determines whether the application meets any of the detection conditions based on the behavioral feature model, the relevant event information of the application, and at least one pre-set detection condition specifically includes: Based on the user's operation sequence information, analyze the user's usage of the application, including first-time use and / or boot interval information, and determine whether the application meets preset time detection conditions based on the usage information; and / or, Based on the relevant event information of the application, analyze the current state information of the application, which includes the current page of the application and the operating environment state of the current page. Based on the current state information, determine whether the application meets the preset state detection conditions; and / or, Based on the behavioral feature model and the user's operation sequence information, it is determined whether the application meets the preset user behavior detection conditions; Specifically, when at least one of the time detection condition, the state detection condition, and the user behavior detection condition is met, the application is determined to meet the corresponding detection condition.

[0016] As an optional implementation, in a second aspect of the present invention, the monitoring module is further configured to monitor the user's guidance response information to the guidance display operation; The device also includes: An evaluation module is used to evaluate the user's level of understanding of the guidance information displayed in the guidance display operation based on the guidance response information. The determination module is used to determine the user operation type based on the user's level of understanding of the guided display operation; The management module is used to execute management operations corresponding to the user operation type; wherein the user operation type includes a correct operation type or an incorrect operation type, and the management operation includes a positive feedback operation based on the correct operation type or a guided update operation based on the incorrect operation type.

[0017] A third aspect of the present invention discloses an electronic device, the electronic device comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute some or all of the steps in the user behavior-based intelligent guidance method according to any of the first aspects of the present invention.

[0018] A third aspect of the present invention discloses a user behavior-based intelligent guidance system, the system comprising at least a user behavior-based intelligent guidance device as described in the second aspect of the present invention, and a network-attached storage device communicatively connected to the user behavior-based intelligent guidance device, wherein the network-attached storage device stores application functions, and when the user behavior-based intelligent guidance device performs user guidance on the application functions, the user behavior-based intelligent guidance device completes the user guidance operation for the application functions according to the user behavior-based intelligent guidance method as described in any of the first aspects of the present invention and based on the application functions. or, The system includes at least an electronic device as described in the third aspect of the present invention, and a network-attached storage device communicatively connected to the electronic device, wherein the network-attached storage device stores application functions, and when the electronic device performs user guidance on the application functions, the electronic device completes the user guidance operation for the application functions based on the application functions according to the intelligent guidance method based on user behavior as described in any of the first aspects of the present invention.

[0019] The fifth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute some or all of the steps in the user behavior-based intelligent guidance method described in any of the first aspects of the present invention.

[0020] Compared with the prior art, the present invention has the following beneficial effects: This invention accurately analyzes the user's behavioral characteristic model within an application by monitoring the user's operation sequence information and related event information. It can accurately analyze user behavior within the application, and then, based on the behavioral characteristic model and related event information, determine whether the application's current conditions meet preset guidance trigger conditions. Only when the guidance trigger conditions are met does it analyze the element characteristic information of the target element in the application and render the corresponding guidance interface to complete the guidance display operation for the user. This invention enables intelligent guidance based on accurately analyzed user behavior and avoids the limitations of using the same guidance scheme for different types of users, thus improving the targeting and effectiveness of guidance and enhancing the user's personalized guidance experience. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating an intelligent guidance method based on user behavior disclosed in an embodiment of the present invention. Figure 2 This is a flowchart illustrating another intelligent guidance method based on user behavior disclosed in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an intelligent guidance device based on user behavior disclosed in an embodiment of the present invention; Figure 4 This is a schematic diagram of another intelligent guidance device based on user behavior disclosed in an embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of the present invention; Figure 6 This is a schematic diagram of the structure of an intelligent guidance system based on user behavior disclosed in an embodiment of the present invention; Figure 7 This is a schematic diagram of another intelligent guidance system based on user behavior disclosed in an embodiment of the present invention. Detailed Implementation

[0023] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or ends.

[0025] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0026] This invention discloses a user behavior-based intelligent guidance method, device, electronic device, and system. It can accurately analyze the user's behavioral characteristic model within an application by monitoring the user's operation sequence information and related event information. This allows for precise analysis of user behavior within the application. Subsequently, based on the behavioral characteristic model and related event information, it determines whether the application's current conditions meet preset guidance trigger conditions. When the trigger conditions are met, it analyzes the element characteristic information of target elements within the application and renders the corresponding guidance interface to complete the guidance display operation for the user. This method achieves intelligent guidance based on accurately analyzed user behavior and avoids the limitations of using the same guidance scheme for different types of users, thus improving the targeting and effectiveness of guidance and enhancing the user's personalized guidance experience. Detailed descriptions follow.

[0027] Example 1 Please see Figure 1 , Figure 1This is a flowchart illustrating an intelligent guidance method based on user behavior disclosed in an embodiment of the present invention. Figure 1 The described user behavior-based intelligent bootstrapping method can be applied to electronic devices with installed applications, where the electronic device is communicatively connected to a network attached storage device (NETS). The electronic device accesses the NETS via the application, and the method is used to boot any application function in the electronic device or the NETS. This method can also be applied to user behavior-based intelligent bootstrapping devices, which may include a boot device, a boot system (cloud system or local system), or a boot server (cloud server or local server). This invention does not limit the scope of the application. Figure 1 As shown, this user behavior-based intelligent guidance method may include the following steps: 101. Monitor the user's operation sequence information in the application and the application's related event information.

[0028] In this embodiment of the invention, specifically, the initialization phase may include operations to set the boot system to initialize upon application startup, minimizing system resource usage; it may also include operations to verify required system permissions and dependent libraries to ensure normal system operation; and it may include operations to check the application version to handle boot adjustments related to version upgrades. Boot system initialization is achieved by loading configuration information, registering UI elements, and establishing behavior listeners. The specific steps for loading configuration information include: reading global configuration parameters, such as background transparency, animation effects, and default display time; loading custom themes and style settings; and initializing system-level switch states (such as developer mode and debug mode). The specific steps for registering UI elements include: scanning and registering UI elements with boot tags; constructing an element index table containing element ID, position information, boot priority, etc.; and preprocessing element attributes, such as shape features, interaction types, and associated functions. The specific steps for establishing behavior listeners include: deploying a global event capture mechanism to listen to user interaction behavior; initializing an operation sequence recorder to prepare for recording user operations; and setting up an application state observer to track page switching and function access.

[0029] Furthermore, the initialization phase may also include user model initialization. User model initialization is achieved by reading historical usage data and creating an initial user model. The specific steps for reading historical usage data include: loading the user's historical usage records from local storage; restoring the user model data from the previous session; and retrieving the completion status of all pre-saved historical guidance sessions. The specific steps for creating the initial user model include: for new users, creating a default beginner-level model; for existing users, restoring the user's existing model and checking for update necessity; after creating the model, it is also necessary to initialize various parameters of the model, such as proficiency score, learning ability index, and preference settings.

[0030] In this embodiment of the invention, the operation sequence information includes historical operation information and current operation sequence information. The operation sequence information includes at least one of the following: the number of user operations in the application, the operation type of each operation, the operation interval between each operation, and the location information of each operation. Specifically, the operation sequence information can be obtained by acquiring user trigger operations (e.g., capturing user clicks, swipes, pauses, etc.) and recording the operation time and location. The specific steps for acquiring user trigger operations may include: capturing all UI interactions using a low-latency event listener; recording the timestamp and coordinate position of each operation; associating the operation with the target UI element to determine the semantic meaning of the operation. The specific steps for recording operation time and location may include: measuring the duration of each operation; tracking the time interval between consecutive operations; and saving the position and trajectory of each operation on the interface. This embodiment of the invention does not limit the scope of these steps.

[0031] In this embodiment of the invention, the relevant events indicated by the relevant event information include at least one of page switching events, functional area entry events, and preset operation completion events. Specifically, the monitoring process for page switching events includes: capturing page navigation and route change events; identifying whether the page is the first page visited to obtain an identification result; and tracking the page dwell time and access frequency based on the identification result. The monitoring process for functional area entry events includes: monitoring whether the user has entered a specific functional area; if so, identifying the key function visited for the first time from all functions in that functional area; and tracking the depth of interaction between the user and the functional area (e.g., if the user's interaction depth with the functional area is deep, it is determined that the user has entered the functional area; if the interaction depth is shallow, monitoring continues). The monitoring process for preset operation completion events includes: monitoring whether there is a predefined completion signal for a key operation; if so, analyzing the quality and efficiency of the operation completion; and identifying difficulties and stutters in the operation.

[0032] 102. Analyze the user's behavioral characteristics model in the application based on the user's operation sequence information.

[0033] 103. Based on the behavioral feature model and relevant event information of the application, determine whether the current conditions of the application meet the preset boot trigger conditions.

[0034] In this embodiment of the invention, the triggering conditions may optionally include one or more combinations of time-triggered conditions, state-triggered conditions, and user behavior-triggered conditions, and this embodiment of the invention does not impose any limitations.

[0035] In this embodiment of the invention, when the judgment result of step 103 is yes, that is, when it is determined that the current condition of the application meets the boot trigger condition, step 104 is triggered; when the judgment result of step 103 is no, that is, when it is determined that the current condition of the application does not meet the boot trigger condition, the process can be terminated.

[0036] 104. Retrieve the target element for bootstrapping from all elements of the application.

[0037] In this embodiment of the invention, specifically, at least one initial element for guidance is located from all elements of the application using a global key or selector. The visibility and interactivity of each initial element are verified, and when the verification passes, the initial element is determined as the target element (i.e., the UI element). Thus, verifying visibility confirms that the element is currently visible in the viewport, and verifying interactivity confirms that the element is in an interactive state.

[0038] 105. Analyze the elemental characteristics of the target element.

[0039] In this embodiment of the invention, optionally, the element feature information includes one or more combinations of element shape information, highlight area information, and highlight effect information. The element shape information can be obtained by analyzing the element's specific visual attributes (such as rounded corners and shadows) after acquiring the element's precise boundary and shape information. Furthermore, if the target element is a composite element, the internal structure of the composite element can be further processed to obtain the element shape information. The highlight area information can be obtained by calculating the optimal coverage area of ​​the highlight effect of the target element and processing the spatial relationship between the target element and surrounding elements. Furthermore, if the target element has an irregular shape, the highlight area can be further optimized for the irregular shape. The highlight effect information can be obtained by selecting a visual effect type based on element characteristics, adjusting its effect parameters (such as brightness and animation speed) for that visual effect type, and further adjusting these effect parameters in conjunction with the current ambient light and screen characteristics.

[0040] 106. Generate guiding content for the target element based on its element feature information.

[0041] In this embodiment of the invention, the guiding content for the target element can be obtained by adjusting the text according to user characteristics. Adjusting the text according to user characteristics can involve preparing explanations of different levels of detail for different user levels. Specifically, this can include adjusting the proportion of technical terms used (e.g., lowering the proportion of technical terms for new users and increasing the proportion for experienced users), adjusting personalized expression and tone, etc. Furthermore, after generating the guiding content, the optimal display position can be determined. For example, positions that would obscure key content can be excluded from all available display positions. For the remaining display positions, the optimal display position is determined based on the user's reading order, eye movement direction, and different screen sizes and orientations.

[0042] 107. Based on the guidance content of the target element, render the corresponding guidance interface of the target element to complete the guidance display operation for the user.

[0043] In this embodiment of the invention, the process of rendering the guided interface may include operations such as creating a precisely fitted highlight mask, adding visual effects, and displaying guided text and interactive elements. Specifically, a mask path is generated based on the shape of the element; for this mask path, rendering is performed using pre-selected highlight effects (such as adding animation and transition effects), and text descriptions and indicator arrows are rendered, as well as operation buttons (such as "Next" and "Skip").

[0044] It is evident that implementation Figure 1 The described user behavior-based intelligent guidance method can accurately analyze user behavior patterns within an application by monitoring user operation sequences and related event information. This allows for precise analysis of user behavior, followed by a determination of whether the application's current conditions meet preset guidance trigger conditions. If the conditions are met, the method identifies the target element for guidance and accurately analyzes its features to generate appropriate guidance content. This improves the compatibility between the target element and the guidance content. The method then renders the corresponding guidance interface based on the accurately generated content, enhancing the accuracy and reliability of the guidance display. This approach enables intelligent guidance based on accurately analyzed user behavior, allows for dynamic adjustment of the guidance display through dynamically determined target elements, and improves the overall intelligence of the guidance display. Furthermore, by analyzing user behavior, the method avoids the limitations of using the same guidance scheme for different user types, improving the targeting and effectiveness of guidance and enhancing the personalized guidance experience for users.

[0045] In an optional embodiment, step 102 above, which analyzes the user's behavioral characteristic model in the application based on the user's operation sequence information, includes: Based on the user's historical operation information, determine the user type and the corresponding initial behavior model; Based on the user's current operation sequence information, identify abnormal operation information in all of the user's operations within the application; Based on the operation sequence information and abnormal operation information, the initial behavior model is updated to obtain the user's behavior feature model in the application.

[0046] In this embodiment of the invention, the user type is used to indicate whether the user is a new user or an existing user. Specifically, for new users, a default novice level model is created; for existing users, the user's existing model is restored.

[0047] In this embodiment of the invention, optionally, the initial behavior model includes multiple dimensional parameters, and all dimensional parameters of the initial behavior model include at least two of the following: proficiency rating parameters, learning ability index parameters, and preference setting parameters. This embodiment of the invention does not impose any limitations.

[0048] In this embodiment of the invention, optionally, abnormal operation information includes at least one of repeated operation information, operation pause information, and erroneous operation information. The process for identifying repeated operation information may include: detecting repeated clicks or swipes in the same area, analyzing the frequency and interval of repeated operations to determine whether the repeated operations are due to habit or confusion, obtaining a repeated operation judgment result, and determining the frequency and interval of repeated operations and the repeated operation judgment result as repeated operation information. The process for identifying operation pause information may include: identifying abnormal pauses in the user's operation flow, analyzing the operation context before and after the pause, evaluating the corresponding user state (thinking state or confused state) at the pause, and obtaining operation pause information. The process for identifying erroneous operation information may include: identifying invalid operation sequences (such as clicking on unresponsive areas), and / or detecting the frequency of undo / return operations, and / or analyzing the uncertainty and tortuosity of the operation path, and determining erroneous operation information from the identification results / detection results / analysis results.

[0049] As can be seen, this optional embodiment can determine the user type and the corresponding initial behavior model based on the user's historical operation information, thereby improving the accuracy and efficiency of determining the initial behavior model corresponding to the user type. Furthermore, based on the user's current operation sequence information, it can identify at least one of the abnormal operation information in all user operations within the application, such as repeated operation information, operation pause information, and erroneous operation information, improving the accuracy and efficiency of abnormal operation information identification. Subsequently, based on the operation sequence information and abnormal operation information, the initial behavior model is updated to obtain the user's behavior feature model within the application. This improves the accuracy and timeliness of updating the user's behavior feature model within the application by combining operation sequence information with quickly and accurately identified abnormal operation information, thus contributing to improved accuracy and timeliness of subsequent guidance.

[0050] In this optional embodiment, as an optional implementation method, the initial behavior model is updated based on the operation sequence information and abnormal operation information to obtain a user behavior feature model in the application, including: Based on the operation sequence information and abnormal operation information, calculate the user's operation proficiency within one or more preset operation time periods; and determine the user's proficiency change trend based on the user's operation proficiency across all operation time periods. Based on the trend of proficiency changes, analyze the user's learning progress curve; and based on the user's operational proficiency and learning progress curve across all operation time periods, update the user's user type to obtain the user's current user type. Based on the operation sequence information, identify the user's operation needs and preferences; Based on the proficiency change trend, learning progress curve, current user type, operation needs and preference information, the initial behavior model is updated to obtain the user's behavior feature model in the application.

[0051] In this embodiment of the invention, the calculation process for operational proficiency may be as follows: analyzing the comparison results between the operation completion speed and the standard reference value, and evaluating the operation fluency and accuracy, and then determining the proficiency range in which the comparison results, operation fluency and accuracy fall.

[0052] As can be seen, this optional implementation can accurately calculate a user's operational proficiency within one or more preset operation time periods using operation sequence information and abnormal operation information. Based on the user's operational proficiency across all operation time periods, it determines the user's proficiency change trend, improving the accuracy and reliability of this trend. Subsequently, based on the proficiency change trend, it accurately analyzes the user's learning progress curve. Based on the user's operational proficiency across all operation time periods and the learning progress curve, it updates the user's user type to obtain the current user type, improving the accuracy and reliability of this update. Based on the operation sequence information, it accurately identifies the user's operational needs and preferences. Then, based on the proficiency change trend, learning progress curve, current user type, and operational needs and preferences, it updates the initial behavior model to obtain the user's behavioral characteristic model within the application. This allows for accurate and timely updates to the user's behavior model through dynamic analysis of the user type, further improving the accuracy and reliability of user behavior analysis.

[0053] In another optional embodiment, based on the behavioral feature model and relevant event information of the application, it is determined whether the current conditions of the application meet the preset boot triggering conditions, including: Based on the behavioral feature model, relevant event information of the application, and at least one pre-defined detection condition, determine whether the application meets any detection condition, and when it is determined that the application meets any detection condition, determine that the current condition of the application meets the preset boot trigger condition; and / or, Based on the behavioral feature model and relevant event information of the application, calculate the boot priority of each function in one or more functions of the application; and determine whether the boot priority of any function is higher than or equal to the preset priority. When it is determined that the boot priority of the function is higher than or equal to the preset priority, determine that the current conditions of the application meet the boot triggering conditions.

[0054] In this embodiment of the invention, the guidance priority of each function can be obtained by analyzing the function's importance, the urgency of the user's need for the function, and the learning difficulty of the function. Specifically, the function's importance can be determined by assessing its core position within the application, analyzing its usage frequency and business value, and analyzing its complexity and learning cost. The urgency of the user's need for the function can be determined by analyzing the relevance of the user's current task within the corresponding function to the required guidance content, assessing the necessity of the function for the user's current goal, and analyzing the degree of difficulty exhibited by the user in operating the function. The learning difficulty of the function can be determined by calculating the user's cognitive complexity of the function and analyzing the difficulty users faced in mastering the function in historical data to determine the degree of difference between the function and the user's known concepts, and then evaluating based on this degree of difference.

[0055] As can be seen, this optional embodiment can flexibly and intelligently determine whether the application needs to trigger the boot process by setting multiple trigger conditions and boot priority triggering mechanisms. This helps to ensure that the boot process occurs at the most appropriate time, reduces the occurrence of boot malfunctions that interrupt the user's normal use of the application, and also helps to improve the user's acceptance of the boot process.

[0056] In this optional embodiment, as an optional implementation method, determining whether an application meets any detection condition based on a behavioral feature model, relevant event information of the application, and at least one pre-set detection condition includes: Based on the user's operation sequence information, analyze the user's usage of the application, including initial use and / or boot intervals, and determine whether the application meets preset time detection conditions based on the usage; and / or, Based on relevant event information from the application, analyze the application's current state information, including the current page and its operating environment. Based on this current state information, determine whether the application meets preset state detection conditions; and / or, Based on the behavioral feature model and the user's operation sequence information, determine whether the application meets the preset user behavior detection conditions.

[0057] In this embodiment of the invention, when at least one of the time detection condition, state detection condition, and user behavior detection condition is met, it is determined that the application meets the corresponding detection condition, that is, the current condition of the application meets the boot trigger condition; when none of the time detection condition, state detection condition, and user behavior detection condition is met, it is determined that the application does not meet any detection condition, that is, the current condition of the application does not meet the boot trigger condition.

[0058] In this embodiment of the invention, optionally, the step of determining whether an application meets the time detection condition may be: verifying whether the application or a specific function is being used for the first time, and if the verification is successful, then determining that the application meets the time detection condition; or it may be: calculating the time interval since the last guided display, and if the time interval is detected to exceed a preset interval, then determining that the application meets the time detection condition; or it may be: checking whether the current time point has reached a preset periodic reminder time point, and if the periodic reminder time point has been reached, then determining that the application meets the time detection condition. This embodiment of the invention does not limit the scope of the steps.

[0059] In this embodiment of the invention, optionally, the step of determining whether an application meets the state detection conditions may be: verifying whether the current page is suitable for displaying the bootloader, and determining that the application meets the state detection conditions when the verification passes; or it may be: assessing the complexity of the current operating environment, and determining that the application meets the state detection conditions when the complexity is detected to be greater than or equal to a preset complexity; furthermore, it may also be possible to check environmental factors such as network status and device performance, and determine that the application meets the state detection conditions only when the environmental factors are detected to meet the conditions for starting the bootloader. This embodiment of the invention does not limit the scope of the invention.

[0060] In this embodiment of the invention, optionally, the step of determining whether the application meets the user behavior detection conditions may be: determining the guidance trigger sensitivity (i.e., the sensitivity to trigger guidance) based on the user type, and determining that the application meets the user behavior detection conditions when the guidance trigger sensitivity is detected to be greater than or equal to a preset sensitivity; furthermore, the user's current emotion and focus state may be analyzed based on the user feedback information in the historical operation information contained in the user's operation sequence information to obtain the user's current emotion value and focus, and determining whether the current emotion value is within a preset guidance trigger emotion value range and whether the focus reaches a preset guidance trigger focus, and determining that the application meets the user behavior detection conditions when it is determined that the current emotion value is within a guidance trigger emotion value range and the focus reaches a preset guidance trigger focus.

[0061] As can be seen, this optional embodiment can determine whether an application meets at least one of the preset time detection conditions, state detection conditions, and user behavior detection conditions based on the behavioral feature model and the relevant event information of the application. If yes, the application is determined to meet the boot triggering conditions; otherwise, the application is determined not to meet the boot triggering conditions. By using diverse detection conditions, the accuracy, comprehensiveness, and reliability of determining whether an application meets the boot triggering conditions are improved.

[0062] Example 2 Please see Figure 2 , Figure 2 This is a flowchart illustrating an intelligent guidance method based on user behavior disclosed in an embodiment of the present invention. Figure 2The described user behavior-based intelligent bootstrapping method can be applied to electronic devices with installed applications, where the electronic device is communicatively connected to a network attached storage device (NETS). The electronic device accesses the NETS via the application, and the method is used to boot any application function in the electronic device or the NETS. This method can also be applied to user behavior-based intelligent bootstrapping devices, which may include a boot device, a boot system (cloud system or local system), or a boot server (cloud server or local server). This invention does not limit the scope of the application. Figure 2 As shown, this user behavior-based intelligent guidance method may include the following steps: 201. Monitor user operation sequence information and related event information in the application.

[0063] 202. Analyze the user's behavioral characteristics model in the application based on the user's operation sequence information.

[0064] 203. Based on the behavioral feature model and relevant event information of the application, determine whether the current conditions of the application meet the preset boot trigger conditions.

[0065] In this embodiment of the invention, when the judgment result of step 203 is yes, that is, when it is determined that the current condition of the application meets the boot triggering condition, step 204 is triggered; when the judgment result of step 203 is no, the process can be terminated.

[0066] 204. Retrieve the target element for bootstrapping from all elements of the application.

[0067] 205. Analyze the elemental characteristics of the target element.

[0068] 206. Generate guiding content for the target element based on its element feature information.

[0069] 207. Based on the guidance content of the target element, render the corresponding guidance interface of the target element to complete the guidance display operation for the user.

[0070] In this embodiment of the invention, for other descriptions of steps 201-207, please refer to the detailed description of steps 101-107 in Embodiment 1. This embodiment of the invention is not limited.

[0071] 208. Monitor user response information to guided display operations.

[0072] In this embodiment of the invention, optionally, the user's guided response information can be obtained by tracking the user's gaze focus, recording the user's operation attempts based on the guided interface, and measuring the user's response time information. Specifically, the matching degree between the user's attention points and the guided goals is analyzed to assess the user's attention to the guided content, thereby achieving the tracking of the user's gaze focus; all user interaction attempts are captured, and the conformity of each interaction attempt with the guided instructions is analyzed, as well as the number of attempts and the interval time are recorded to obtain the user's operation attempts based on the guided interface; the time interval between the start time of the guided display and the start time of the user's first operation is calculated, and the total time spent by the user to complete all operations indicated by the guided display is analyzed to obtain the user's operation response time; furthermore, the operation response time can be compared with the expected response time. Optionally, the user's response time information can include the user's operation response time, and can also include the time difference between the operation response time and the expected response time, which is not limited in this embodiment of the invention.

[0073] 209. Based on the guidance response information, assess the user's level of understanding of the guidance information displayed during the guidance display operation.

[0074] In this embodiment of the invention, the user's level of understanding can be assessed by verifying the correctness of the operation, analyzing the smoothness of the operation, and detecting repeated attempts. Verifying the correctness of the operation may include at least one of the following: checking whether the user's operation meets expectations, analyzing the accuracy of operation parameters (such as click position, swipe distance), and verifying the correctness of the operation sequence (applicable to multi-step guidance). Analyzing the smoothness of the operation may include at least one of the following: evaluating the continuity and rhythm of the operation, detecting hesitation and repetition, and analyzing the accuracy and control of the action. Detecting repeated attempts may include at least one of the following: identifying patterns of multiple failed attempts, analyzing the consistency of the reasons for failure, and evaluating the slope of the user's learning curve. This embodiment of the invention does not limit the scope of these methods.

[0075] 210. Based on the user's level of understanding, determine the user's operation type based on the guided display; and execute the management operation corresponding to the user's operation type.

[0076] In this embodiment of the invention, user operation types include correct operation types or incorrect operation types, and management operations include positive feedback operations based on correct operation types or guided update operations based on incorrect operation types. Specifically, when the user operation type is a correct operation type, the positive feedback operations performed may include at least one operation such as displaying visual success prompts (e.g., animations, icons), providing encouraging text feedback, recording successful experiences, and updating the user model; when the user operation type is an incorrect operation type, the guided update operations performed may include error prompt operations and operations to simplify the guided steps. Error prompt operations may include providing targeted error correction guidance and / or highlighting the correct operation location or method. Operations to simplify the guided steps may include detecting consecutive failure patterns and automatically adjusting the guided content according to the failure patterns to provide more detailed explanations; furthermore, they may also include providing alternative paths or skip options, which are not limited in this embodiment of the invention.

[0077] It is evident that implementation Figure 2 The described user behavior-based intelligent guidance method can accurately analyze user behavior patterns within an application by monitoring user operation sequences and related event information. This allows for precise analysis of user behavior, followed by a determination of whether the application's current conditions meet preset guidance trigger conditions. If the conditions are met, the method identifies the target element for guidance and accurately analyzes its features to generate appropriate guidance content. This improves the compatibility between the target element and the guidance content. The method then renders the corresponding guidance interface based on the accurately generated content, enhancing the accuracy and reliability of the guidance display. This approach enables intelligent guidance based on accurately analyzed user behavior, allows for dynamic adjustment of the guidance display through dynamically determined target elements, and improves the overall intelligence of the guidance display. Furthermore, by analyzing user behavior, the method avoids the limitations of using the same guidance scheme for different user types, improving the targeting and effectiveness of guidance and enhancing the personalized guidance experience for users. Furthermore, by monitoring users' responses to guided display operations, it can accurately assess users' understanding of the guided information displayed in the guided display operations, thereby accurately determining the user's operation type based on the guided display operations, and flexibly and accurately executing management operations corresponding to the user's operation type.

[0078] For example, during the guided demonstration, the guided effect data is first recorded. This data may include at least one of the following: completion rate statistics (e.g., calculating the percentage of users who successfully complete the guided demonstration, analyzing the differences in completion rates for different guided steps, identifying common abandonment points and difficulties), comprehension speed analysis data (e.g., measuring the average time it takes for users to master new features, comparing the learning speed of different user groups, analyzing the correlation between comprehension speed and guided design), and guided satisfaction evaluation data (e.g., collecting user feedback on the applicability of the guided demonstration, analyzing users' continued usage patterns after the guided demonstration, and evaluating changes in the frequency of feature usage after the guided demonstration). Subsequently, based on the guided effect data, the guided strategy is updated. Specifically, this may include at least one of the following: adjusting triggering conditions (e.g., optimizing triggering timing based on effect data, fine-tuning triggering thresholds and sensitivity, personalizing triggering strategies for different user groups), optimizing guided content (e.g., improving poorly performing guided steps, adjusting copy and visual elements, redesigning complex or confusing interaction patterns), and improving highlighting effects (e.g., optimizing visual effects based on user attention data, adjusting animation and transition effects, optimizing display issues in special scenarios). Furthermore, it can also receive user-sent data, such as anonymous data, and analyze it. Specific analysis may include at least one of the following: submitting usage statistics, identifying common problems, and generating optimization suggestions. Specifically, submitting usage statistics may include at least one of the following: summarizing anonymous usage data (requiring user permission), generating boot system performance reports, and comparing the performance differences between different versions and configurations. Identifying common problems may include at least one of the following: analyzing patterns and trends in global data, identifying common difficulties across user groups, and discovering problems specific to a particular device or platform. Generating optimization suggestions may include at least one of the following: providing improvement suggestions based on big data analysis, recommending targeted optimization measures, and predicting the improvement in performance after optimization.

[0079] For example, the system architecture of this embodiment of the invention may consist of a core controller, a user behavior analyzer, an adaptive highlighter, a context manager, an interaction verifier, a development toolkit, a data flow and interaction model, etc.

[0080] The core controller serves as the central nervous system of the entire system, responsible for implementing one or more of the following functions: unified scheduling and management (coordinating communication and data flow between functional modules to ensure system efficiency and consistency), global state maintenance (managing the system's global configuration and state, such as current activity guidance, user characteristic models, and application runtime environment), lifecycle control (managing the initialization, startup, pause, recovery, and termination processes of the system), event distribution (receiving application layer events and distributing them to the appropriate functional modules for processing based on event type), and exception handling (unified capture and processing of exceptions during system operation to ensure system stability). In this way, the core controller interacts with the application layer through standardized API interfaces, providing developers with a simple yet powerful entry point for functionality.

[0081] User behavior analyzers are the foundation for personalized onboarding. Through in-depth analysis of user actions, they build accurate user characteristic models. The user behavior analyzer includes an operation sequence recorder, a dwell time monitor, a misoperation detector, and a user model generator. The operation sequence recorder captures various user actions in real time, such as clicks, swipes, long presses, and inputs; it records the type, target object, coordinates, and timestamp of each action; and uses a low-overhead event listening mechanism to minimize the impact on application performance. The dwell time monitor tracks the time users spend on various interfaces and functional elements; it identifies user hotspots and areas of interest; and detects areas that may present difficulties (abnormally long dwell times). The misoperation detector uses application pattern recognition algorithms to detect repetitive and invalid operations; it identifies frequent undo / return operations to determine user comprehension difficulties; and it analyzes abnormal patterns in operation sequences, such as random clicks and aimless swipes. The user model generator builds multi-dimensional user characteristic models based on collected behavioral data; it assesses user proficiency levels (novice, intermediate, expert); analyzes user learning curves and operational preferences; and uses incremental learning algorithms to continuously update the user model. The user behavior analyzer employs a lightweight algorithm design to ensure efficient operation on mobile devices while protecting user privacy, with all analysis performed locally.

[0082] The adaptive highlighter addresses the mismatch between highlighted areas and UI element shapes in traditional guide systems, providing precisely aligned and visually appealing highlight displays. It comprises a UI element shape detector, a dynamic mask generator, and an effects renderer. The UI element shape detector accurately captures the actual shape of target UI elements by accessing the render tree and view hierarchy; it supports complex shapes such as irregular buttons and custom controls; and it tracks element position and size changes in real time to adapt to dynamic UIs. The dynamic mask generator generates precisely aligned mask paths based on the detected element shapes; it supports rounded corners, shadows, gradients, and other visual effects; and it provides intelligent outline extraction for nested elements and complex view structures. The effects renderer provides various highlight visual effects, including pulses, halos, spotlights, and outline strokes; it automatically selects the most suitable effect based on the importance of the guide element and the UI style; it supports custom effect parameters such as color, intensity, and animation speed; and it uses hardware-accelerated rendering to ensure high performance and low power consumption. The adaptive highlighter features a non-intrusive design that requires no modification to existing UI code and can be seamlessly integrated into any Flutter application.

[0083] The Context Manager analyzes the application state and user environment to trigger bootstrapping at the optimal time, avoiding interruptions to the user's workflow. The Context Manager includes an application state listener, a trigger condition evaluator, and a priority sorter. The application state listener monitors application lifecycle events (startup, foreground, background, termination); tracks page navigation and route changes; listens for entry and exit of specific functional areas; and captures system state changes, such as network connectivity and device orientation. The trigger condition evaluator supports various trigger condition types, such as time conditions (e.g., first use, interval, frequency of use), state conditions (e.g., current page, function status, network status), and user conditions (e.g., user type, operation history, difficulty identification); it also provides a condition combiner supporting AND / OR / NOT logical operations and implements a condition priority mechanism to handle complex triggering strategies. The priority sorter calculates the priority of each boot when multiple boots simultaneously meet their trigger conditions. Priority calculation considers various factors, such as feature importance score, urgency of user needs, learning difficulty assessment, and developer-specified base priority. It also implements an adaptive adjustment mechanism to dynamically adjust priorities based on user feedback. Context managers ensure that onboarding occurs at the appropriate time, minimizing disruption to the user while increasing onboarding acceptance and effectiveness.

[0084] The interactive validator not only provides guidance content but also verifies whether users correctly understand and execute operations, forming a complete learning loop. The interactive validator includes an operation guidance generator, an operation completion detector, and a feedback generator. The operation guidance generator is used to personalize the guidance content based on user characteristic models, such as providing detailed step-by-step instructions for novice users, simplified guidance content for intermediate users, and only functional prompts for expert users; it is also used to generate different forms of guidance, such as text descriptions and prompts, illustrative animation demonstrations, interactive operation guidance, and voice prompts (if applicable). The operation completion detector is used to monitor the user's response to the guidance and verify the correctness of the operation through set completion criteria. These criteria can include simple criteria (such as element clicks, specific inputs) or complex criteria (such as operation sequences, condition combinations); it also supports setting operation timeouts and attempt limits. The feedback generator provides immediate feedback based on the results of operation verification. For example, it confirms and encourages successful feedback, provides prompts and corrections for incorrect feedback, and offers next-step instructions for guided feedback. It also adjusts the intensity and detail of feedback to match user characteristics and supports progressive prompts, gradually increasing the level of assistance. Through a complete feedback mechanism, the interaction verifier ensures that users truly master the functionality, rather than simply passively receiving information.

[0085] The SDK provides application developers with user-friendly configuration and management tools, simplifying the development and maintenance of bootstrapping systems. The SDK includes a visual configuration interface, bootstrapping orchestration tools, and data analysis dashboards. It significantly reduces the difficulty for developers to implement complex bootstrapping flows, improves development efficiency, and provides data support to help continuously optimize bootstrapping performance.

[0086] The data flow and interaction model can include multiple data flows, such as: user behavior capture flow, guidance trigger decision flow, personalized content generation flow, highlight rendering flow, interaction verification feedback flow, data analysis and optimization flow, etc.

[0087] Example 3 Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of an intelligent guidance device based on user behavior disclosed in an embodiment of the present invention. Wherein, Figure 3 The described user behavior-based intelligent guidance device can be applied to electronic devices with installed applications, and the electronic devices are communicatively connected to a network attached storage device (NETS). The electronic devices access the NETS via applications, and the device is used to guide any application function in the electronic devices or NETS. The device may include a guidance device or guidance system (local system or cloud system) or a guidance server (cloud server or local server). This invention is not limited in its embodiments. Figure 3 As shown, the user behavior-based intelligent guidance device may include: The monitoring module 301 is used to monitor the user's operation sequence information and related event information in the application.

[0088] Analysis module 302 is used to analyze the user's behavioral feature model in the application based on the user's operation sequence information.

[0089] The judgment module 303 is used to determine whether the current conditions of the application meet the preset guidance trigger conditions based on the behavioral feature model and the relevant event information of the application.

[0090] The acquisition module 304 is used to acquire the target element for booting from all elements of the application when the judgment module 303 determines that the current conditions of the application meet the boot triggering conditions.

[0091] The analysis module 302 is also used to analyze the element feature information of the target element, which includes one or more combinations of element shape information, highlight area information and highlight effect information.

[0092] The generation module 305 is used to generate the guiding content of the target element based on the element feature information of the target element.

[0093] The guidance module 306 is used to render the corresponding guidance interface of the target element according to the guidance content of the target element, so as to complete the guidance display operation for the user.

[0094] It is evident that implementation Figure 3The described user behavior-based intelligent guidance device can accurately analyze user behavior patterns within an application by monitoring user operation sequences and related event information. It then determines whether the application's current conditions meet preset guidance trigger conditions based on these patterns and event information. When the conditions are met, it identifies the target element for guidance and accurately analyzes its features to generate precise guidance content. This improves the compatibility between the target element and the guidance content. The device then renders the corresponding guidance interface based on the accurately generated content, enhancing the accuracy and reliability of the guidance display. This intelligent guidance system, based on accurately analyzed user behavior, allows for dynamic adjustments to the guidance display through dynamically determined target elements, improving the overall intelligence of the guidance presentation. Furthermore, by analyzing user behavior, it avoids the limitations of using the same guidance scheme for different user types, improving the targeting and effectiveness of guidance and enhancing the personalized guidance experience.

[0095] In an optional embodiment, the operation sequence information includes historical operation information and current operation sequence information, wherein the operation sequence information includes at least one of the following: the number of times the user performs an operation in the application, the operation type of each operation, the operation interval between each operation, and the location information of each operation. Furthermore, the analysis module 302 analyzes the user's behavioral characteristic model in the application based on the user's operation sequence information, specifically including the following methods: Based on the user's historical operation information, determine the user type and the corresponding initial behavior model. The initial behavior model includes multiple dimension parameters, and all dimension parameters of the initial behavior model include at least two of the following: proficiency score parameters, learning ability index parameters, and preference setting parameters. Based on the user's current operation sequence information, identify abnormal operation information in all user operations within the application. Abnormal operation information includes at least one of the following: repeated operation information, operation pause information, and erroneous operation information. Based on the operation sequence information and abnormal operation information, the initial behavior model is updated to obtain the user's behavior feature model in the application.

[0096] As can be seen, this optional embodiment can determine the user type and the corresponding initial behavior model based on the user's historical operation information, thereby improving the accuracy and efficiency of determining the initial behavior model corresponding to the user type. Furthermore, based on the user's current operation sequence information, it can identify at least one of the abnormal operation information in all user operations within the application, such as repeated operation information, operation pause information, and erroneous operation information, improving the accuracy and efficiency of abnormal operation information identification. Subsequently, based on the operation sequence information and abnormal operation information, the initial behavior model is updated to obtain the user's behavior feature model within the application. This improves the accuracy and timeliness of updating the user's behavior feature model within the application by combining operation sequence information with quickly and accurately identified abnormal operation information, thus contributing to improved accuracy and timeliness of subsequent guidance.

[0097] In this optional embodiment, as an optional implementation method, the analysis module 302 updates the initial behavior model based on the operation sequence information and abnormal operation information to obtain the user's behavior feature model in the application. Specifically, this includes: Based on the operation sequence information and abnormal operation information, calculate the user's operation proficiency within one or more preset operation time periods; and determine the user's proficiency change trend based on the user's operation proficiency across all operation time periods. Based on the trend of proficiency changes, analyze the user's learning progress curve; and based on the user's operational proficiency and learning progress curve across all operation time periods, update the user's user type to obtain the user's current user type. Based on the operation sequence information, identify the user's operation needs and preferences; Based on the proficiency change trend, learning progress curve, current user type, operation needs and preference information, the initial behavior model is updated to obtain the user's behavior feature model in the application.

[0098] As can be seen, this optional implementation can accurately calculate a user's operational proficiency within one or more preset operation time periods using operation sequence information and abnormal operation information. Based on the user's operational proficiency across all operation time periods, it determines the user's proficiency change trend, improving the accuracy and reliability of this trend. Subsequently, based on the proficiency change trend, it accurately analyzes the user's learning progress curve. Based on the user's operational proficiency across all operation time periods and the learning progress curve, it updates the user's user type to obtain the current user type, improving the accuracy and reliability of this update. Based on the operation sequence information, it accurately identifies the user's operational needs and preferences. Then, based on the proficiency change trend, learning progress curve, current user type, and operational needs and preferences, it updates the initial behavior model to obtain the user's behavioral characteristic model within the application. This allows for accurate and timely updates to the user's behavior model through dynamic analysis of the user type, further improving the accuracy and reliability of user behavior analysis.

[0099] In another optional embodiment, the relevant events indicated by the relevant event information include at least one of a page switching event, a function area entry event, and a preset operation completion event. Furthermore, the method by which the judgment module 303 determines whether the current conditions of the application meet the preset guidance triggering conditions based on the behavioral feature model and the relevant event information of the application specifically includes: Based on the behavioral feature model, relevant event information of the application, and at least one pre-defined detection condition, determine whether the application meets any detection condition, and when it is determined that the application meets any detection condition, determine that the current condition of the application meets the preset boot trigger condition; and / or, Based on the behavioral feature model and relevant event information of the application, calculate the boot priority of each function in one or more functions of the application; and determine whether the boot priority of any function is higher than or equal to the preset priority. When it is determined that the boot priority of the function is higher than or equal to the preset priority, determine that the current conditions of the application meet the boot triggering conditions.

[0100] As can be seen, this optional embodiment can flexibly and intelligently determine whether the application needs to trigger the boot process by setting multiple trigger conditions and boot priority triggering mechanisms. This helps to ensure that the boot process occurs at the most appropriate time, reduces the occurrence of boot malfunctions that interrupt the user's normal use of the application, and also helps to improve the user's acceptance of the boot process.

[0101] In this optional embodiment, as an optional implementation method, the determination module 303 determines whether the application meets any detection condition based on the behavioral feature model, relevant event information of the application, and at least one pre-set detection condition. Specifically, this includes: Based on the user's operation sequence information, analyze the user's usage of the application, including initial use and / or boot intervals, and determine whether the application meets preset time detection conditions based on the usage; and / or, Based on relevant event information from the application, analyze the application's current state information, including the current page and its operating environment. Based on this current state information, determine whether the application meets preset state detection conditions; and / or, Based on the behavioral feature model and the user's operation sequence information, determine whether the application meets the preset user behavior detection conditions; Specifically, when at least one of the time detection condition, state detection condition, and user behavior detection condition is met, the application is determined to meet the corresponding detection condition.

[0102] As can be seen, this optional implementation can determine whether an application meets at least one of the preset time detection conditions, state detection conditions, and user behavior detection conditions based on the behavioral feature model and relevant event information of the application. If so, the application is determined to meet the boot triggering conditions; otherwise, the application is determined not to meet the boot triggering conditions. By using diverse detection conditions, the accuracy, comprehensiveness, and reliability of determining whether an application meets the boot triggering conditions are improved.

[0103] In yet another optional embodiment, the monitoring module 301 is further configured to monitor user response information to guided display operations. And, as... Figure 4 As shown, Figure 4 This is a schematic diagram of another intelligent guidance device based on user behavior disclosed in an embodiment of the present invention, wherein the device may further include: The evaluation module 307 is used to evaluate the user's understanding of the guidance information displayed in the guidance display operation based on the guidance response information; The determination module 308 is used to determine the type of user operation based on the user's level of understanding. The management module 309 is used to execute management operations corresponding to the user operation type; wherein the user operation type includes a correct operation type or an incorrect operation type, and the management operation includes a positive feedback operation based on the correct operation type or a guided update operation based on the incorrect operation type.

[0104] As can be seen, this optional embodiment can accurately assess the user's understanding of the guidance information displayed in the guidance display operation by monitoring the user's guidance response information, thereby accurately determining the user's operation type based on the guidance display operation, so as to flexibly and accurately execute management operations corresponding to the user operation type.

[0105] Example 4 Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of the present invention. For example... Figure 5 As shown, the electronic device may include: Memory 401 storing executable program code; Processor 402 coupled to memory 401; The processor 402 calls the executable program code stored in the memory 401 to execute some or all of the steps in the user behavior-based intelligent guidance device described in Embodiment 1 or Embodiment 2 of the present invention.

[0106] Example 5 Please see Figure 6 , Figure 6 This is a schematic diagram of the structure of an intelligent guidance system based on user behavior disclosed in an embodiment of the present invention. Figure 6 As shown, the user behavior-based intelligent guidance system includes at least a user behavior-based intelligent guidance device 50 as described in Embodiment 3 of the present invention, and a network-attached storage device 60 communicatively connected to the user behavior-based intelligent guidance device 50. The network-attached storage device 60 stores application functions. When the user behavior-based intelligent guidance device 50 performs user guidance on the application functions, the user behavior-based intelligent guidance device 50 completes the user guidance operation for the application functions based on the user behavior-based intelligent guidance method described in Embodiment 1 or Embodiment 2 of the present invention and based on the application functions. or, Please see Figure 7 , Figure 7 This is a schematic diagram of another intelligent guidance system based on user behavior disclosed in an embodiment of the present invention. Figure 7 As shown, the user behavior-based intelligent guidance system includes at least an electronic device 40 as described in Embodiment 4 of the present invention, and a network-attached storage device 60 that is communicatively connected to the electronic device 40. The network-attached storage device 60 stores application functions. When the electronic device 40 performs user guidance on the application functions, the electronic device 40 completes the user guidance operation for the application functions based on the application functions according to the user behavior-based intelligent guidance method described in Embodiment 1 or Embodiment 2 of the present invention.

[0107] Example 6 This invention discloses a computer storage medium storing computer instructions. When these computer instructions are invoked, they are used to execute some or all of the steps in the user behavior-based intelligent guidance method described in Embodiment 1 or Embodiment 2 of this invention.

[0108] Example 7 This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps in the user behavior-based intelligent guidance method described in Embodiment 1 or Embodiment 2.

[0109] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0110] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0111] Finally, it should be noted that the above embodiments are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A user behavior-based intelligent guidance method, characterized in that, The method is applied to an electronic device with an application installed, and the electronic device is communicatively connected to a network attached storage device (NETS). The electronic device accesses the NETS through the application. The method is used to boot any application function in the electronic device or the NETS; wherein the method includes: Monitor user operation sequence information and related event information in the application; and analyze the user's behavioral characteristic model in the application based on the user's operation sequence information. Based on the behavioral feature model and the relevant event information of the application, determine whether the current conditions of the application meet the preset guidance triggering conditions; When it is determined that the current conditions of the application meet the boot triggering conditions, the target element for booting is obtained from all elements of the application; Analyze the element feature information of the target element, wherein the element feature information includes one or more combinations of element shape information, highlight area information and highlight effect information; Based on the element feature information of the target element, generate the guidance content of the target element; and based on the guidance content of the target element, render the guidance interface corresponding to the target element to complete the guidance display operation for the user. The operation sequence information includes historical operation information and current operation sequence information, wherein the operation sequence information includes at least one of the following: the number of times the user performs an operation in the application, the operation type of each operation, the operation interval between each operation, and the location information of each operation. And, the step of analyzing the user's behavioral characteristic model in the application based on the user's operation sequence information includes: Based on the user's historical operation information, the user type of the user is determined, and the initial behavior model corresponding to the user type is determined. The initial behavior model includes multiple dimension parameters, and all the dimension parameters of the initial behavior model include at least two of the following: proficiency rating parameter, learning ability index parameter, and preference setting parameter. Based on the user's current operation sequence information, identify abnormal operation information in all of the user's operations within the application. The abnormal operation information includes at least one of repeated operation information, operation pause information, and erroneous operation information. Based on the operation sequence information and the abnormal operation information, the initial behavior model is updated to obtain the user's behavior feature model in the application; The step of updating the initial behavior model based on the operation sequence information and the abnormal operation information to obtain the user's behavior feature model in the application includes: Based on the operation sequence information and the abnormal operation information, calculate the user's operation proficiency within one or more preset operation time periods; and determine the user's proficiency change trend based on the user's operation proficiency across all the operation time periods. Based on the proficiency change trend, analyze the user's learning progress curve; and based on the user's operational proficiency and learning progress curve across all operational time periods, update the user's user type to obtain the user's current user type. Based on the operation sequence information, the user's operation needs and preferences are identified; Based on the proficiency change trend, the learning progress curve, the current user type, and the operation requirements and preferences information, the initial behavior model is updated to obtain the user's behavior feature model in the application.

2. The intelligent guidance method based on user behavior according to claim 1, characterized in that, The relevant events indicated by the relevant event information include at least one of page switching events, function area entry events, and preset operation completion events; And, the step of determining whether the current conditions of the application meet the preset guidance triggering conditions based on the behavioral feature model and the relevant event information of the application includes: Based on the behavioral feature model, relevant event information of the application, and at least one pre-set detection condition, it is determined whether the application meets any of the detection conditions. When it is determined that the application meets any of the detection conditions, it is determined that the current condition of the application meets a preset guidance trigger condition; and / or, Based on the behavioral feature model and the relevant event information of the application, the guidance priority of each of the one or more functions of the application is calculated; and it is determined whether the guidance priority of any function is higher than or equal to a preset priority. When it is determined that the guidance priority of the function is higher than or equal to the preset priority, it is determined that the current condition of the application satisfies the guidance triggering condition.

3. The intelligent guidance method based on user behavior according to claim 2, characterized in that, The step of determining whether the application meets any of the detection conditions based on the behavioral feature model, the relevant event information of the application, and at least one pre-set detection condition includes: Based on the user's operation sequence information, analyze the user's usage of the application, including first-time use and / or boot interval information, and determine whether the application meets preset time detection conditions based on the usage information; and / or, Based on the relevant event information of the application, analyze the current state information of the application, which includes the current page of the application and the operating environment state of the current page. Based on the current state information, determine whether the application meets the preset state detection conditions; and / or, Based on the behavioral feature model and the user's operation sequence information, it is determined whether the application meets the preset user behavior detection conditions; Specifically, when at least one of the time detection condition, the state detection condition, and the user behavior detection condition is met, the application is determined to meet the corresponding detection condition.

4. The intelligent guidance method based on user behavior according to any one of claims 1-3, characterized in that, The method further includes: Monitor the user's response information to the guided display operation; and assess the user's level of understanding of the guided information displayed in the guided display operation based on the guided response information. Based on the user's level of understanding, determine the user's operation type based on the guided display operation; and execute the management operation corresponding to the user's operation type. The user operation type includes a correct operation type or an incorrect operation type, and the management operation includes a positive feedback operation based on the correct operation type or a guided update operation based on the incorrect operation type.

5. A user behavior-based intelligent guidance device, characterized in that, The device is applied to an electronic device with an application installed, and the electronic device is communicatively connected to a network attached storage device (NETS). The electronic device accesses the NETS via the application. The device is used to bootstrap any application function in the electronic device or the NETS, and the device is used to execute the user behavior-based intelligent boot method as described in any one of claims 1-4; wherein the device includes: The monitoring module is used to monitor the user's operation sequence information and related event information in the application; The analysis module is used to analyze the user's behavioral characteristic model in the application based on the user's operation sequence information; The judgment module is used to determine whether the current conditions of the application meet the preset guidance triggering conditions based on the behavior feature model and the relevant event information of the application. The acquisition module is used to acquire the target element for booting from all elements of the application when the judgment module determines that the current conditions of the application meet the boot triggering conditions; The analysis module is also used to analyze the element feature information of the target element, the element feature information including one or more combinations of element shape information, highlight area information and highlight effect information; The generation module is used to generate the guiding content of the target element based on the element feature information of the target element; The guidance module is used to render the guidance interface corresponding to the target element based on the guidance content of the target element, so as to complete the guidance display operation for the user.

6. An electronic device, characterized in that, The electronic device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the user behavior-based intelligent guidance method as described in any one of claims 1-4.

7. A user behavior-based intelligent guidance system, the system comprising at least the user behavior-based intelligent guidance device as described in claim 5, and a network-attached storage device communicatively connected to the user behavior-based intelligent guidance device, wherein the network-attached storage device stores application functions, and when the user behavior-based intelligent guidance device performs user guidance on the application functions, the user behavior-based intelligent guidance device completes the user guidance operation for the application functions according to the user behavior-based intelligent guidance method as described in any one of claims 1-4 and based on the application functions; or, The system includes at least the electronic device as described in claim 6, and a network-attached storage device communicatively connected to the electronic device, wherein the network-attached storage device stores application functions, and when the electronic device performs user guidance on the application functions, the electronic device completes the user guidance operation for the application functions based on the user behavior-based intelligent guidance method as described in any one of claims 1-4 and based on the application functions.

8. A computer storage medium, characterized in that, The computer storage medium stores computer instructions, which, when invoked, are used to execute the user behavior-based intelligent guidance method as described in any one of claims 1-4.

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