Information push methods, devices, equipment, media and program products

Abstract

This disclosure provides an information push method relating to the fields of artificial intelligence, finance, or other fields. The method includes: acquiring user maintenance data, the maintenance data including the user's static information data and dynamic behavior data; matching N target maintenance rules from a set of maintenance rules based on the maintenance data, wherein the set of maintenance rules is pre-obtained based on interaction data between M maintenance personnel and a target user group, and N and M are integers greater than or equal to 1; determining the triggering conditions for each of the N target maintenance rules; and pushing information conforming to that target maintenance rule to the user when the maintenance data meets the triggering condition of any one of the N target maintenance rules.

Description

Technical Field

[0001] This disclosure relates to the fields of artificial intelligence, finance, or other fields, and more specifically, to an information push method, apparatus, device, medium, and program product. Background Technology

[0002] Pushing information to users via SMS, email, apps, phone calls, or public accounts is an important service and marketing method. Current technologies typically combine human and automated methods. For example, bank account managers may inform customers of promotional activities or business reminders via phone calls, online chat, or SMS, or programs may automatically send information about new activities and offers to a large number of users through full-scale message push.

[0003] In realizing the inventive concept of this disclosure, the inventors discovered that different account managers using different customer maintenance methods lead to different customer maintenance results, which may reduce user experience. Using automated messaging makes it difficult to accurately determine marketing timing, and sending a large amount of invalid information wastes computing resources. Summary of the Invention

[0004] In view of the above problems, this disclosure provides information push methods, apparatus, equipment, media and program products.

[0005] According to a first aspect of this disclosure, an information push method is provided, characterized by comprising: acquiring user maintenance data, the maintenance data including the user's static information data and dynamic behavior data; matching N target maintenance rules from a set of maintenance rules based on the maintenance data, wherein the set of maintenance rules is pre-obtained based on interaction data between M maintenance personnel and a target user group, and N and M are integers greater than or equal to 1; determining the triggering conditions for each of the N target maintenance rules; and pushing information conforming to that target maintenance rule to the user when the maintenance data satisfies the triggering condition of any one of the N target maintenance rules.

[0006] According to embodiments of this disclosure, obtaining the maintenance rule set in advance includes: obtaining interaction data between each of the M maintenance personnel and the target user group; obtaining feedback data that meets preset conditions from the interaction data; obtaining corresponding behavioral data from the interaction data based on the feedback data, wherein the behavioral data characterizes the maintenance behavior of at least one maintenance personnel, and the feedback data characterizes the feedback behavior of at least one target user in response to the maintenance behavior; and performing cluster analysis on the behavioral data to obtain the maintenance rule set.

[0007] According to an embodiment of this disclosure, each target maintenance rule includes S candidate push periods, which are obtained by clustering feedback times in the feedback data, where S is an integer greater than or equal to 1; when the maintenance data meets the triggering condition of any one of the N target maintenance rules, pushing information that conforms to the target maintenance rule to the user includes pushing information in at least one of the S candidate push periods.

[0008] According to embodiments of this disclosure, the S candidate push time periods are obtained by clustering feedback times in the feedback data, including: the S candidate push time periods corresponding to the user are obtained by clustering feedback times in their feedback data.

[0009] According to an embodiment of this disclosure, pushing information in at least one of the S candidate push periods includes: obtaining available push resources for each of the S candidate push periods, wherein the available resource data includes hardware resources and software resources for performing information push; and determining the at least one push period to push information to the user based on the available push resources for each period.

[0010] According to an embodiment of this disclosure, when the maintenance data meets the triggering conditions of at least two of the N target maintenance rules, information that conforms to the at least two target maintenance rules is pushed to the user in a combined manner.

[0011] According to embodiments of this disclosure, merging and pushing information conforming to the at least two target maintenance rules to the user includes: obtaining at least two information templates corresponding one-to-one with the at least two target maintenance rules; extracting information elements to be pushed from the maintenance data; generating merged push information based on the information elements and the at least two information templates; and pushing the merged push information to the user.

[0012] According to embodiments of this disclosure, pre-assigning a priority to each maintenance rule in the set of maintenance rules and pushing the merged push information to the user includes: determining the highest priority rule among the at least two target maintenance rules; and pushing information to at least one candidate time period corresponding to the highest priority rule.

[0013] Another aspect of this disclosure provides an information push device, characterized in that it includes: a data acquisition module, used to acquire user maintenance data, the maintenance data including the user's static information data and dynamic behavior data; a rule matching module, used to match N target maintenance rules from a set of maintenance rules based on the maintenance data, wherein the set of maintenance rules is pre-obtained based on the interaction data of M maintenance personnel with a target user group, and N and M are integers greater than or equal to 1; a trigger determination module, used to determine the trigger conditions of each of the N target maintenance rules; and an information push module, used to push information conforming to any one of the N target maintenance rules to the user when the maintenance data meets the trigger condition of any one of the target maintenance rules.

[0014] Another aspect of this disclosure provides an electronic device, including: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors perform the method as described above.

[0015] Another aspect of this disclosure provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, causes the processor to perform the method described above.

[0016] Another aspect of this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the method described above.

[0017] The above one or more embodiments have the following beneficial effects: By using big data analysis, a set of maintenance rules is pre-obtained based on the interaction data between M maintenance personnel and the target user group, and a rule-based approach is used to solidify high-performing customer maintenance methods. Furthermore, target maintenance rules are matched based on users' static information data and dynamic behavior data, enabling personalized information pushes. This allows for a unified customer maintenance approach through automated rule matching and personalized information pushes, accurately grasping marketing timing, avoiding the sending of invalid information to save computing resources, and improving user experience. Attached Figure Description

[0018] The foregoing contents, as well as other objects, features, and advantages of this disclosure, will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0019] Figure 1 This diagram illustrates an application scenario of information push according to an embodiment of the present disclosure.

[0020] Figure 2 A flowchart illustrating an information push method according to an embodiment of the present disclosure is shown schematically;

[0021] Figure 3 A flowchart illustrating a pre-obtained set of maintenance rules according to an embodiment of this disclosure is shown schematically;

[0022] Figure 4 A flowchart illustrating the selection of a time period for pushing information is shown schematically according to an embodiment of the present disclosure;

[0023] Figure 5 A flowchart illustrating the merged push according to an embodiment of this disclosure is shown schematically;

[0024] Figure 6 A flowchart illustrating a merge push according to another embodiment of this disclosure is shown schematically;

[0025] Figure 7 This schematic diagram illustrates the structure of an information push device according to an embodiment of the present invention.

[0026] Figure 8 A block diagram schematically illustrates an electronic device suitable for implementing an information push method according to an embodiment of the present disclosure. Detailed Implementation

[0027] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0028] In the technical solution of this invention, the user information (including but not limited to user personal information, user image information, user device information, such as location information) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of related data all comply with relevant laws, regulations, and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entry points for users to choose to authorize or refuse.

[0029] In scenarios involving automated decision-making using personal information, the methods, devices, and systems provided in this invention offer users corresponding entry points for choosing to agree to or reject the automated decision-making results. If the user chooses to reject, the process proceeds to the expert decision-making stage. Here, "automated decision-making" refers to the activity of automatically analyzing and evaluating an individual's behavioral habits, interests, or economic, health, and credit status through computer programs, and then making a decision. Here, "expert decision-making" refers to the activity of making decisions by personnel who specialize in a particular field, possess specialized experience, knowledge, and skills, and have reached a certain level of professional expertise.

[0030] Figure 1 The diagram illustrates an application scenario for information push notifications according to embodiments of this disclosure. It should be noted that... Figure 1 The examples shown are merely examples to illustrate the application of the embodiments of this disclosure, in order to help those skilled in the art understand the technical content of this disclosure. However, they do not mean that the embodiments of this disclosure cannot be used in other devices, systems, environments, or scenarios.

[0031] like Figure 1 As shown, application scenario 100 according to this embodiment may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as a medium for providing a communication link between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0032] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).

[0033] Terminal devices 101, 102, and 103 can be various electronic devices with displays and web browsing capabilities, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0034] Server 105 can be a server providing various services, such as a backend management server supporting websites browsed by users using terminal devices 101, 102, and 103 (this is just an example). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices. For example, server 105 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud computing, network services, and middleware services.

[0035] It should be noted that the information push method provided in this disclosure can generally be executed by at least one of a terminal device or a server. Accordingly, the information push device provided in this disclosure can generally be installed in at least one of a terminal device or a server.

[0036] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0037] The following will be based on Figure 1 The described scene, through Figures 2-6 The information push method of the present disclosure embodiments will be described in detail.

[0038] Figure 2 A flowchart illustrating an information push method according to an embodiment of the present disclosure is shown.

[0039] like Figure 2 As shown, this embodiment includes:

[0040] During operation S210, user maintenance data is obtained, which includes static information data and dynamic behavior data of the user.

[0041] For example, maintenance data refers to data needed during customer maintenance. Static information data includes basic user information such as gender, age, occupation, customer level, and bank card type. Dynamic behavioral data includes behavioral data generated by users during service usage, such as browsing history, transaction records, and login frequency, which are updated as users' activities change. Examples include bank users' online banking transfer records and account query frequency.

[0042] In operation S220, N target maintenance rules are matched from the maintenance rule set based on maintenance data. The maintenance rule set is obtained in advance based on the interaction data between M maintenance personnel and the target user group, where N and M are integers greater than or equal to 1.

[0043] The maintenance rule set includes one or more pre-defined rules, which are pre-defined based on interaction data between maintenance personnel and the target user group. Maintenance rules matching the user's status are selected from the pre-defined rule set to execute personalized information pushes.

[0044] For example, interactive data can include data from the following four types of wiki scenarios:

[0045] 1. Emotional Connection: Send reminders such as holiday greetings, weather care, and birthday wishes to customers on important holidays including but not limited to the Spring Festival, solar terms such as the Great Cold and the End of Heat, and customers' birthdays.

[0046] 2. Important Time Periods: During special times, including but not limited to Singles' Day and before the year-end bonuses of payroll clients, marketing of financing products and promotional activities will be sent via SMS.

[0047] 3. Condition-triggered marketing: This includes, but is not limited to, benefit reminders, credit card binding spending and target achievement marketing, mobile banking activation promotion, and marketing for withdrawals of 50,000 yuan or more. Through RPA robots, streaming platforms, and other means, we monitor customer benefits, credit card and mobile banking usage, and withdrawals of 50,000 yuan or more in real time, sending marketing and reminder messages to customers when the corresponding trigger conditions are met.

[0048] 4. Important Reminders: These include, but are not limited to, credit card annual fee reminders, credit card expiration and card issuance reminders, points redemption reminders, document validity reminders, and supplementary registration reminders. Relevant information is retrieved via RPA robots, and corresponding SMS reminders are sent.

[0049] In the process of matching N target maintenance rules, corresponding scenario keywords can be extracted from the maintenance data based on the above scenario, and the corresponding target maintenance rules can be searched in the maintenance rule set.

[0050] In operation S230, determine the triggering conditions for each of the N target maintenance rules.

[0051] For example, a target maintenance rule might be: "If Customer A has conducted foreign currency transactions within the past month and their foreign currency account balance is below a certain amount, then push relevant information about foreign exchange purchases." The triggering conditions are those set in the target maintenance rule. When a user's maintenance data meets these conditions, the information push will be triggered. For example, the triggering conditions might be Customer A's foreign currency transaction activity and account balance status. When the bank system detects that the user meets the triggering conditions of the foreign exchange purchase push rule, it automatically sends a foreign exchange purchase notification to the user.

[0052] In operation S240, when the maintenance data meets the triggering condition of any one of the N target maintenance rules, information that conforms to that target maintenance rule is pushed to the user.

[0053] Taking a banking scenario as an example, with user authorization or full authorization from all parties, the bank's backend database collects user-filled personal information forms (static information data) and user online transaction records, login frequency, etc. (dynamic behavioral data). Database query languages, such as SQL, are used to extract and process user data. Data analytics applications can execute machine learning algorithms to analyze user data and match corresponding maintenance rules. For example, classification algorithms, such as Support Vector Machines (SVM) or decision trees, can be used for rule prediction and matching. The rule engine parses the maintenance rules, extracts condition parameters, and monitors whether user data meets these conditions. When a condition is triggered, a request is sent to the push notification system. The push notification system automatically sends relevant information to the user. For example, in business scenarios with high data timeliness requirements (such as promptly pushing "Credit Card Automatic Repayment Agreement" marketing information to customers who have just completed credit card repayment), the system monitors new customer transaction data generation and enables the streaming platform to automatically synchronize and obtain customer information to be pushed in this scenario. For business scenarios where data timeliness is generally required (such as sending "birthday wishes" to a customer on their birthday), the customer information to be pushed in this scenario is obtained through a database (data is generally stored in the database with a time limit of T+1 or T+2).

[0054] According to embodiments of this disclosure, a set of maintenance rules is pre-determined based on the interaction data between M maintenance personnel and the target user group using big data analysis. This rule-based approach solidifies effective customer maintenance methods. Furthermore, target maintenance rules are matched based on users' static information data and dynamic behavioral data to deliver personalized information pushes. This achieves a unified customer maintenance approach through automated rule matching and personalized information pushes, accurately pinpointing marketing opportunities, avoiding the sending of invalid information to save computing resources, and improving user experience.

[0055] Figure 3 A flowchart illustrating a pre-obtained set of maintenance rules according to an embodiment of this disclosure is shown schematically.

[0056] like Figure 3 As shown, this embodiment includes:

[0057] By operating S310, we obtain the interaction data between M maintenance personnel and the target user group.

[0058] The target user group includes multiple users. By collecting interaction data based on the target user group served by one or more maintenance personnel, the group behavior can be analyzed through big data to obtain high-performing maintenance methods.

[0059] When operating S320, feedback data that meets preset conditions is obtained from interactive data.

[0060] In operation S330, corresponding behavioral data is obtained from the interaction data based on the feedback data. The behavioral data represents the maintenance behavior of at least one maintenance personnel, and the feedback data represents the feedback behavior of at least one target user in response to the maintenance behavior.

[0061] Interaction data includes data on interactions between maintenance personnel and the user group. For example, maintenance actions include greetings, birthday wishes, and promotional reminders sent by maintenance personnel to users at a specific time. Behavioral data includes time, communication method, communication duration, communication content, and script templates. Feedback actions include user responses after receiving maintenance actions, such as expressing gratitude, inquiring about activity details, or immediately hanging up. Feedback data includes the content of user expressions, sentiment analysis results, communication duration, and service evaluations. Sentiment analysis results can be predicted using trained machine learning models based on the content and tone of user expressions.

[0062] Feedback data can be filtered, for example, removing negative user reviews. The preset condition can be the number of positive reviews received by the same maintenance action that meets the preset number of people (e.g., 1000 people, just an example), or it can be that each maintenance action is rated, with the preset condition being the rating standard value (e.g., 90 points out of 100 points, just an example).

[0063] By operating S340, cluster analysis is performed on the behavioral data to obtain a set of maintenance rules.

[0064] For example, first, select or construct a set of features representing maintenance behaviors from the behavioral data. Then, choose a suitable clustering algorithm, such as K-means, hierarchical clustering, or DBSCAN. Next, taking K-means as an example, the number of clusters can be predetermined using methods such as the Elbow Method or the Silhouette Coefficient. Then, run the clustering algorithm to divide the behavioral data into multiple clusters. Each cluster represents a set of similar maintenance behaviors. Next, analyze each cluster to extract maintenance rules, such as through statistical analysis of the data within the cluster, to identify common features and patterns. Finally, verify the effectiveness of the rules obtained from the clustering analysis by practically applying these rules and collecting feedback. Adjust and optimize based on the feedback. Construct the extracted set of maintenance rules into a form that can be used by the system, such as a decision tree or rule base.

[0065] According to embodiments of this disclosure, a set of maintenance rules is obtained by clustering analysis of behavioral data from interactions between maintenance personnel and users. This allows for more precise formulation of push rules based on the relationship between maintenance behaviors and feedback. This not only improves the personalization and accuracy of information pushes but also enhances the user experience.

[0066] In other embodiments, some rules in the maintenance rule set may also come from non-interactive data such as business processes, industry experience, or data analysis. For example, if big data analysis identifies a certain time period as a peak time for active users to make transactions, then pushing relevant activity information to users during this time period may yield better results than during other time periods. Therefore, maintenance rules can not only come from direct user feedback but also from other sources, such as industry experience and data analysis.

[0067] In some embodiments, each target maintenance rule includes S candidate push periods, which are obtained by clustering feedback times in the feedback data, where S is an integer greater than or equal to 1.

[0068] When the maintenance data meets the trigger condition of any one of the N target maintenance rules, information that conforms to that target maintenance rule will be pushed to the user, including:

[0069] Information is pushed out during at least one of the S candidate push time slots. The time slot can be selected based on computer resources, the number of users to be pushed to, or a random strategy.

[0070] For example, candidate push notification times include time periods that might be suitable for sending push notifications. These times are determined by analyzing feedback data such as user activity times. For instance, if data shows that users frequently check bank information between 8 PM and 9 PM, this time period might be selected as a candidate push notification time. Or, if interaction data shows that calling or sending messages to users between 5 AM and 6 AM on weekends typically results in negative feedback, then that time period would be excluded.

[0071] Feedback time clustering refers to analyzing the times of user feedback and dividing these times into different groups (clusters) to find the time periods when users were most likely to have viewed the information. For example, the K-means algorithm can be used to cluster the times when users viewed information to determine candidate push notification times. The feedback time can include a specific time, such as 7:30 AM, or it can include the difference between the time the information arrived and the time it was viewed.

[0072] According to embodiments of this disclosure, candidate push time periods are determined through cluster analysis, which can be optimized for specific user activity times, thereby increasing the probability of users reading information. This can improve message open rates and user interaction rates, thus improving the efficiency of information delivery. It avoids pushing information during user inactivity times, reducing user interference and improving user experience. It allows for multiple push time periods, providing greater flexibility and adaptability for information push strategies.

[0073] In some embodiments, the S candidate push time periods are obtained by clustering the feedback time in the feedback data, including: the S candidate push time periods corresponding to a user are obtained by clustering the feedback time in their feedback data.

[0074] For example, Customer A has been using Bank A's services for over 10 years, accumulating a large amount of feedback data. Clustering algorithms can be used to process this data to identify S candidate push notification time slots. These time slots represent the times when Customer A is most likely to view and interact with information. This allows for tailoring the information push to each user's needs, making it more precise, increasing user open and interaction rates, and thus enhancing the effectiveness of the push notifications.

[0075] In some embodiments, since user behavior patterns may change over time and in different contexts, push notification times can be dynamically updated to match the latest user needs. For example, the latest user feedback data can be retrieved periodically (e.g., weekly or monthly), cluster analysis can be performed again, and push notification times can be updated accordingly.

[0076] Figure 4 A flowchart illustrating the selection of a time period for pushing information is shown in accordance with an embodiment of this disclosure.

[0077] like Figure 4 As shown, this embodiment includes pushing information during at least one of the S candidate push time periods, specifically including:

[0078] In operation S410, the available push resources for each of the S candidate push time periods are obtained. The available resource data includes hardware and software resources used to perform information push.

[0079] For example, hardware resources typically include server processing power (such as CPU and GPU utilization), hard disk storage space, memory usage, and network device status. Software resources typically include system bandwidth usage, average server load, latency of specific services (such as database queries), and the number of tasks pending in the system queue. For instance, in a push system, the resource monitoring module monitors the usage of hardware and software resources in real time. The decision-making module is used to determine the push period based on resource conditions.

[0080] In operation S420, based on the available push resources for each time period, at least one time period is determined to push information to the user.

[0081] Continuing with the banking scenario as an example, the bank's marketing department wants to push credit product information to users. First, they analyze the times when users are most likely to log into the bank's app, such as 6 PM to 7 PM and 8 PM to 9 PM. These times are selected as candidate push notification times. Next, they assess the available push notification resources during 6 PM to 7 PM, finding that while user activity is high during this period, hardware resources (such as server capacity and network bandwidth) may experience bottlenecks due to peak network traffic. To dynamically allocate resources based on real-time resource utilization and task urgency, they choose to push notifications during 8 PM to 9 PM, when user activity is high but other tasks are less frequent. This ensures that the message arrives when users are most likely to open the app, and the push process is not affected by resource limitations.

[0082] According to embodiments of this disclosure, by taking into account the availability of hardware and software resources required for information push, the success rate of information push and the user response rate can be improved, while ensuring the efficient execution of push tasks.

[0083] In some embodiments, to avoid excessively disturbing customers, a limit is placed on the number of times a certain number of customers can be reached. A limited number of text messages are sent only to customers who meet multiple reach conditions or the same reach condition is met multiple times within a certain time period. This is implemented as follows:

[0084] 1. All historical customer contact information for each business scenario is entered into the target directory on the local server according to the scenario.

[0085] 2. Through interconnection (triggered by executing HQL scripts via Xshell), new customer data is periodically (database data is generally migrated once a day, and streaming data is generally migrated in real time) from the remote (head office) HADOOP / MPP cluster to the local cluster server. Key data is encrypted or anonymized. The storage time of data on the local server can be limited to the maximum time period required for "anti-interference" (e.g., in a scenario where "customers can only be reached once a quarter", the storage period is one quarter).

[0086] 3. Based on the "Do Not Disturb" requirements (such as "Can be reached a total of 2 times" or "Can be reached once within 6 months"), match the new data with the historical data. If the customer meets the "Do Not Disturb" conditions, filter the data; otherwise, retain it.

[0087] In some embodiments, to avoid excessive disruption to customers, when maintenance data meets the triggering conditions of at least two of the N target maintenance rules, information that meets at least two target maintenance rules is pushed to the user in a combined manner.

[0088] For example, in a banking scenario, several maintenance rules are set, such as "account balance below 1000 yuan" and "no transaction activity in the past month" as conditions to trigger a user to receive maintenance information. The bank's system periodically checks each user's account and records the met rule conditions. Customer A's account balance drops to 950 yuan, and there have been no transactions in the past month, which meets the conditions of the above two rules. The bank's push system will automatically merge the two pieces of information and send a push message to Customer A, reminding the user to pay attention to their account balance and noting the inactive status of their account.

[0089] According to embodiments of this disclosure, the push strategy is optimized to reduce the frequency of information reception for users and to reduce user-perceived interference by merging push notifications. This saves users time, as they only need to read and respond to one message, while also ensuring effective information delivery.

[0090] Figure 5 A flowchart illustrating the merged push according to an embodiment of this disclosure is shown schematically.

[0091] like Figure 5 As shown, this embodiment pushes information to users that conforms to at least two target maintenance rules in a combined manner, including:

[0092] When operating S510, obtain at least two information templates corresponding to at least two target maintenance rules.

[0093] For example, a message template refers to a pre-designed message framework used to send a specific type of message. Each template corresponds to one or more maintenance rules. For instance, for a rule regarding insufficient balance, a message template might contain the text: "Your account balance is below [amount], please top up promptly."

[0094] When operating the S520, extract the information elements to be pushed from the maintenance data.

[0095] Information elements refer to specific information points extracted from user maintenance data, used to populate information templates. For example, if a user's account balance is 900 yuan, then "900 yuan" is an information element.

[0096] When operating S530, merged push information is generated based on information elements and at least two information templates.

[0097] When operating the S540, push merged push information to users.

[0098] Merged push notifications refer to the process of combining multiple messages into a single message and then pushing it to the user.

[0099] For example, a script template can be set up in advance according to the business scenario corresponding to the maintenance rules and imported into the system. The template has reserved variable positions (such as customer name, product name, product expiration date, etc.), so that when the script template is triggered, the information elements corresponding to different customers can be filled into the template respectively.

[0100] For example, if Customer A triggers two rules: "Account balance below 1000 yuan" and "No transaction activity in the past month," the bank's push notification system will perform the following operations: First, it retrieves the information templates corresponding to these two rules. The first template might be a balance warning template, and the second a user account inactivity warning template. Then, it extracts information elements from Customer A's maintenance data, such as an account balance of 950 yuan and the last transaction date being one month ago. Next, using these information elements, the system generates a combined message based on a preset template: "Dear Customer A, your account balance is below 950 yuan, and you have not had any transaction activity in the past month. For your account security, please log in to the bank's app to check and take necessary actions."

[0101] Next, determine the current data capacity and push volume of the customer outreach channel (such as intelligent outbound calling, SMS, mobile banking, WeChat for Business, etc.). Taking SMS as an example, if the remaining daily outbound SMS volume for the online channel (near real-time) is X messages, and the current data to be pushed is greater than X messages, then the top X customers are selected according to a certain set condition (such as sorting customers by salary amount from highest to lowest), and stored in the local server's historical push record (as shown in section 1 of the data storage and filtering section). The remaining customers are automatically discarded and not recorded in the historical push record. If the current data to be pushed is less than or equal to X messages, then all customers are retained and stored in the local server's historical push record.

[0102] Then, via API, the system calls the target customer outreach channels, such as smart outbound calling, bulk SMS, online SMS (near real-time), mobile banking, and WeChat Work, to push the processed information to the customer. Finally, it synchronously sends message reminders to the customer's account manager's internal devices in real time, informing them of the customer information push status, and automatically retains all outbound call logs.

[0103] According to embodiments of this disclosure, by merging push notifications, users can avoid the inconvenience of receiving multiple messages, simplifying their information processing workflow. By using information templates and elements, push notifications tailored to individual user circumstances can be generated, improving the standardization of information.

[0104] When multiple rules are triggered simultaneously, a priority strategy is defined to determine which rules are more important. This priority strategy may be set based on factors such as the business importance of the rule, user preferences, etc. For example, the priority of rules can be dynamically adjusted based on data such as the number of times a rule is triggered, the conversion rate of the rule, and user feedback related to the rule.

[0105] In some embodiments, during operation S530, merged push information is generated according to priority, and information corresponding to rules with higher priority is placed at the beginning and has a larger content proportion.

[0106] Figure 6 A flowchart illustrating a merge push according to another embodiment of this disclosure is shown schematically.

[0107] like Figure 6 As shown, this embodiment is one example of operation S540, which pre-assigns a priority to each maintenance rule in the maintenance rule set, and pushes merged push information to the user, including:

[0108] In operation S610, determine the highest priority rule among at least two target maintenance rules.

[0109] When operating S620, information is pushed during at least one candidate time period corresponding to the highest priority rule.

[0110] The highest priority rule refers to the rule with the highest priority among all triggered target maintenance rules. By identifying the highest priority rule and its corresponding push time period, important information can be conveyed more effectively, allowing users to allocate sufficient attention to important information.

[0111] Based on the above information push method, this disclosure also provides an information push device, which is described below in conjunction with... Figure 7 To elaborate further.

[0112] Figure 7 A schematic diagram of the information push device according to an embodiment of the present invention is shown.

[0113] like Figure 7 The information push device 700 may include a data acquisition module 710, a rule matching module 720, a trigger determination module 730, and an information push module 740.

[0114] The data acquisition module 710 can perform operation S210 to acquire user maintenance data, which includes user static information data and dynamic behavior data.

[0115] The rule matching module 720 can perform operation S220, which is used to match N target maintenance rules from the maintenance rule set based on maintenance data. The maintenance rule set is obtained in advance based on the interaction data between M maintenance personnel and the target user group, and N and M are integers greater than or equal to 1.

[0116] The trigger determination module 730 can perform operation S230 to determine the trigger conditions for each of the N target maintenance rules.

[0117] The information push module 740 can perform operation S240, which is used to push information that conforms to the target maintenance rule to the user when the maintenance data meets the triggering condition of any one of the N target maintenance rules.

[0118] In some embodiments, the information push module 740 may perform operations S410 to S420, operations S510 to S540, and operations S610 to S620, which will not be described in detail here.

[0119] In some embodiments, the information push device 700 may further include a rule extraction module, which can perform operations S310 to S340, which will not be described in detail here.

[0120] For any parts not mentioned in the apparatus section, please refer to the various embodiments of the above method for understanding. That is, the apparatus section includes modules for performing each step of any of the method embodiments described above. Furthermore, the implementation methods, technical problems solved, functions achieved, and technical effects of each module / unit / subunit in the apparatus section embodiments are the same as or similar to the implementation methods, technical problems solved, functions achieved, and technical effects of the corresponding steps in the method section embodiments, and will not be repeated here.

[0121] According to embodiments of this disclosure, any and multiple modules among the data acquisition module 710, rule matching module 720, trigger determination module 730, and information push module 740 can be combined into one module, or any one of these modules can be split into multiple modules. Alternatively, at least some of the functions of one or more of these modules can be combined with at least some of the functions of other modules and implemented in one module.

[0122] According to embodiments of this disclosure, at least one of the data acquisition module 710, rule matching module 720, trigger determination module 730, and information push module 740 can be at least partially implemented as a hardware circuit, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or any other reasonable method of integrating or packaging the circuit, or implemented in software, hardware, or firmware, or in any suitable combination of any of these three methods. Alternatively, at least one of the data acquisition module 710, rule matching module 720, trigger determination module 730, and information push module 740 can be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.

[0123] Figure 8A block diagram schematically illustrates an electronic device suitable for implementing an information push method according to an embodiment of the present disclosure.

[0124] like Figure 8 As shown, an electronic device 800 according to an embodiment of this disclosure includes a processor 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage portion 808 into a random access memory (RAM) 803. The processor 801 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 801 may also include onboard memory for caching purposes. The processor 801 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of this disclosure.

[0125] RAM 803 stores various programs and data required for the operation of electronic device 800. Processor 801, ROM 802, and RAM 803 are interconnected via bus 804. Processor 801 performs various operations of the method flow according to embodiments of the present disclosure by executing programs in ROM 802 and / or RAM 803. It should be noted that programs may also be stored in one or more memories other than ROM 802 and RAM 803. Processor 801 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in one or more memories.

[0126] According to embodiments of this disclosure, the electronic device 800 may further include an input / output (I / O) interface 805, which is also connected to a bus 804. The electronic device 800 may also include one or more of the following components connected to the I / O interface 805: an input section 806 including a keyboard, mouse, etc.; an output section 807 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card such as a LAN card, modem, etc. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the I / O interface 805 as needed. A removable medium 811, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 810 as needed so that computer programs read from it can be installed into the storage section 808 as needed.

[0127] This disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments, or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of this disclosure.

[0128] According to embodiments of this disclosure, the computer-readable storage medium can be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this disclosure, the computer-readable storage medium may include ROM 802 and / or RAM 803 and / or one or more memories other than ROM 802 and RAM 803 described above.

[0129] Embodiments of this disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code is used to cause the computer system to implement the methods provided in the embodiments of this disclosure.

[0130] When the computer program is executed by the processor 801, it performs the functions defined in the system / apparatus of this disclosure embodiments. According to embodiments of this disclosure, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0131] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and may be downloaded and installed via the communication section 809, and / or installed from a removable medium 811. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0132] In such an embodiment, the computer program can be downloaded and installed from a network via communication section 809, and / or installed from removable medium 811. When the computer program is executed by processor 801, it performs the functions defined in the system of this disclosure embodiment. According to embodiments of this disclosure, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0133] According to embodiments of this disclosure, program code for executing the computer programs provided in embodiments of this disclosure can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can execute entirely on a user's computing device, partially on a user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0134] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0135] Those skilled in the art will understand that the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways, even if such combinations or combinations are not explicitly described in this disclosure. In particular, the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0136] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. The scope of this disclosure is defined by the appended claims and their equivalents. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.

Claims

1. An information push method, characterized in that, include: Obtain user maintenance data, which includes the user's static information data and dynamic behavior data; Based on the maintenance data, N target maintenance rules are matched from the maintenance rule set, wherein the maintenance rule set is obtained in advance based on the interaction data between M maintenance personnel and the target user group, and N and M are integers greater than or equal to 1; Determine the triggering conditions for each of the N target maintenance rules; When the maintenance data meets the triggering condition of any one of the N target maintenance rules, information that conforms to that target maintenance rule is pushed to the user.

2. The method according to claim 1, characterized in that, The set of maintenance rules obtained in advance includes: Obtain the interaction data between each of the M maintenance personnel and the target user group; Obtain feedback data that meets preset conditions from the interaction data; Based on the feedback data, corresponding behavioral data is obtained from the interaction data, wherein the behavioral data characterizes the maintenance behavior of at least one maintenance personnel, and the feedback data characterizes the feedback behavior of at least one target user in response to the maintenance behavior. Cluster analysis is performed on the behavioral data to obtain the maintenance rule set.

3. The method according to claim 2, characterized in that, Each target maintenance rule includes S candidate push time periods, which are obtained by clustering the feedback time in the feedback data, where S is an integer greater than or equal to 1. When the maintenance data meets the triggering condition of any one of the N target maintenance rules, information conforming to that target maintenance rule is pushed to the user, including: Information is pushed during at least one of the S candidate push periods.

4. The method according to claim 3, characterized in that, The S candidate push time periods are obtained by clustering the feedback time in the feedback data, including: The S candidate push time periods corresponding to each user are obtained by clustering the feedback time in their feedback data.

5. The method according to claim 3, characterized in that, At least one of the S candidate push time periods includes push information for the following time periods: Obtain the available push resources for each of the S candidate push periods, wherein the available resource data includes hardware and software resources used to perform information push; Based on the available push resources for each time period, determine the at least one time period for which information will be pushed to the user.

6. The method according to claim 3 or 5, characterized in that, When the maintenance data meets the triggering conditions of at least two of the N target maintenance rules, information that conforms to the at least two target maintenance rules is pushed to the user in a combined manner.

7. The method according to claim 6, characterized in that, The process of merging and pushing information that conforms to at least two target maintenance rules to the user includes: Obtain at least two information templates that correspond one-to-one with the at least two target maintenance rules; Extract the information elements to be pushed from the maintenance data; Generate merged push information based on the information elements and the at least two information templates; The merged push information is pushed to the user.

8. The method according to claim 7, characterized in that, Priority is pre-assigned to each maintenance rule in the set of maintenance rules, and the merged push information is pushed to the user, including: Determine the highest priority rule among the at least two target maintenance rules; Information is pushed to at least one candidate time period corresponding to the highest priority rule.

9. An information push device, characterized in that, include: The data acquisition module is used to acquire the user's maintenance data, which includes the user's static information data and dynamic behavior data; The rule matching module is used to match N target maintenance rules from the maintenance rule set based on the maintenance data, wherein the maintenance rule set is obtained in advance based on the interaction data between M maintenance personnel and the target user group, and N and M are integers greater than or equal to 1; The trigger determination module is used to determine the trigger conditions for each of the N target maintenance rules; The information push module is used to push information that conforms to the target maintenance rule to the user when the maintenance data meets the triggering condition of any one of the N target maintenance rules.

10. An electronic device, comprising: One or more processors; Memory, used to store one or more computer programs. The characteristic feature is that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 8.

12. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 8.

Images