Multi-data-source prompt information processing method and system

Abstract

The embodiment of the invention provides a multi-data-source prompt information processing method and system, and the method comprises the steps: receiving first prompt information from a plurality of data sources during the execution of a digital currency application program; analyzing the first prompt information, determining key data of the first prompt information, and abstracting the first prompt information into a first prompt task; according to one or more of a data source to which the first prompt task belongs, at least part of the key data, and response behavior characteristics of a user to at least one second prompt task which is received previously from the same data source, determining the priority of the first prompt task and the priority sequence between the first prompt task and the existing prompt tasks in the prompt queue, and updating the prompt queue; and according to the priority sequence of the prompt tasks, displaying each prompt task in a display window corresponding to the prompt type to which the prompt task belongs. The system resource consumption can be reduced, the timely display of the high-priority prompt task is ensured, and the user experience is improved.

Description

Technical Field

[0001] Embodiments of this disclosure relate to a method, system, electronic device, and storage medium for processing prompt information from multiple data sources. Background Technology

[0002] In application development, there are often scenarios where information needs to be retrieved from multiple data sources and displayed to users in the form of pop-ups. For example, in a comprehensive financial application, information such as account balance change alerts and red envelopes may be retrieved from multiple data sources, including banking systems, merchant platform systems, and third-party payment institutions, and then displayed to users via pop-ups.

[0003] However, when Android operating systems handle pop-ups from multiple data sources, the common practice is to display them sequentially according to their arrival time, or to ensure priority by blocking the thread of the corresponding pop-up. These practices have obvious drawbacks, which can lead to important pop-ups being covered or causing a waste of thread resources. Summary of the Invention

[0004] At least one embodiment of this disclosure provides a method for processing prompt information from multiple data sources. The method includes: receiving at least one first prompt information from different data sources during the execution of a digital currency application; parsing the first prompt information to determine key data of the first prompt information, and abstracting the first prompt information into a first prompt task based on the key data, wherein the key data includes a prompt title, prompt content, and prompt type; determining the priority of each first prompt task and its priority order relative to existing prompt tasks in the prompt queue based on one or more of the data source to which each first prompt task belongs, at least a portion of the key data, and user response behavior characteristics to at least one second prompt task previously received from the same data source, and updating the prompt queue; and displaying each prompt task in a corresponding display window of the digital currency application according to the priority order of each prompt task in the prompt queue and based on the key information of each prompt task.

[0005] For example, in the step of determining the priority of a first prompt task, a method for processing prompt information from multiple data sources according to at least one embodiment of the present disclosure includes: determining a contextual relationship with the current execution flow of the application based on the data source to which the first prompt task belongs and at least some of the key data; and determining, based on the contextual relationship, that the first prompt task has the highest priority in the prompt queue.

[0006] For example, the multi-data source prompt information processing method according to at least one embodiment of the present disclosure further includes: in response to the user's response to the prompt task displayed in the display window, removing the prompt task that the user has responded to from the prompt queue, and updating the priority order of the prompt tasks in the prompt queue according to the priority of the remaining prompt tasks in the prompt queue and / or the priority of at least one first prompt task received.

[0007] For example, the multi-data source prompt information processing method according to at least one embodiment of the present disclosure further includes: recording user response behavior data for each prompt task, and extracting response behavior features of prompt tasks from the same data source when a preset condition is met.

[0008] For example, in the step of displaying each prompt task in a display window in a multi-data source prompt information processing method according to at least one embodiment of the present disclosure, the step includes: displaying each prompt task in a display window corresponding to the prompt category, according to the size and position of each prompt task based on its priority.

[0009] For example, in the multi-data source prompt information processing method according to at least one embodiment of the present disclosure, the step of determining the priority of the first prompt task includes: comparing one or more of the data source to which the first prompt task belongs, at least some of the key data, and the user's response behavior characteristics to the second prompt task previously received from the same data source with a priority strategy; wherein the priority strategy includes multiple dimensions and the priority corresponding to each dimension or priority algorithms corresponding to different priority strategies.

[0010] For example, in the step of determining the priority order of the first prompt task in the multi-data source prompt information processing method according to at least one embodiment of the present disclosure, the method further includes: if the priority of the first prompt task conflicts with the priority of at least one prompt task in the task queue, then the priority order between the first prompt task and the conflicting prompt task is further determined according to at least one of the following elements: the arrival time of the first prompt task and the conflicting prompt task; the metadata associated with each of the first prompt task and the conflicting prompt task; and the communication frequency with the data source to which each of the first prompt task and the conflicting prompt task belongs.

[0011] At least one embodiment of this disclosure provides a multi-data source prompt information processing system, comprising: a communication module configured to receive at least one first prompt information from different data sources during the execution of a digital currency application; an information parsing module configured to parse the first prompt information and determine key data of the first prompt information, and abstract the first prompt information into a first prompt task based on the key data, wherein the key data includes a prompt title, prompt content, and prompt type; a priority engine module configured to determine the priority of each first prompt task and its priority order relative to existing prompt tasks in the prompt queue based on one or more of the data source to which each first prompt task belongs, at least a portion of the key data, and user response behavior characteristics to at least one second prompt task previously received from the same data source, and update the prompt queue; and an information display module configured to display each prompt task in a corresponding display window in the digital currency application according to the priority order of each prompt task in the prompt queue and based on the key data of each prompt task.

[0012] At least one embodiment of this disclosure provides an electronic device, including: one or more processors; and a memory storing one or more computer program modules; wherein the one or more computer program modules are configured to be executed by the one or more processors to implement a method provided according to at least one embodiment of this disclosure.

[0013] At least one embodiment of this disclosure provides a computer-readable storage medium for storing non-transitory computer-readable instructions, wherein the non-transitory computer-readable instructions, when executed by one or more processors, implement a method provided according to at least one embodiment of this disclosure.

[0014] The multi-data source prompt information processing scheme of this disclosure can solve the problems of high system resource consumption, untimely display of important information, and poor user experience when dealing with prompt information from multiple data sources, such as pop-up windows, during the execution of digital currency applications in existing solutions. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments of this disclosure will be briefly described below. Clearly, the drawings described below only relate to some embodiments of this disclosure and are not intended to limit the scope of this disclosure.

[0016] Figure 1 A block diagram of a prompt information processing system with multiple data sources according to at least one embodiment of the present disclosure is shown;

[0017] Figure 2 A flowchart is shown for a method of processing prompt information from multiple data sources according to at least one embodiment of the present disclosure;

[0018] Figure 3 A flowchart is shown for another information processing method using multiple data sources according to at least one embodiment of the present disclosure;

[0019] Figure 4 A block diagram of another multi-data source prompt information processing system according to at least one embodiment of the present disclosure is shown;

[0020] Figure 5 A flowchart illustrating a prompt task monitoring and processing method for a digital currency transaction process according to at least one embodiment of the present disclosure is shown.

[0021] Figure 6 A schematic diagram of an electronic device according to at least one embodiment of the present disclosure is shown;

[0022] Figure 7 A schematic diagram of a computer-readable storage medium according to at least one embodiment of the present disclosure is shown. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the described embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0024] Unless otherwise defined, the technical or scientific terms used herein should have the ordinary meaning understood by one of ordinary skill in the art to which this disclosure pertains. The terms “first,” “second,” and similar terms used in this disclosure do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, terms such as “comprising” or “including” mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as “connected” or “linked” are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as “upper,” “lower,” “left,” and “right” are used only to indicate relative positional relationships, which may change accordingly when the absolute position of the described objects changes.

[0025] Note that the examples described below are merely specific examples and are not intended to limit the embodiments of this disclosure to the specific shapes, hardware, connections, operations, values, conditions, data, sequences, etc., shown and described. Those skilled in the art can utilize the concepts of this disclosure to construct further embodiments not mentioned herein by reading this specification.

[0026] The terminology used in this disclosure is that which is currently widely used in the art in consideration of the functionality of this disclosure; however, these terms may vary depending on the intent, precedent, or new technology of those skilled in the art. Furthermore, specific terms may be chosen by the applicant, and in such cases, their detailed meanings will be described in the detailed description of this disclosure. Therefore, the terminology used in this specification should not be construed as simple names, but rather based on the meaning of the terms and the overall description of this disclosure.

[0027] This disclosure uses flowcharts to illustrate the operations performed by a system according to embodiments of this disclosure. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, various steps can be processed in reverse order or simultaneously, as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0028] As described in the background section, while displaying pop-ups strictly according to their arrival time is technically simple and consumes few system resources, it can lead to a situation where high-priority pop-ups are blocked by low-priority pop-ups. While thread blocking solutions can ensure timely responses to high-priority pop-ups, they involve complex thread management, consume significant thread resources, and are prone to deadlocks.

[0029] At least one embodiment of this disclosure provides a method, system, electronic device, and storage medium for information processing from multiple data sources. By abstracting received prompt information into a prompt task, assigning a priority to the prompt task, and adding the prompt task to a prompt queue, each prompt task in the prompt queue is displayed sequentially according to its priority. This reduces system resource consumption and ensures the timely display of high-priority prompt tasks, thereby improving the user experience.

[0030] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0031] Figure 1 A block diagram of a multi-data source prompt information processing system 1000 according to at least one embodiment of the present disclosure is shown. Figure 1As shown, the system 1000 receives message data from multiple data sources 2000. These multiple data sources 2000 can refer to various types of data sources from different systems, platforms, or channels, primarily third-party data sources external to the system. In practical applications, these data sources may have different data formats, transmission protocols, and storage methods.

[0032] like Figure 1 As shown, system 1000 includes a communication module 110, an information parsing module 120, a priority engine module 130, an information display module 140, a priority strategy module 160, and an optional user behavior monitoring module 150 and user behavior database 150A. In other embodiments, system 1000 may include alternative configurations, different components, or additional components. Each module may be implemented as software, firmware, hardware, or a combination thereof. During the operation of an application (e.g., a digital currency application), system 1000 can receive prompts from multiple data sources, such as digital currency wallet management backends, bank systems acting as digital currency operators, merchant platform systems, and third-party payment institutions. It then determines the priority of each prompt task through parsing and displays the prompt tasks in the corresponding display window according to their priority in the prompt queue.

[0033] For example, in some embodiments of this disclosure, the communication module 110 establishes communication between the system 1000 and multiple data sources 2000, receiving at least one first notification message from different data sources 2000. Taking a digital currency application as an example, multiple data sources may include financial institution data (e.g., transaction records and account information of commercial banks and payment institutions), merchant-side data (e.g., transaction logs and user behavior data of POS machines and e-commerce platforms), wallet data (e.g., balance of user digital wallets, transfer records, and operation logs), regulatory data (e.g., anti-money laundering and compliance data), and other data (third-party payment platforms, credit reporting systems, geolocation information, etc.). In this scenario, the first notification message may be a notification message generated by a payment institution to display changes in transaction status (success or failure), a notification message generated by an external partner institution, such as a merchant platform system, to display consumer red envelope information, or a notification message generated by the application backend server to display service upgrades and version updates. All of this information can be received by the communication module 110 and provided to the information parsing module 120.

[0034] For example, messages between communication module 110 and various data sources 2000 can be sent over a communication network using secure communication protocols, such as, but not limited to, File Transfer Protocol (FTP), Hypertext Transfer Protocol (HTTP), and Secure Hypertext Transfer Protocol (HTTPS). The communication network can include any one and / or a combination of the following: direct interconnection, the Internet, a local area network (LAN), a metropolitan area network (MAN), a secure custom connection, a wide area network (WAN), a wireless network, etc.

[0035] For example, the communication module 110 receives message request results or pushed messages through API interfaces or message push services (such as HTTP / HTTPS, MQTT, WebSockey, etc.). This information is transmitted in a structured data format, commonly JSON or XML. This data can also be initially stored and managed in a database, establishing a unified metadata directory to record the meaning, format, source, and communication frequency of fields from each data source.

[0036] For example, in some embodiments of this disclosure, the information parsing module 120 parses the first prompt information and determines the key data of the first prompt information, and abstracts the first prompt information into a first prompt task based on the key data, wherein the key data includes the prompt title, prompt content and prompt type.

[0037] In some examples, the data format of the initial prompt message sent from different data sources differs. Generally, they all contain key fields representing the prompt title, content, and type. By extracting these key fields from the prompt message in different data formats, the key data of the prompt message can be obtained. For example, when the initial prompt message is in JSON data format, it contains a "title" field representing the prompt title, a "content" field representing the prompt content, and a "type" field representing the prompt type. By extracting the information corresponding to these fields, the key data of the initial prompt message can be determined.

[0038] For example, the prompt type can include various display window types, such as full-screen windows, dialog boxes, tooltips / bubbles, and collapsible panels. In one example, for a cryptocurrency application, the display window types could include: "C2C (Customer-to-Customer) Red Packet" pop-up, "Consumption Red Packet" pop-up, "Service Upgrade" pop-up, "Homepage Guide" pop-up, "Consumption Red Packet" bubble, and "My" bubble.

[0039] like Figure 1As shown, the information parsing module 120 generates n prompt tasks (also called "pop-up tasks") 120A through the above operations. The priority levels of these prompt tasks are currently undetermined. Rendering multiple display windows simultaneously may block the main thread, while randomly rendering a single display window would reduce the user experience.

[0040] For example, in some embodiments of this disclosure, the priority engine module 130 determines the priority of the first prompt task and its priority order among existing prompt tasks in the prompt queue based on one or more of the following: the data source to which each first prompt task belongs (denoted as "first element"), at least a portion of the key data (denoted as "second element"), and the user's response behavior characteristics to at least one previously received second prompt task from the same data source (denoted as "third element"). In other words, the priority engine module 130 can determine the priority of the first prompt task and its priority order in the prompt queue based on the first element, the second element, the third element, a combination of the first and second elements, a combination of the first and third elements, or a combination of all elements.

[0041] For example, in some embodiments of this disclosure, the information display module 140 displays each prompt task in a display window in the digital currency application corresponding to the prompt type to which each prompt task belongs, according to the priority order of each prompt task in the prompt queue and based on the key data of each prompt task.

[0042] For example, each prompt task in the prompt queue can include the following: task identifier, prompt type, priority value (the smaller the value, the higher the priority), and display data (such as title, content, and timestamp). The prompt type can also be represented by a number; for example, "001" could represent a "Consumption Red Envelope" pop-up.

[0043] In one example, if the first notification message is in JSON format, it would look like this:

[0044] "notification":{

[0045] "type":"001",

[0046] "priority":"P0",

[0047] "title":"Consumption Red Packet",

[0048] "content":"You have an exclusive spending coupon from XX Bank waiting to be claimed."

[0049] "timestamp":"2023-10-01T14:30:00Z",

[0050] "action":"Claim Now"

[0051] }

[0052] By parsing and extracting fields, we obtained a pop-up window with the type "Consumption Red Packet," the theme "Consumption Red Packet," and the content "You have an exclusive consumption red packet issued by XX Bank waiting to be claimed." Furthermore, this key data can be used to construct metadata for the pop-up message, as shown in the table below:

[0053]

[0054] For example, in some embodiments of this disclosure, when abstracting the prompt information into a prompt task, the following operations may be included: First, a task object containing key attributes is predefined, supporting dynamic expansion and priority scheduling. The key attributes may include: task identifier ID, prompt title, prompt content, prompt type, priority, triggering conditions (such as user behavior, time window), execution strategy (such as immediate execution, delayed execution), data source identifier, and expiration time. Second, the parsed key data (such as JSON fields) is mapped to the task attributes, thereby abstracting it into a first prompt task corresponding to the first prompt information.

[0055] For example, in some embodiments, basic or dynamic weights can be pre-set for different data sources, and then the priority of the prompt task can be determined based on the calculated weight values. In one example, if the data source type is a banking system, the basic weight can be set to 0.7, and the weight can be dynamically increased by 0.2 or dynamically set to 1 in the case of failed transactions or transaction amounts exceeding a set amount (e.g., 20,000). If the data source type is a risk control engine, the basic weight can be set to 0.8, and the weight can be dynamically set to 1 in high-risk situations. If the data source type is a merchant platform, the basic weight can be set to 0.3, and the weight can be dynamically increased by 0.2 when, for example, the expiration time of a consumer coupon is less than a set time (e.g., 1 hour). When determining the priority of the first prompt task, it can be determined based on the set data source type weight. It is easy to understand that the higher the weight, the higher the priority, and the earlier the task will be in the prompt queue.

[0056] For example, in some embodiments, priority lists corresponding to different prompt themes and / or prompt categories can be pre-set. The parsed prompt themes and / or prompt categories are compared with the priority lists to determine the priority of the first prompt task. For example, in one example, for prompt categories such as "C2C Red Envelope" pop-up, "Consumption Red Envelope" pop-up, "Service Upgrade" pop-up, "Homepage Guide" pop-up, "Consumption Red Envelope" bubble, and "My" bubble, different priority levels can be set to form a priority list. The parsed prompt categories are compared with the priority lists to determine the priority of the first prompt task.

[0057] For example, in some embodiments of this disclosure, user response behavior data for each prompt task from different data sources can be pre-stored. By analyzing the user's historical behavior for different prompt tasks, the user's sensitivity to a specific data source can be dynamically assessed, thereby assigning a more reasonable priority to newly received prompt tasks from the same data source or of the same prompt type.

[0058] In one example, user response behavior data can include basic behavior, time characteristics, contextual information, and prompt task attributes. Basic behavior can include clicking the prompt, closing the prompt, ignoring (no action), and swiping to skip; time characteristics can include the prompt appearance time and user dwell time; contextual information includes the application's current execution flow (e.g., checkout process); and prompt task attributes can include the prompt type. Based on this response behavior data, a priority calculation formula can be established for a specific data source or prompt type:

[0059]

[0060] Among them, P S Priority score; α, β, γ, δ: weighting coefficients (must satisfy α + β + γ + δ = 1); B: basic behavioral score; T base : Basic value of time characteristic; λ: Time decay rate; Δ t： The interval between the current time and the time the action occurred (units consistent with λ); C: Contextual information score; P t : This indicates the score for the task attributes.

[0061] The base weight can be preset based on the attributes of the prompt task; the user preference weight is set according to different user behaviors, which can include positive behaviors, negative behaviors, neutral behaviors, and long-term preferences. The time decay factor can set a higher weight for recent behaviors, such as setting the weight of behaviors from the past 7 days to 1, 1 month ago to 0.5, and 3 months ago to 0.2.

[0062] It should be noted that different priority score ranges correspond to different priorities. After a new prompt task is obtained, its score is calculated according to the priority calculation formula mentioned above, and the priority of the new prompt task is determined based on the score.

[0063] For example, in some embodiments of this disclosure, if the priority of a first prompt task is determined based on the data source to which each first prompt task belongs, at least a portion of the key data, and the user's response behavior characteristics to at least one second prompt task previously received from the same data source, it can be calculated using, for example, the following formula:

[0064] P=α·B(u,s)+β·C(t,k,c)+γ·S(s)

[0065] in,

[0066] P represents the final priority score (0-100) of the prompt task;

[0067] α, β, γ represent dimensional weight coefficients (for example, the initial values ​​can be set to 0.5, 0.3, and 0.2 respectively);

[0068] B(u,s) represents the user behavior feature score (based on user u's interaction history with data source s).

[0069] C(t,k,c) represents the content criticality score (based on cue type t, topic k, and content c).

[0070] S(s) represents the scores from different data sources.

[0071] For example, in some embodiments of this disclosure, the priority engine module 130 performs the following operations to determine the priority of the first prompt task: based on the data source to which the first prompt task belongs and at least a portion of the key data, it determines that there is a contextual relationship with the current execution flow of the digital currency application; based on the contextual relationship, it determines that the priority of the first prompt task is the highest in the prompt queue. In this example, "contextual relationship" refers to the dynamic correlation between the first prompt task and the current running state of the digital currency application, the user operation flow, and the data environment. Specifically, it may include aspects such as execution flow correlation, data source dependency, timing sensitivity, and / or state machine mapping.

[0072] In some embodiments of this disclosure, the execution flow correlation is determined by the following operation: determining whether the first prompt task is triggered by the core task currently being performed by the user (such as an identity verification pop-up during the payment process). If the prompt content of the first prompt task directly affects the continuation of the current operation chain (such as the absence of the pop-up causing the process to be interrupted), then it is considered that there is a contextual relationship.

[0073] For example, regarding data source dependencies, the following dimensions can be used to determine whether a contextual relationship exists: whether the first prompt task directly interacts with the data in the current application execution flow (e.g., the data of the pop-up task is a subsequent processing result of the current flow data); whether the data source type (e.g., database, API, file) that the pop-up task depends on is consistent with the current flow, and whether the data structure (e.g., fields, format) is compatible; and whether the function of the pop-up task is closely related to the business logic of the current flow (e.g., in the payment process, if a pop-up task prompts payment failure and guides a retry, its dependent payment interface data source is consistent with the current flow, which is context-dependent).

[0074] In addition, the context relationship determination result can include strong context relationship, weak context relationship, and no context relationship. Strong context relationship: The pop-up task shares the same data source as the current process, and the data flow, structure, and business logic are highly matched (such as a payment result pop-up in a payment process). In this case, the pop-up should be displayed immediately and deeply interact with the current operation. Weak context relationship: The data source partially overlaps or the business logic is indirectly related (such as a pop-up recommending similar products when a user browses products). In this case, the pop-up priority can be adjusted according to user preferences.

[0075] For example, in terms of time sensitivity, it determines whether the first prompt task needs to be displayed within a specific time window. For instance, a "consumption bonus" pop-up that appears when a user makes a payment has a completely different timeliness value than one that appears after exiting the payment page.

[0076] For example, in a state machine mapping, an application may be in different states (such as active in the foreground / running in the background, logged in / not logged in). For example, a payment failure pop-up has the highest priority when the user is on the checkout page, but may be downgraded if the user has been redirected to another page.

[0077] Additionally, in some embodiments, the priority engine module 130 can access priority policies stored in the priority policy module 160, which represent a set of rules for assigning priorities to prompt tasks. These priority policies include multiple dimensions and the priorities corresponding to each dimension, or priority algorithms corresponding to different priority policies. For example, priorities corresponding to one or more of the following: data source, key data of the prompt message, and user response behavior characteristics, can be configured by the application developer.

[0078] For example, in some embodiments of this disclosure, the priority engine module 130 compares one or more of the following: the data source to which the first prompt task belongs, at least some of the key data, and the user's response behavior characteristics to a second prompt task previously received from the same data source, with a priority policy to determine the priority of the first prompt task.

[0079] For example, in one instance, a priority strategy can include multiple priority strategy tables, such as a single-dimensional priority strategy table and a multi-dimensional priority strategy table. An example of a single-dimensional priority strategy table is shown below:

[0080]

[0081] As an example of a multi-dimensional priority strategy table, it can be shown in the following table:

[0082]

[0083]

[0084] For example, in some embodiments of this disclosure, if the priority of the first prompt task conflicts with the priority of at least one prompt task in the task queue, the priority engine module 130 further determines the priority order between the first prompt task and the conflicting prompt task based on at least one of the following elements: the arrival time of the first prompt task and the conflicting prompt task; the metadata associated with each of the first prompt task and the conflicting prompt task; and the communication frequency with the data source to which each of the first prompt task and the conflicting prompt task belongs.

[0085] For example, in some embodiments, if there is a priority conflict between the priority of the first prompt task and the prompt tasks in the task queue, their arrival times are compared to determine their priority order in the task queue. For instance, the newest prompt task may be considered to have a higher priority than an older prompt task.

[0086] Additionally or alternatively, the metadata of the two entities can be compared to determine their priority. This could be based on the prompt type, data source, and / or predefined priority levels provided in the metadata. The communication frequency of their respective data sources can also be compared, with prompting tasks from higher-frequency data sources having higher priority than those from lower-frequency data sources.

[0087] For example, in some embodiments of this disclosure, the priority engine module 130, in response to a user's response to a prompt task displayed in the display window, removes the prompt task that the user has responded to from the prompt queue, and updates the priority order of the prompt tasks in the prompt queue according to the priority of the remaining prompt tasks in the prompt queue and / or the priority of at least one first prompt task received.

[0088] In one example, the priority order of each prompt task in the prompt queue can be updated according to a queue update strategy. The queue update strategy sets up an event-driven update mechanism and a hierarchical update strategy. The event-driven update mechanism includes user response events, which trigger the cleanup and reordering of the prompt queue; and new prompt arrival events, which trigger incremental insertion and partial reordering. The hierarchical update strategy includes a queue update mode: removing user-responded prompt tasks, recalculating the priority of affected pop-up tasks, and performing a full reordering of all prompt tasks in the prompt queue.

[0089] In addition, the user behavior monitoring module 150 can also record the user's response behavior data for each prompt task, and extract the response behavior characteristics of prompt tasks from the same data source when preset conditions are met.

[0090] Set up event tracking points on the application frontend to record response behavior data for each prompt task. This data can include display time, user operation time, and operation behavior (click, confirm, close, ignore, timeout). Then store this data in a user behavior database (150A), for example, in the form of an event log. Each record includes the prompt task ID, user ID, timestamp, and response behavior data. Calculate the response behavior characteristics of users for each data source regarding past prompt tasks, such as response rate, average response time, and pop-up type preference, to analyze user preferences and behavior patterns for different pop-ups. Furthermore, users can be categorized based on these response behavior characteristics. Preset conditions can include a defined time window, a task quantity threshold, and the timing of response behavior characteristics from at least one second prompt task from the same data source when determining the priority of a new first prompt task. When the set conditions are met, response behavior characteristics for prompt tasks from the same data source can be extracted for subsequent priority calculations of newly received prompts from the same data source.

[0091] For example, in some embodiments of this disclosure, the information display module 140 is configured to display each pop-up window in a pop-up window interface corresponding to the prompt type, according to the size and position of each pop-up window's priority.

[0092] In some embodiments, each prompt task is displayed in a display window position according to a predefined response prompt type, based on its relative priority. For some prompt tasks, even if they are of the same prompt type, their position and size in the display window may differ depending on their priority. For example, a prompt task with higher priority may be displayed in a larger size at the top and / or left of the display window. Furthermore, other visual characteristics, such as color, animation, text size / style, etc., can be modified to indicate the relative priority of each prompt task. Moreover, each prompt task can have any type of geometry, such as rectangle, ellipse, circle, irregular shape, or other shapes.

[0093] Figure 2 A flowchart illustrating a method for processing prompt information from multiple data sources according to at least one embodiment of the present disclosure is shown. The methods, process steps, or stages shown in the figures may be implemented as independent routines or processes, or as part of a larger routine or process. It should be noted that in other embodiments, each depicted process step or stage may be implemented as a device, method, or system including a processor executing a set of instructions. In some embodiments, method 200 is described with reference to the accompanying drawings described herein.

[0094] like Figure 2 As shown, in step S210, during the execution of the digital currency application, at least one first prompt message is received from different data sources.

[0095] In step S220, the first prompt information is parsed to determine the key data of the first prompt information, and the first prompt information is abstracted into a first prompt task based on the key data. The key data includes the prompt title, prompt content, and prompt type.

[0096] In step S230, the priority of each first prompt task and its priority order relative to other prompt tasks in the prompt queue are determined based on one or more of the following: the data source to which each first prompt task belongs, at least some of the key data, and the user's response behavior characteristics to at least one second prompt task previously received from the same data source. The prompt queue is then updated.

[0097] In step S240, each prompt task is displayed in the corresponding display window of the cryptocurrency application according to its priority order in the prompt queue and based on its key information. The display windows for different prompt types are geographically separated.

[0098] For example, in some embodiments of this disclosure, the step of determining the priority of the first prompt task includes: determining a contextual relationship with the current execution flow of the application based on the data source to which the first prompt task belongs and at least some of the key data; and determining, based on the contextual relationship, that the priority of the first prompt task is the highest in the prompt queue.

[0099] For example, in some embodiments of this disclosure, method 200 further includes: recording user response behavior data for each prompt task, and extracting response behavior features of prompt tasks from the same data source when a preset condition is met.

[0100] For example, in some embodiments of this disclosure, the step of displaying each prompt task in the display window includes: displaying each prompt task in the display window corresponding to the prompt type, according to the size and position of each prompt task based on its priority.

[0101] For example, in some embodiments of this disclosure, the step of determining the priority of the first prompt task includes: comparing one or more of the following: the data source to which the first prompt task belongs, at least a portion of the key data, and the user's response behavior characteristics to a second prompt task previously received from the same data source, with a priority strategy; wherein the priority strategy includes multiple dimensions and the priorities corresponding to each dimension, or priority algorithms corresponding to different priority strategies.

[0102] For example, in some embodiments of this disclosure, the step of determining the priority order of the first prompt task further includes: if the priority of the first prompt task conflicts with the priority of at least one prompt task in the task queue, then the priority order between the first prompt task and the conflicting prompt task is further determined according to at least one of the following elements: the arrival time of the first prompt task and the conflicting prompt task; the metadata associated with each of the first prompt task and the conflicting prompt task; and the communication frequency with the data source to which each of the first prompt task and the conflicting prompt task belongs.

[0103] In one example, firstly, during the execution of the cryptocurrency application, it listens for push notifications from multiple data sources by registering a broadcast receiver. Upon receiving pop-up data, it parses the data using JSON parsing. Next, it prioritizes the received pop-ups, using Java's PriorityQueue to sort them according to pre-defined priority rules and strategies, and checks the current pop-up status (whether there is a pop-up currently being displayed). Then, it uses PriorityQueue's poll() method to retrieve the pop-up task, displays it, and removes it from the queue. After the pop-up operation is complete, PriorityQueue's poll() method retrieves the next pop-up task from the queue for display. Through this specific implementation, priority management of pop-ups in Android multi-data source scenarios can be achieved, ensuring that important pop-ups are displayed to the user in a timely manner, improving the user experience.

[0104] Figure 3 A flowchart illustrating a method for processing prompt information from multiple data sources according to at least one embodiment of the present disclosure is shown. Figure 3 As shown, with Figure 2 The same or similar steps are indicated by the same label and will not be described in detail; only the steps that are different will be explained.

[0105] The method further includes step S310, in response to the user's response to the prompt task displayed in the display window, removing the prompt task that the user has responded to from the prompt queue, and updating the priority order of the prompt tasks in the prompt queue according to the priority of the remaining prompt tasks in the prompt queue and / or the priority of at least one first prompt task received.

[0106] The additional aspects of the multi-data source prompt information processing methods 200 and 300 according to at least one embodiment of the present disclosure can correspond to the additional aspects of the multi-data source prompt information processing system 1000 according to at least one embodiment of the present disclosure. Therefore, the technical effects of the additional aspects of the multi-data source prompt information processing system 1000 according to at least one embodiment of the present disclosure can also be mapped to the additional aspects of the multi-data source prompt information processing methods 200 and 300 according to at least one embodiment of the present disclosure, which will not be repeated here.

[0107] Figure 4 A block diagram of another multi-data source prompt information processing system according to at least one embodiment of the present disclosure is shown. Figure 4 As shown, with Figure 1 Modules that are the same or similar are represented by the same label and will not be described in detail; only the modules that are different will be explained.

[0108] like Figure 4As shown, the prompt information processing system 1000' also includes a prompt task monitoring module 170. This module 170 is configured to perform real-time monitoring of different transaction flow nodes of the digital currency application, collect and monitor the UI interaction status data of each transaction flow node during the execution of the digital currency application to see if UI page rendering errors occur, and execute the corresponding error handling strategy when UI page rendering errors occur.

[0109] Figure 5 A flowchart illustrating a method 500 for monitoring and processing error prompts in a digital currency transaction process according to at least one embodiment of the present disclosure is shown. In the digital currency transaction process, the digital currency app receives different transaction instructions and / or transaction data at different transaction flow nodes and executes corresponding digital currency transaction functions. During function execution, the prompt task monitoring module 170 execution method 500 can monitor and process detected error prompt tasks in real time and continuously.

[0110] The following is for reference. Figure 5 The method steps of the embodiments of this disclosure will be explained.

[0111] In step S510, in response to the command to start the digital currency transaction process, real-time monitoring of each transaction flow node in the digital currency transaction process is initiated.

[0112] Upon receiving a command to initiate a digital currency transaction process, such as when a user clicks on the payment component in the digital currency app, a monitoring mechanism is activated to conduct real-time and continuous monitoring of each transaction flow node in the digital currency transaction process, tracking transaction-related data, UI page rendering status, page navigation path, and / or background transaction status at each node.

[0113] In some embodiments of this disclosure, transaction process errors may include UI page rendering errors. For example, before making a payment on a payment page, user identity needs to be verified. Correspondingly, a pop-up window for verifying user identity should be displayed in this scenario, but instead, a "Consumption Red Packet" pop-up window is displayed, thus confirming that a pop-up window display error has occurred.

[0114] In step S520, UI interaction status data of each transaction flow node during the execution of the digital currency application are collected.

[0115] In one example, the UI interaction status data of each transaction flow node includes user click behavior on the operation interface, the content and format of input information, page visibility data (including page loading time, display status of page elements, loading order), and various interaction feedback information between the user and the digital currency app.

[0116] For example, for pop-up UI interfaces, after detecting the pop-up area, multimodal data of the pop-up area is extracted to obtain the key data of the pop-up page (pop-up title, pop-up content). In addition, the contextual features of the pop-up can also be obtained by parsing logs and tracking user operation flow.

[0117] In step S530, based on the current transaction flow node, the corresponding AI sub-model in the digital currency AI big model is triggered to perform the following operations: extract feature data from the collected data, and detect whether a UI page rendering error has occurred based on the extracted feature data.

[0118] For example, in some embodiments of this disclosure, the large-scale digital currency AI model is a model trained according to the data model and flow graph of the digital currency transaction process, used for error detection and processing at different transaction flow nodes. It includes multiple AI sub-models corresponding to each transaction flow node, with each sub-model handling different categories of transaction errors within that node. For example, error categories include UI page rendering errors, page redirection errors, query interface errors, and payment interface errors.

[0119] For example, in one example disclosed herein, the large-scale digital currency AI model can include a transaction initialization AI sub-model, a transaction order AI sub-model, a transaction authentication AI sub-model, a transaction risk control verification AI sub-model, and a transaction result AI sub-model for each transaction flow node. When the large-scale digital currency AI model receives various types of collected data, the management and distribution model within the model performs business analysis and deconstruction, identifies the current transaction flow node, and distributes the data collected from different transaction flow nodes to the corresponding AI sub-models for processing. For instance, the corresponding sub-models in the large-scale digital currency AI model extract key information from various types of data for analysis to determine whether UI page rendering errors have occurred. Furthermore, the management and distribution model includes the flow logic of the transaction flow nodes, determining the sub-model to which the data or process belongs based on this logic, and distributing the collected data to the specific sub-model for processing.

[0120] It should be noted that, in some embodiments of this disclosure, after creating each AI sub-model, a mapping relationship between each transaction flow node and the AI ​​sub-model can be established. Based on this mapping relationship, the data collected by the transaction flow node can be accurately distributed to the corresponding AI sub-model of the transaction flow node for processing. For example, page redirection data in the payment process can be distributed to the transaction order AI sub-model.

[0121] For example, extracted feature data is a crucial data element for determining the status of cryptocurrency transactions. For instance, during page rendering, the transaction initialization AI sub-model determines the expected status based on current order data and wallet details. It then combines this with the button states and text displayed on the rendered page to determine the actual displayed status (extracted feature data). If the actual displayed status matches the expected status, the UI page is fine; otherwise, it indicates a UI page rendering error. Similarly, for example, if the current data corresponds to a sufficient wallet balance, clicking the "Pay Now" button should trigger identity authentication in the cryptocurrency app, displaying a payment password or biometric authentication pop-up. If authentication is not performed and the user is redirected to the "Top Up Now" page, the transaction order AI sub-model, by analyzing the extracted feature data, will determine that a UI page rendering error has occurred.

[0122] For example, in one scenario, transaction rules can be used to analyze whether transaction anomalies have occurred. These rules can include transaction threshold rules, transaction process logic rules, and UI rendering flow rules. Reasonable threshold ranges for various transaction metrics are set based on historical transaction data and business experience. For instance, according to established transaction business processes and logic, each transaction is checked to see if it follows the correct steps. For example, in transactions involving multi-step verification, if a key verification step is missing but the transaction is successfully submitted, it is considered a transaction process anomaly. For UI rendering pages, flow rules for the rendering page are also defined.

[0123] In step S540, the AI ​​sub-model responds to the UI page rendering error and outputs an error handling strategy to deal with the transaction process error.

[0124] For example, in some embodiments of this disclosure, the error handling strategy output in step S540 may include an operation scenario change strategy, a page refresh strategy, and a payment method substitution strategy. The operation scenario change strategy includes recommending possible digital currency operation scenarios (represented by available scenario IDs) and guiding the user to switch to the recommended scenario. Operation scenarios may include different scenarios such as main scan payment, scan-based password-free payment, scan-based verification payment, mobile number transfer, and wallet ID transfer. For example, if an error occurs in the "main scan payment" operation scenario, a prompt message "You can use the payment code for password-free payment" can be displayed to the user for selection. The page refresh strategy includes an instruction indicating that the user needs to manually refresh the page and guiding the user to refresh the page, such as "Please try refreshing the page to resolve the current problem" and displaying a refresh button. The alternative payment method strategy includes an identifier indicating the alternative payment method and guiding the user to switch to that alternative payment method, such as "Please switch to XX payment method." Of course, the above is only an example, and those skilled in the art can modify the above error handling strategy according to actual needs; this disclosure does not limit it.

[0125] In step S550, an error handling strategy is executed, and a bootstrap message is generated for the transaction flow node.

[0126] Furthermore, in some embodiments of this disclosure, in step S550, if the error category of the transaction process error is a UI page rendering error or a page redirection error, a guidance message prompting the user to change the operation scenario or refresh the page can be generated. If the error category of the transaction process error is a query interface error or a payment interface error, an error handling strategy is executed, and a guidance message recommending possible alternative payment methods to the user is generated.

[0127] Specifically, if there are errors in the front-end UI rendering or redirection path, try to find other solutions to automatically prompt the user to change the operation scenario or refresh the page to complete the transaction and avoid transaction interruption.

[0128] It should be noted that the above application scenarios are merely exemplary in order to describe one or more aspects of this disclosure in specific scenarios. However, these aspects are not essential, and various modifications can be made to the application scenario. The embodiments of this disclosure are not limited.

[0129] At least some embodiments of this disclosure also provide an electronic device. Figure 6 A schematic diagram of an electronic device 600 according to at least one embodiment of the present disclosure is shown.

[0130] like Figure 6As shown, the electronic device 600 includes one or more processors 610 and a memory 620. The memory 620 includes one or more computer program modules 621. The one or more computer program modules 621 are stored in the memory 620 and configured to be executed by the processor 610. These computer program modules 621 include instructions for executing a method 300 for processing digital currency hard wallet transactions according to at least one embodiment of the present disclosure and its additional aspects. When executed by the processor 610, they can perform one or more steps of a multi-data source prompting information processing method 200, 300, a prompting task monitoring processing method 500, and its additional aspects according to at least one embodiment of the present disclosure. The memory 620 and the processor 610 can be interconnected via a bus system and / or other forms of connection mechanisms (not shown). For example, the bus can be a Peripheral Component Interconnect Standard (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc.

[0131] For example, processor 610 may be a central processing unit (CPU), a digital signal processor (DSP), or other processing unit with data processing and / or program execution capabilities, such as a field-programmable gate array (FPGA). Processor 610 may be a general-purpose processor or a special-purpose processor, capable of controlling other components in electronic device 600 to perform desired functions.

[0132] Exemplarily, memory 620 may include any combination of one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, erasable programmable read-only memory (EPROM), portable compact disc read-only memory (CD-ROM), USB memory, flash memory, etc. One or more computer program modules 621 may be stored on the computer-readable storage medium, and processor 610 may run one or more computer program modules 621 to implement various functions of electronic device 600. The computer program modules include multiple computer-executable instructions. Various application programs and various data, as well as various data used and / or generated by the application programs, may also be stored in the computer-readable storage medium.

[0133] For example, electronic device 600 may also include input devices such as touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, and gyroscopes; output devices such as liquid crystal displays, speakers, and vibrators; storage devices such as magnetic tapes and hard disks (HDDs or SDDs); and communication devices such as network interface cards like LAN cards and modems. The communication devices allow electronic device 600 to communicate wirelessly or wiredly with other devices to exchange data and perform communication processing via networks such as the Internet. A drive is connected to the I / O interface as needed. Removable storage media, such as disks, optical disks, magneto-optical disks, and semiconductor memories, are installed on the drive as needed so that computer programs read from them can be installed into the storage device as required.

[0134] For example, the electronic device 600 may further include a peripheral interface (not shown in the figure). This peripheral interface can be of various types, such as a USB interface, a Lightning interface, etc. The communication device can communicate wirelessly with networks and other devices, such as the Internet, intranets and / or wireless networks such as cellular telephone networks, wireless local area networks (LANs) and / or metropolitan area networks (MANs). Wireless communication can use any of a variety of communication standards, protocols, and technologies, including but not limited to Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), Wideband Code Division Multiple Access (W-CDMA), Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Bluetooth, Wi-Fi (e.g., based on IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, and / or IEEE 802.11n standards), Voice over Internet Protocol (VoIP), Wi-MAX, protocols for email, instant messaging, and / or Short Message Service (SMS), or any other suitable communication protocol.

[0135] The electronic device 600 may be, for example, a system-on-a-chip (SOC) or a device including the SOC. For instance, it can be any device such as a mobile phone, tablet computer, laptop computer, e-reader, game console, television, digital photo frame, navigator, home appliance, communication base station, industrial controller, server, etc., or any combination of data processing devices and hardware. The embodiments of this disclosure do not limit this. The specific functions and technical effects of the electronic device 600 can be found in the description above of the multi-data source prompt information processing method 200, 300, prompt task monitoring processing method 500, and their additional aspects according to at least one embodiment of this disclosure, and will not be repeated here.

[0136] Figure 7A schematic diagram of a computer-readable storage medium 700 according to at least one embodiment of the present disclosure is shown.

[0137] like Figure 7 As shown, a non-transitory computer instruction 710 is stored on a computer-readable storage medium 700. When executed by a processor, the non-transitory computer instruction 710 performs one or more steps of the multi-data source prompt information processing method 200, 300, prompt task monitoring processing method 500 and its additional aspects as described above.

[0138] For example, when the program code is read by a computer, the computer can execute the program code stored in the computer storage medium to perform one or more steps to implement, for example, the multi-data source prompt information processing method 200, 300, prompt task monitoring processing method 500 and its additional aspects according to at least one embodiment of the present disclosure.

[0139] For example, the computer-readable storage medium may include a memory card of a smartphone, a storage component of a tablet computer, a hard disk of a personal computer, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), portable compact disc read-only memory (CD-ROM), flash memory, and other computer-readable media or any combination thereof.

[0140] At least some of the embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other.

[0141] It should be noted that, in this document, relational terms such as "first," "second," etc., are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0142] The following points should be noted regarding this disclosure:

[0143] (1) The accompanying drawings of the embodiments of this disclosure only involve the structures involved in the embodiments of this disclosure. Other structures can be referred to the general design.

[0144] (2) Where there is no conflict, the embodiments of this disclosure and the features in the embodiments can be combined with each other to obtain new embodiments.

[0145] The above description is merely an exemplary embodiment of this disclosure and is not intended to limit the scope of protection of this disclosure, which is determined by the appended claims.

Claims

1. A method for processing prompt information from multiple data sources, the method comprising: During the execution of the digital currency application, at least one initial notification message is received from different data sources; The first prompt information is parsed to determine the key data of the first prompt information, and the first prompt information is abstracted into a first prompt task based on the key data, wherein the key data includes prompt title, prompt content and prompt type; Based on one or more of the following: the data source to which each first prompt task belongs, at least some of the key data, and the user's response behavior characteristics to at least one second prompt task previously received from the same data source, determine the priority of the first prompt task and its priority order relative to other prompt tasks in the prompt queue, and update the prompt queue. According to the priority order of each prompt task in the prompt queue, and based on the key information of each prompt task, each prompt task is displayed in the corresponding display window in the digital currency application.

2. The method according to claim 1, wherein, The step of determining the priority of the first prompt task includes: Based on the data source to which the first prompt task belongs and at least some of the key data, it is determined that there is a contextual relationship with the current execution flow of the application; Based on the context relationship, the priority of the first prompt task is determined to be the highest in the prompt queue.

3. The method according to claim 1, wherein, The method further includes: In response to the user's response to the prompt tasks displayed in the display window, the prompt tasks that the user has responded to are removed from the prompt queue, and the priority order of the prompt tasks in the prompt queue is updated according to the priority of the remaining prompt tasks in the prompt queue and / or the priority of at least one first prompt task received.

4. The method according to claim 3, wherein, The method further includes: Record the user's response behavior data for each prompt task, and extract the response behavior features of prompt tasks from the same data source when a preset condition is met.

5. The method according to claim 1, wherein, The steps for each prompted task are displayed in the presentation window, including: Within the display window corresponding to the prompt type, each prompt task is displayed according to its size and position based on its priority.

6. The method according to claim 1, wherein, The step of determining the priority of the first prompt task includes: The first prompt task's data source, at least some of the key data, and one or more of the user's response behavior characteristics to the second prompt task previously received from the same data source are compared with the priority strategy. The priority strategy includes multiple dimensions and the priorities corresponding to each dimension, or priority algorithms corresponding to different priority strategies.

7. The method according to claim 1, wherein, The step of determining the priority order of the first prompting task further includes: If the priority of the first prompt task conflicts with the priority of at least one prompt task in the task queue, the priority order between the first prompt task and the conflicting prompt task is further determined based on at least one of the following elements: the arrival time of the first prompt task and the conflicting prompt task; the metadata associated with each of the first prompt task and the conflicting prompt task; and the communication frequency with the data source to which each of the first prompt task and the conflicting prompt task belongs.

8. A multi-data source prompt information processing system, comprising: The communication module is configured to receive at least one first prompt message from different data sources during the execution of the digital currency application; The information parsing module is configured to parse the first prompt information and determine the key data of the first prompt information, and abstract the first prompt information into a first prompt task based on the key data, wherein the key data includes prompt title, prompt content and prompt type; The priority engine module is configured to determine the priority of the first prompt task and its priority order relative to existing prompt tasks in the prompt queue based on one or more of the following: the data source to which each first prompt task belongs, at least some key data, and the user's response behavior characteristics to at least one second prompt task previously received from the same data source; and to update the prompt queue. The information display module is configured to display each prompt task in the corresponding display window of the digital currency application according to the priority order of each prompt task in the prompt queue and based on the key data of each prompt task.

9. An electronic device comprising: One or more processors; Memory, which stores one or more computer program modules. The one or more computer program modules are configured to be executed by the one or more processors to implement the method according to any one of claims 1-7.

10. A computer-readable storage medium for storing non-transitory computer-readable instructions, wherein, When a non-transitory computer-readable instruction is executed by one or more processors, it is used to implement the method according to any one of claims 1-7.

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