Intelligent contact system and method based on user characteristics

Abstract

The present invention discloses an intelligent reach system and method based on user characteristics, which belongs to the field of message push technology. The system generates user portraits through user behavior data, including labels such as reach channels, reach time and reach content, and generates preference features based on user click behavior, such as channel preference, time preference, algorithm preference and copy preference. The system calculates the weights of each reach channel, time and content package through the factor optimization module, and selects the optimal combination to generate a message sending plan. If the message fails to be sent successfully, the system will automatically re-select and generate a new sending plan and resend it. The present invention can realize accurate and personalized message push based on the user's historical behavior and preferences, significantly improve the message reach rate and user response rate, and is suitable for scenarios such as e-commerce platforms and social media that require large-scale personalized push.

Description

Technical Field

[0001] The present invention belongs to the field of message push technology, and specifically relates to an intelligent reaching system and method based on user characteristics. Background Art

[0002] The rapid development of internet technology has revolutionized the way information is disseminated. Traditional messaging models are gradually being replaced by intelligent, personalized push notification systems. Especially with the widespread adoption of mobile internet and social media, the channels, timing, and content of information received by users have become more diverse. However, despite the progress made in intelligent push notification systems, many challenges remain in practical application, particularly in improving message reach and user response rates.

[0003] Limitations of traditional message push methods:

[0004] In traditional message push systems, information is usually sent to users through fixed channels, fixed times, and unified content. Although this method is simple and easy to implement, it has the following significant problems: Single channel limitation: Traditional push systems often rely on a single push channel, such as SMS, email, or APP notifications. The use of this single channel can easily lead to information fatigue in users, and may even cause the information to fail to reach them effectively due to different user preferences. For example, some users may prefer to receive information through social media rather than email. Fixed time push: Traditional systems usually send messages to all users at fixed time points, ignoring the activity of different users in different time periods. This "one-size-fits-all" push strategy often fails to capture the best time for users to receive information, resulting in a large amount of information being ignored or missed. Content homogeneity: Traditional push systems lack in-depth exploration of user interests and needs, and usually send the same or similar content to all users. This lack of personalized recommendations not only reduces the effectiveness of messages, but may also cause user disgust, thereby affecting brand image. The current development status of intelligent message push

[0005] To address these challenges, intelligent, personalized push messaging technologies have been widely adopted in recent years. Leveraging technologies such as big data analysis, machine learning, and artificial intelligence, intelligent push messaging systems can make precise recommendations based on user behavior data and preferences, thereby increasing information reach and user engagement. Modern intelligent push messaging systems now support multiple channels, including SMS, email, app notifications, and social media. By selecting the most appropriate channel based on historical user behavior data, they can significantly increase the likelihood of messages being opened and read. Based on user profiles and behavioral data analysis, intelligent push messaging systems can tailor content to each user. For example, by analyzing the types of messages a user has clicked on in the past, they can predict topics they may be interested in in the future and provide precise recommendations. Intelligent push messaging systems can dynamically adjust push strategies based on real-time data. For example, if a message fails to reach its target user within the scheduled timeframe, the system can automatically select a suboptimal alternative to resend, ensuring the message ultimately reaches the user.

[0006] Despite significant progress in smart push technology, practical applications still face numerous challenges. Most current smart push systems still face the following challenges: While many existing smart push systems can implement multi-channel, multi-timepoint, and personalized content recommendations, they often lack detailed management of the entire process in practice. For example, when multiple messages need to be delivered simultaneously, selecting the optimal combination (i.e., which channel, at which time, to send which content) remains a challenge. Existing systems often require manual configuration of multiple campaigns to determine when and through which channel to send which content. This not only increases operational costs but is also prone to configuration errors and omissions. When sending multiple messages to a target user group, existing technologies often rely on manual recommendations rather than automatically selecting the optimal delivery plan for each user. This approach is not only inefficient but can also result in some important information not reaching its intended target users in a timely manner. For example, in some cases, if a message fails to be delivered or is ignored, existing systems cannot automatically select a suboptimal plan for resending, resulting in message loss. While some smart push systems can optimize to a certain extent based on historical data, most still lack real-time dynamic optimization mechanisms. That is to say, when user behavior changes (such as changes in receiving preferences or active time periods), existing systems are often unable to adjust their push strategies in a timely manner, thus affecting the effectiveness of information reach.

[0007] As competition in the internet market becomes increasingly fierce, major platforms and businesses are hoping to increase user engagement and conversion rates by improving message reach. Therefore, an efficient and accurate intelligent reach system is crucial for businesses. Future development trends will focus on the following areas:

[0008] Comprehensive, multi-dimensional data collection and analysis: To achieve more accurate personalized recommendations, future intelligent reach systems will need to possess more robust data collection and analysis capabilities. These systems must not only collect basic behavioral data (such as click-through rates and open rates), but also incorporate data from multiple dimensions, such as geographic location, device type, and social connections, to generate a more comprehensive and accurate profile of each user.

[0009] Automated Decision-Making and Optimization: Future intelligent reach systems will rely even more heavily on automated decision-making and optimization mechanisms. Using machine learning algorithms, the system automatically selects the optimal combination for each user (including the best channel, optimal timing, and optimal content) and continuously adjusts strategies based on real-time feedback to ensure each message reaches its target audience in the most efficient way.

[0010] Seamless cross-platform and multi-terminal connectivity: With the development of the Internet of Things and 5G technology, future information dissemination will no longer be limited to a single terminal or platform. Intelligent reach systems must seamlessly connect across platforms and multiple terminals to ensure that users receive relevant information in a timely manner, regardless of their location or device.

[0011] In summary, while existing technologies have addressed some of the issues with traditional message push to a certain extent, they still present numerous deficiencies. Therefore, there is a need for an intelligent user-feature-based reach method and system that comprehensively optimizes multiple dimensions, such as channels, timing, and content, to achieve more efficient and accurate information delivery, thereby improving message reach and user engagement. This is also the problem addressed by the present invention. Summary of the Invention

[0012] Problem to be solved

[0013] To address the problems of low smart message reach and insufficient push accuracy in the existing technology, the present invention provides an improved user feature-based smart reach system and method, aiming to improve the effectiveness and accuracy of message reach. It has the following advantages and innovations:

[0014] Multi-dimensional user portrait generation:

[0015] The present invention uses a user profile module to generate user contact channel tags, contact time tags, and contact content tags based on user behavior data (such as browsing, clicking, logging in, etc.). These tags can accurately reflect user preferences, making subsequent message push more personalized and accurate.

[0016] Dynamic preference generation and real-time updates:

[0017] The preference generation module dynamically generates and updates user preferences for channel, time, algorithm, and content based on their click-through behavior on historical messages. Through iterative updates, the system continuously optimizes push strategies to ensure that each push maximizes the user's current interests and needs.

[0018] Factor selection mechanism:

[0019] -The factor selection module calculates the weights of each reach channel, reach time, and content package, and selects the optimal combination to generate a message delivery plan. This module includes:

[0020] -Channel optimization unit: Calculates the weight of each channel based on user channel preference and platform normalized click-through rate.

[0021] -Content package selection unit: matches the user algorithm preference features and copywriting preference features with the corresponding features in the content package to calculate the content package weight.

[0022] -Timing optimization unit: Calculate the weight of each time period based on the user's time preference characteristics and the platform's normalized click-through rate.

[0023] -Through these weight calculations, the system can select the optimal combination in multiple dimensions, thereby greatly improving the accuracy of message push.

[0024] Automatic reissue mechanism:

[0025] If a message fails to be delivered or doesn't actually reach the user, the system will re-generate a new delivery plan, ignoring time factors, and immediately resend it. This mechanism ensures that the message is ultimately delivered to the user, improving overall reach.

[0026] Smart push of multiple messages:

[0027] This invention supports multiple channels, multiple content packages, and multiple time points within a single campaign, intelligently selecting the optimal combination for push based on pre-set conditions. This avoids the tedious process of creating separate campaigns for each message, significantly improving operational efficiency.

[0028] Flexible scalability and security:

[0029] The system interacts with the central server through communication modules, supporting flexible data transmission and processing. The central server not only stores and analyzes data but also issues instructions to optimize the processing logic of each module while ensuring the security of data transmission.

[0030] This invention achieves precise control over the intelligent message delivery process by introducing multi-dimensional user profiles, a dynamically updated preference generation mechanism, and a factor selection module. Compared to existing technologies, this invention significantly improves message reach and user interaction rates while reducing operating costs.

[0031] Technical Solution

[0032] To solve the above problems, the present invention adopts the following technical solutions.

[0033] An intelligent reach system based on user characteristics, a user portrait module, is used to generate user reach channel tags, reach time tags, and reach content tags based on user behavior data;

[0034] The preference generation module is used to generate the user's channel preference characteristics, time preference characteristics, algorithm preference characteristics, and copywriting preference characteristics based on the user's click behavior on the message;

[0035] The factor selection module is used to calculate the weight of each reach channel, the weight of each reach time, and the weight of each content package based on the user preference characteristics, select the optimal reach channel, reach time, and content package combination, and generate a message sending plan, where:

[0036] The factor selection module includes:

[0037] The channel optimization unit is used to calculate the weight of each reach channel based on the user's channel preference characteristics and the normalized click-through rate of the platform channel;

[0038] A content package selection unit is used to match the algorithmic and textual characteristics of the content package with the user's algorithmic and textual characteristics, and calculate the weight of each content package;

[0039] The timing optimization unit is used to calculate the weight of each touch time based on the user's time preference characteristics and the platform time normalized click rate;

[0040] A combining unit, configured to generate a message sending plan that satisfies a preset number of messages by combining the reach channel weight, reach time weight, and content package weight in descending order of weight;

[0041] An execution module is configured to send a corresponding content package to a user at a corresponding reach time and through a corresponding reach channel according to the message sending plan; if the message fails to be sent or does not actually reach the user, a new message sending plan is generated based on the best approach, the reach time is ignored, and the message is resent;

[0042] The user portrait module, the preference generation module and the factor selection module are all connected to the central server through a communication module.

[0043] The communication module is used to implement data interaction between the user portrait module, preference generation module, and factor selection module and the central server, and the communication module includes:

[0044] A data sending unit is used to send the user contact channel label, contact time label and contact content label generated by the user portrait module, the channel preference feature, time preference feature, algorithm preference feature and copywriting preference feature generated by the preference generation module, and the contact channel weight, contact time weight and content package weight calculated by the factor selection module to the central server;

[0045] A data receiving unit, configured to receive processing instructions and data update information issued by a central server, so that the user profiling module, preference generation module, and factor selection module can process according to the latest instructions and data;

[0046] The communication control unit is used to manage and control the workflow of the data sending unit and the data receiving unit, ensuring the effective transmission and secure communication of data between each module and the central server.

[0047] The following reactive power regulation algorithm is used between the communication module and the central server: where r ij (i=1, 2, ..., k; j=1, 2, ..., 5) represents the degree of membership of data transmission between the i-th communication module and the j-th central server.

[0048] R (k×5) : The matrix represents the relationship between k communication modules and 5 central servers; r ij Membership: This formula represents the quality or priority of data transmission between the i-th communication module and the j-th central server. This formula is used to optimize load distribution during data transmission, ensuring that critical data is transmitted first when network load is high. By calculating membership, resource allocation can be dynamically adjusted to improve system responsiveness and stability.

[0049] The following algorithm formula for the adjustable capacity is used between the communication module and the factor selection module:

[0050]

[0051] in in The adjustable capacity provided for the transmitted data i in time period t, where V i t is the actual transmission speed of the transmitted data i in time period t, where is the upper limit of the regulation of the transmitted data in time period i, where is the lower limit of the data transmitted in time period i, where P it is the actual amount of data bytes transmitted in time period i, where E i is the capacity service provision for data i transmitted, where β is the daily transmission rate of data transmitted.

[0052] It is used to calculate the data adjustable capacity in each time period, and E i The overall capacity service supply is calculated by accumulating the adjustable capacity of all time periods. It is used to define and calculate E i That is, we first have the adjustable capacity for each period, and then accumulate these values ​​to get the total service supply.

[0053] Example parameters:

[0054] Time periods: 96

[0055] Actual conveying speed V i t : Random between 0.8 and 1.2

[0056] Adjustment upper limit

[0057] The lower limit of regulation is 50% of the maximum capacity

[0058] Actual data bytes P it : Randomly generated between minimum and maximum limits

[0059] Daily transmission rate β = 0.9

[0060] Calculation process

[0061] 1. Calculate the adjustable capacity for each time period:

[0062] For each time period, use the formula

[0063] 2. Calculate the total capacity service supply:

[0064] Use the formula to accumulate the adjustable capacity of all time periods to get the total supply E i =16.2.

[0065] The user portrait module includes:

[0066] Behavior data collection unit, used to collect user behavior data, including information on receiving, opening, and clicking messages;

[0067] A tag generation unit is configured to analyze and generate a user's contact channel tag, contact time tag, and contact content tag based on the behavior data, wherein:

[0068] The reach channel label indicates the user's preference for different channels;

[0069] The reach time label indicates the user's activity level in different time periods;

[0070] The access content tags indicate the user's interest preferences for different types of content.

[0071] Wherein, the preference generation module includes:

[0072] A channel preference generation unit is used to generate a user's channel preference feature based on the user's click behavior on messages from different reach channels, and iteratively update the feature based on the user's click rate;

[0073] A time preference generation unit is used to generate a user's time preference feature based on the user's click behavior on messages with different reach times, and iteratively update the feature based on the user's message click time;

[0074] An algorithm preference generation unit, configured to generate a user's algorithm preference feature based on the algorithm features in the content package corresponding to the message clicked by the user, and iteratively update the feature based on the user's click-through rate on content recommended by different algorithms;

[0075] The copy preference generation unit is used to generate the user's copy preference features based on the copy features in the content package corresponding to the message clicked by the user, and iteratively update the features based on the user's click rate for different copy styles.

[0076] Wherein, the central server includes:

[0077] The data processing unit is used to receive and store data from the communication module, including user portrait tags, user preference characteristics, weights of each contact channel, contact time weights, and content package weights;

[0078] The instruction control unit is used to generate and send processing instructions and data update information to the user profiling module, preference generation module and factor selection module;

[0079] Data analysis unit, used to analyze collected user data and optimize reach strategies and feature tag systems;

[0080] Security management unit, used to ensure the security of data during transmission and processing, and prevent data leakage and illegal access;

[0081] The load balancing unit is used to distribute data transmission tasks among multiple communication modules to improve the stability and efficiency of the system.

[0082] An intelligent reaching method based on user characteristics, comprising the following steps:

[0083] (1) User profile generation: Obtain user behavior data and generate user contact channel tags, contact time tags, and contact content tags based on the behavior data;

[0084] (2) Preference feature generation: Based on the user's click behavior on the message, the user's channel preference feature, time preference feature, algorithm preference feature, and copy preference feature are generated;

[0085] (3) Calculation of factor weights:

[0086] Calculate the weight of each reach channel based on the user's channel preference characteristics and the normalized click-through rate of the platform channel;

[0087] Calculate the weight of each touch time based on the user's time preference characteristics and the platform's time-normalized click-through rate;

[0088] Match the user's algorithm preference features and copywriting preference features with the algorithm features and copywriting features of the content package, and calculate the weight of each content package;

[0089] (4) Optimal combination selection: Based on the calculated reach channel weight, reach time weight, and content package weight, select the best combination from high to low weight to generate a message sending plan that meets the preset number of messages;

[0090] (5) Message sending: according to the message sending scheme, at the corresponding reach time, through the corresponding reach channel, send the corresponding content package to the user;

[0091] (6) Resending mechanism: If a message fails to be sent or does not actually reach the user, a new message sending plan is generated based on the best approach, ignoring the reach time, and the message is resent immediately;

[0092] (7) Data interaction: Data interaction with the central server is realized through the communication module, user portraits, preference characteristics and weight information are sent, and processing instructions and data update information from the central server are received to update and optimize the processing of each module.

[0093] In general: Figure 4 As shown in the figure, the intelligent reach system achieves accurate message push to users through four steps: layered profiling, user preference generation, factor selection, and final execution. The following is a summary and optimization suggestions for each step:

[0094] Step 1: Layered portrait labels

[0095] Objective: Based on the user's historical behavior data (such as reception status, opening rate, login habits, etc.), generate portrait labels of the user's contact channels, contact time and contact content.

[0096] Reach channel stratification: Select the optimal channel based on the user's reception and opening rates on different channels (such as Push, IM, and email).

[0097] Optimization suggestion: When selecting channels, not only should the user's historical preferences be considered, but also the current network status and channel congestion should be combined to dynamically adjust the push strategy.

[0098] Reach time stratification: Based on the user's historical login habits, the day is divided into multiple time slots (such as 8 o'clock, 9 o'clock, etc.), and the time period when the user is most active is selected for push.

[0099] Optimization suggestion: Further refine the time division, for example, into half-hour or 15-minute units, and make decisions based on the user's click behavior rather than login behavior to increase the message open rate.

[0100] Reach content stratification: Based on the user opening rate of different types of content (such as job recommendations, browsing similar jobs, etc.), select the most suitable content package for push.

[0101] Optimization suggestion: Introduce the concept of "content package". A content package contains algorithm features and copy features. Make decisions based on the user's open rate under different algorithms and copy to ensure that the pushed content is more accurate.

[0102] Step 2: Preference Generation

[0103] Objective: Based on users’ click behavior on messages, generate and update users’ preference characteristics in terms of access channels, access time, and access content.

[0104] Channel preference generation: Generate and iteratively update the user's channel preference based on the user's click behavior on messages from different channels.

[0105] Optimization suggestion: Introduce a real-time feedback mechanism to dynamically adjust users' channel preferences based on the click data after each push.

[0106] Time preference generation: Generate and update the user's time preference based on the specific time when the user clicks the message.

[0107] Optimization suggestion: Combine the optimal sending time for different types of messages (such as promotional information and reminder information) to further refine time preferences.

[0108] Content package preference generation: Generate and update the user's algorithm and copy preferences based on the algorithm features and copy features corresponding to when the user clicks on the message.

[0109] Optimization suggestion: Use machine learning models to analyze large amounts of historical data and automatically identify the algorithms and copywriting features that are most likely to attract user attention.

[0110] Step 3: Factor selection

[0111] Objective: By calculating the weight of each factor (channel, content package, timing), select the best combination from high to low to generate a complete message sending plan.

[0112] Channel factor selection: Calculate the weight of each channel based on the normalized click-through rates stored by the data team. If data for a channel is missing, the platform's overall data is used to supplement it.

[0113] Optimization suggestion: Introduce a real-time monitoring mechanism to dynamically adjust the weight of each channel according to the current network conditions to avoid overloading of a certain channel.

[0114] Content package elements selection:

[0115] Algorithmic weight calculation: The weight is calculated based on the intersection of the user's algorithmic features and the algorithmic features in the content package. For example, if a user has a high preference for "high-paying jobs," the content package containing the "high-paying" feature will receive a higher weight.

[0116] Content weight calculation: The weight is calculated based on the intersection of the user's content features and the content features in the content pack. For example, if a user prefers "urgent" content, content packs with this style of content will receive a higher weight.

[0117] Optimization suggestion: Continuously optimize the algorithm and copy matching strategy through A / B testing to improve the overall conversion rate.

[0118] Timing factor optimization: The weights of each time period are calculated based on the data stored on the current day. If data for a time period is missing, it is supplemented by data from the previous day or the platform as a whole.

[0119] Optimization suggestion: Further optimize the timing selection strategy by combining external factors (such as weather, holidays, etc.).

[0120] Combination unit selection: The system selects the optimal combination from each dimension to generate the first message, then selects the next optimal combination from the remaining elements to generate the second message, until the preset number of messages is met. Time and content packages within each dimension cannot be selected repeatedly, but channels can be selected repeatedly.

[0121] Optimization suggestion: Introduce a multi-round decision-making mechanism and dynamically adjust the subsequent message combination based on real-time feedback to improve the overall push effect.

[0122] Step 4: Implementation and execution

[0123] During actual execution, the system will derive the configured Smart Reach campaign into multiple sub-campaigns and, after selecting the best one, find the target sub-campaign to send the message. If a message fails to be sent for some reason, the system will re-generate a new delivery plan, ignoring time factors, and immediately resend it.

[0124] Optimization suggestions:

[0125] Introduce an intelligent reissue mechanism to select the most appropriate reissue method and time by analyzing the cause of failure (such as network problems, equipment problems, etc.).

[0126] Review and analyze the feedback data after each push, and continuously optimize the operation strategy and feature label system to improve the overall push effect.

[0127] Through four steps: layered profile tagging, preference generation, factor selection, and intelligent execution, this intelligent reach system delivers precise and efficient information push to users. The system not only automatically selects the optimal reach channel, time, and content, but also dynamically adjusts push strategies based on real-time feedback, significantly improving message reach and conversion rates.

[0128] Beneficial effects

[0129] Compared with the prior art, the present invention has the following beneficial effects:

[0130] Improve message reach and accuracy:

[0131] Existing push notifications often rely on a simple, fixed channel, fixed time, and fixed content package approach, lacking a deep understanding of individual user needs. This results in low message reach and user response rates. Traditional push notification methods typically require configuring multiple channels, times, and content packages for each campaign, and each push requires manual intervention, making them neither automated nor intelligent.

[0132] The present invention can generate user portraits based on user behavior data (such as browsing, clicking, logging in, etc.) by introducing a user portrait module, a preference generation module, and an element optimization module, and dynamically generate channel preference features, time preference features, algorithm preference features, and text preference features based on the user's click behavior on the message. Subsequently, the element optimization module calculates the channel weight, time weight, and content package weight to select the optimal combination for message sending. This intelligent push mechanism greatly improves the accuracy of message reach, ensuring that each message can reach the user at the most appropriate time through the most appropriate channel with content that best suits the user's interests, thereby significantly improving the overall message reach rate.

[0133] Dynamic update and iterative optimization:

[0134] The present invention features a dynamic update mechanism that continuously monitors and analyzes user click behavior, updating user preferences (such as channel preferences, time preferences, algorithm preferences, and copywriting preferences) in real time. This dynamic update mechanism enables the system to continuously optimize push strategies based on the latest user behavior data, ensuring that each push is most consistent with the user's current interests and needs.

[0135] Furthermore, the present invention features iterative optimization, continuously optimizing operational strategies and feature tagging systems through reanalysis of historical data (such as open rates and click-through rates). Over time, the system can more accurately predict user behavior, further improving push effectiveness. This continuous optimization mechanism ensures the system's stability and efficiency over the long term.

[0136] Multi-dimensional factor selection mechanism:

[0137] Traditional push methods usually only consider a single dimension (such as a fixed channel or fixed time) and ignore other important dimensions (such as content packages or algorithms). This invention introduces a multi-dimensional factor selection mechanism, comprehensively considering the three key factors of channel, time and content package, and assigning weights to each dimension. Specifically:

[0138] The channel optimization unit calculates the weight of each reach channel based on the user's channel preference characteristics and the platform's normalized click-through rate.

[0139] The timing optimization unit calculates the weight of each reach time period based on the user's time preference characteristics and the platform's normalized click-through rate.

[0140] The content package selection unit matches the user's algorithm preference features and copywriting preference features with the corresponding features in the content package to calculate the content package weight.

[0141] By calculating these weights, the system can select the optimal combination across multiple dimensions, significantly improving the accuracy of message push. Compared to traditional methods, this invention not only considers the delivery channel, but also comprehensively considers the timing of delivery and content matching, making the push process more personalized and intelligent.

[0142] Automatic reissue mechanism:

[0143] In existing technologies, if a message fails to be sent or doesn't actually reach the user, it typically needs to be manually resent. This not only increases operational costs but can also cause important messages to miss their optimal delivery time. This invention introduces an automatic resending mechanism. When a message fails to be sent or doesn't actually reach the user, the system automatically generates a new delivery plan based on the best strategy and immediately resends it, regardless of time constraints. This automatic resending mechanism ensures that every important message is ultimately delivered to the user, thereby improving the overall reliability of information delivery.

[0144] Improve operational efficiency and flexibility:

[0145] Traditional push methods often require creating a separate campaign for each message and manually configuring multiple channels, content packages, and timeframes. This not only consumes significant human resources but is also prone to errors. This invention utilizes an intelligent system to automatically push multiple messages, providing multiple channels, content packages, and timeframes within a single campaign. The system intelligently selects the optimal combination for push based on pre-set conditions, significantly simplifying operational processes and improving efficiency.

[0146] Furthermore, the present invention supports flexible expansion, allowing operators to adjust push strategies at any time based on business needs, such as modifying channel weights, adjusting content packages, or adding new time periods. The system can quickly respond to these adjustments and make corresponding optimizations to meet business needs in different scenarios.

[0147] Data security and stability assurance:

[0148] This invention uses communication modules to interact with a central server, ensuring data security during data transmission. The central server receives and stores data from each module, including user profile tags, preference characteristics, and factor weighting information. It also issues processing instructions to optimize each module's processing logic. Furthermore, this invention incorporates a load balancing unit to rationally distribute data transmission tasks across multiple communication modules, improving the system's data processing capabilities and stability in high-concurrency scenarios.

[0149] Improved personalized experience:

[0150] Existing push methods are often overly mechanical and fail to provide differentiated services for different types of users. This invention, through a refined strategy, treats each user as an individual, generates a personalized profile based on their historical behavior data, and dynamically adjusts the push strategy based on their current interests. This highly personalized service not only enhances the user experience but also strengthens user engagement with the platform, helping to increase business conversion rates.

[0151] Real-time feedback and closed-loop management:

[0152] This invention features real-time feedback. When a message is successfully delivered and opened, the system immediately collects relevant data and feeds it back to a central server. This real-time feedback data is used for subsequent analysis to further optimize push strategies. Furthermore, this invention employs a closed-loop management model, automating the entire process from data collection to strategy execution and result feedback, effectively reducing human intervention and improving work efficiency.

[0153] In summary, compared to existing technologies, this invention not only significantly improves the reach and accuracy of intelligent messages, but also achieves comprehensive control over the intelligent message delivery process through innovative mechanisms such as dynamic updates, factor selection, and multi-dimensional comprehensive considerations. Furthermore, this invention also features an automatic reissue mechanism, flexible scalability, and efficient data security, providing operators with an efficient, reliable, and intelligent information push solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0154] Figure 1 This is an architectural diagram of the user feature-based intelligent reach system of the present invention;

[0155] Figure 2 Flowchart of the behavioral event triggering of the present invention;

[0156] Figure 3 This is the intelligent access entity relationship diagram of the present invention;

[0157] Figure 4 This is a flowchart of the actual application experience of the user feature-based intelligent contact system of the present invention;

[0158] Figure 5 This is a schematic diagram of the user interface of the user feature-based intelligent contact system of the present invention;

[0159] Figure 6 This is the second user interface diagram of the user feature-based intelligent contact system of the present invention;

[0160] Figure 7 This is the third user interface diagram of the user feature-based intelligent contact system of the present invention;

[0161] Figure 8 This is the fourth user interface diagram of the user feature-based intelligent contact system of the present invention;

[0162] Figure 9 The process of the intelligent contact method based on user characteristics of the present invention Figure 1 ;

[0163] Figure 10 The process of the intelligent contact method based on user characteristics of the present invention Figure 2 ;

[0164] Figure 11 The process of the intelligent contact method based on user characteristics of the present invention Figure 3 ;

[0165] Figure 12 A block diagram of the user feature-based intelligent reach system of the present invention;

[0166] Figure 13This is a block diagram of the communication module of the user feature-based intelligent reach system of the present invention;

[0167] Figure 14 This is a block diagram of the user portrait module of the user feature-based intelligent reach system of the present invention;

[0168] Figure 15 A block diagram of a preference generation module of the user feature-based intelligent reach system of the present invention;

[0169] Figure 16 A block diagram of a central server of the user feature-based intelligent reach system of the present invention;

[0170] Figure 17 This is a flowchart of the user feature-based intelligent reaching method of the present invention. DETAILED DESCRIPTION

[0171] The present invention will be further described below with reference to specific embodiments.

[0172] Implementation Plan

[0173] Please refer to Figure 12-16 The present invention discloses an intelligent reaching system based on user characteristics, comprising:

[0174] The user portrait module is used to generate user contact channel labels, contact time labels, and contact content labels based on user behavior data.

[0175] Among them, reference Figure 14 , the user portrait module includes:

[0176] Behavior data collection unit, used to collect user behavior data, including information on receiving, opening, and clicking messages;

[0177] A tag generation unit is configured to analyze and generate a user's contact channel tag, contact time tag, and contact content tag based on the behavior data, wherein:

[0178] The reach channel label indicates the user's preference for different channels;

[0179] The reach time label indicates the user's activity level in different time periods;

[0180] The access content tags indicate the user's interest preferences for different types of content.

[0181] In the intelligent reach system based on user characteristics, the user portrait module is used to generate user reach channel tags, reach time tags, and reach content tags based on user behavior data. Specifically, the user portrait module generates tags in the following ways:

[0182] Generation of reach channel labels:

[0183] Behavioral data collection: The user profile module collects user behavior data across different engagement channels, including message reception, message open rates, click-through rates, etc. Engagement channels include but are not limited to app push notifications, instant messaging (IM), email, etc.

[0184] Channel Preference Analysis: Analyze user activity and responsiveness across various channels based on collected behavioral data. Count metrics such as the number of message opens, clicks, and response times for each channel.

[0185] Tag Generation: Based on the results of channel preference analysis, we generate a user's reach channel tag. This tag reflects the user's preference for each reach channel, identifies the user's activity level on different channels, and provides a basis for subsequent channel selection.

[0186] Generation of touch time labels:

[0187] Behavioral data collection: Collect user's historical login time, message opening time, active time period and other behavioral data.

[0188] Time Segmentation: Divide a 24-hour day into multiple time periods, such as hourly or half-hourly time periods. For example, time periods A (00:00-00:59), B (01:00-01:59), and so on up to N (23:00-23:59).

[0189] Time preference analysis: Analyze users’ activity in different time periods and count users’ message opening and click rates in different time periods.

[0190] Tag generation: Based on the results of time preference analysis, we generate a user reach time tag. This tag reflects the user's activity level at different times of the day, identifies the time when the user is most likely to respond to messages, and helps the system send messages to users at the most appropriate time.

[0191] Generation of reach content tags:

[0192] Behavioral data collection: Collect user response behavior data to different types of content, including the open rate and click-through rate of content recommended by different algorithms and messages with different copywriting styles.

[0193] Content type classification: Classify the content into different categories based on its nature. For example:

[0194] Job content: regular recommended jobs, browse similar jobs, save similar jobs, search for keyword-matched jobs, quick pitch list, new jobs, etc.

[0195] Content news: industry information, career guidance, workplace topics, etc. (specific content types can be refined according to business needs).

[0196] Content preference analysis:

[0197] Algorithm feature analysis: Analyze user feedback on content recommended by different algorithms, count the user's message opening rate under different algorithm recommendations, and identify the user's preference for content recommended by different algorithms.

[0198] Copy feature analysis: Analyze users' responses to messages with different copy styles and identify users' preferred copy styles (e.g., urgent, lively, positive, etc.).

[0199] Tag Generation: Based on the results of content preference analysis, tags are generated for the user's accessed content, including algorithmic feature tags and text feature tags. These tags reflect the user's preferences for different content types and styles, helping the system provide more personalized content for users.

[0200] Through this process, the user profiling module, based on user behavioral data, details user preferences regarding reach channels, reach times, and reach content. These labels provide crucial insights for the system's subsequent intelligent decision-making, enabling it to select the most appropriate channels, time, and content for message reach based on each user's unique characteristics, thereby improving effective message reach and user satisfaction.

[0201] The implementation of this module requires not only comprehensive collection and detailed analysis of user history, but also the establishment of a dynamically updated tagging system. As user behavior changes, the system can update user profile tags in real time, ensuring the accuracy and timeliness of tags and enabling continuous optimization of intelligent reach strategies.

[0202] Continue to refer to Figure 12 The intelligent reach system based on user characteristics also includes a preference generation module, which is used to generate the user's channel preference characteristics, time preference characteristics, algorithm preference characteristics and copy preference characteristics based on the user's click behavior on the message.

[0203] Among them, reference Figure 15 , the preference generation module includes:

[0204] A channel preference generation unit is used to generate a user's channel preference feature based on the user's click behavior on messages from different reach channels, and iteratively update the feature based on the user's click rate;

[0205] A time preference generation unit is used to generate a user's time preference feature based on the user's click behavior on messages with different reach times, and iteratively update the feature based on the user's message click time;

[0206] An algorithm preference generation unit, configured to generate a user's algorithm preference feature based on the algorithm features in the content package corresponding to the message clicked by the user, and iteratively update the feature based on the user's click-through rate on content recommended by different algorithms;

[0207] The copy preference generation unit is used to generate the user's copy preference features based on the copy features in the content package corresponding to the message clicked by the user, and iteratively update the features based on the user's click rate for different copy styles.

[0208] The specific operation examples are as follows:

[0209] The preference generation module is a key component of this invention. It is responsible for generating user preference profiles for channel, time, algorithm, and content based on their click-through behavior on messages. This module continuously monitors and analyzes user behavior data, iteratively optimizing user preference profiles to provide the system with more precise push strategies. The specific implementation plan is as follows:

[0210] Channel preference generation unit:

[0211] Functional Description: The channel preference generation unit is used to generate a user's channel preference profile based on their message click behavior across different reach channels. This unit can identify user responses to different channels (such as app push, IM messages, emails, etc.) and iteratively update the profile based on the user's click-through rate across each channel.

[0212] -Implementation steps:

[0213] 1. Data Collection: The system monitors users' behavior in receiving, opening, and clicking on messages across different channels. For example, a user may be more likely to receive and open messages via app push and less likely to use email.

[0214] 2. Preference calculation: Based on the user's click-through rate on each channel, the system assigns a corresponding weight to each channel. The higher the weight, the stronger the user's preference for that channel.

[0215] 3. Iterative Updates: Over time, the system will iteratively update a user's channel preference characteristics based on the latest click data. For example, if a user gradually decreases their response to App Push messages and increases their clicks on IM messages, the weight of IM messages will gradually increase.

[0216] -Application scenario: When the system needs to push a message, the channel selection unit will select the optimal contact channel based on the user's current channel preference characteristics to ensure that the message can be delivered to the user through the most appropriate channel.

[0217] Time preference generation unit:

[0218] Functional Description: The Time Preference Generation Unit generates a user's time preference profile based on their click behavior on messages during different time periods. This unit identifies the time periods of the day when users are most active and most likely to open messages, and iteratively updates the profile based on this behavioral data.

[0219] -Implementation steps:

[0220] 1. Data collection: The system records the specific time each time a user clicks on a message. For example, a user may be more likely to open messages between 9:00 AM and 11:00 AM, but less likely to interact in the evening.

[0221] 2. Time period division: The system divides a day into multiple time periods (such as each hour or half hour as a time slot) and counts the user's click behavior in each time period.

[0222] 3. Weight calculation and update: The system assigns weights to each time period based on its click-through rate, and continuously updates this weight based on the latest data. For example, if a user typically opens messages between 8:00 AM and 10:00 AM, this time period will be given a higher weight.

[0223] -Application scenario: When the system needs to push a message, the timing optimization unit will select the optimal sending time based on the user's current time preference characteristics to ensure that the message can be delivered and opened at the most appropriate time.

[0224] Algorithm preference generation unit:

[0225] Functional Description: The algorithm preference generation unit is used to generate a user's algorithm preference profile based on the algorithm features in the content package corresponding to the message clicked by the user. This unit can identify the user's response to different algorithm-recommended content (such as job recommendations, news recommendations, etc.) and iteratively update based on this behavioral data.

[0226] -Implementation steps:

[0227] 1. Algorithm feature extraction: Each push message corresponds to a content package, which contains algorithmic recommendations (such as job matching recommendations, personalized news recommendations, etc.). When a user clicks on a message, the system records the algorithm features used in the content package.

[0228] 2. Response Analysis and Weighting: The system analyzes user responses to different algorithmic recommendations (e.g., high-paying jobs, trending news, etc.) and assigns corresponding weights to each algorithmic feature. For example, if a user is more likely to click on the "high-paying job" recommendation, the algorithmic feature "high salary" will be assigned a higher weight.

[0229] 3. Iterative Updates: As more click data is collected, the system will continuously update the user's algorithmic preference features. For example, if the click-through rate of "hot jobs" increases within a certain period, the weight of this algorithmic feature will gradually increase.

[0230] -Application scenario: When the system needs to push information containing algorithm-recommended content, the content package selection unit will select the content package that best suits the user's interests based on the user's current algorithm preference characteristics to improve the push effect.

[0231] Copy preference generation unit:

[0232] Functional Description: The copy preference generation unit generates a user's copy preference profile based on the copy styles in the content package corresponding to the message the user clicked. By analyzing the impact of different copy styles (such as urgent, lively, and formal) on user behavior, the unit can identify the copy style that best captures the user's attention and iterate accordingly.

[0233] -Implementation steps:

[0234] 1. Copywriting style extraction: The copywriting in each push message has different styles (such as urgent, humorous, formal, etc.). When a user clicks on a message, the system will record the copywriting style used in the content package.

[0235] 2. Response Analysis and Weighting: The system analyzes the impact of different copywriting styles on the user and assigns corresponding weights to each style. For example, if "urgent" style copywriting receives a higher click-through rate during a certain period, the "urgent" style will be assigned a higher weight.

[0236] 3. Iterative Updates: As more data is collected, the system will continuously update the user's response to different copywriting styles. For example, if "humorous" copywriting receives more attention during a certain period, its corresponding weight will gradually increase.

[0237] -Application scenario: When the system needs to push information with copywriting information, the content package selection unit will select the copywriting style that best suits the user's preferences based on the user's current copywriting preference characteristics to improve the push effect.

[0238] Summarize

[0239] Through the above four sub-modules, combined Figure 9 、 Figure 10 and Figure 11, this invention can dynamically generate and continuously optimize the personalized preferences of each target user based on multi-dimensional data analysis. This refined data processing method ensures that each push notification meets the needs of the target group to the greatest extent possible, thereby significantly improving the effectiveness of intelligent reach. In actual application, these modules work together to provide accurate data support for the subsequent factor selection module, making the entire intelligent reach process more efficient and accurate.

[0240] Continue to refer to Figure 12 The intelligent reach system based on user characteristics also includes a factor selection module for calculating the weight of each reach channel, the weight of each reach time, and the weight of each content package based on the user preference characteristics, selecting the optimal combination of reach channel, reach time, and content package, and generating a message sending plan, wherein:

[0241] The factor selection module includes:

[0242] The channel optimization unit is used to calculate the weight of each reach channel based on the user's channel preference characteristics and the normalized click-through rate of the platform channel;

[0243] A content package selection unit is used to match the algorithmic and textual characteristics of the content package with the user's algorithmic and textual characteristics, and calculate the weight of each content package;

[0244] The timing optimization unit is used to calculate the weight of each touch time based on the user's time preference characteristics and the platform time normalized click rate;

[0245] A combining unit, configured to generate a message sending plan that satisfies a preset number of messages by combining the reach channel weight, reach time weight, and content package weight in descending order of weight;

[0246] An execution module is configured to send a corresponding content package to a user at a corresponding reach time and through a corresponding reach channel according to the message sending plan; if the message fails to be sent or does not actually reach the user, a new message sending plan is generated based on the best approach, the reach time is ignored, and the message is resent;

[0247] The user portrait module, the preference generation module and the factor selection module are all connected to the central server through a communication module.

[0248] Among them, reference Figure 15 , the preference generation module includes:

[0249] A channel preference generation unit is used to generate a user's channel preference feature based on the user's click behavior on messages from different reach channels, and iteratively update the feature based on the user's click rate;

[0250] A time preference generation unit is used to generate a user's time preference feature based on the user's click behavior on messages with different reach times, and iteratively update the feature based on the user's message click time;

[0251] An algorithm preference generation unit, configured to generate a user's algorithm preference feature based on the algorithm features in the content package corresponding to the message clicked by the user, and iteratively update the feature based on the user's click-through rate on content recommended by different algorithms;

[0252] The copy preference generation unit is used to generate the user's copy preference features based on the copy features in the content package corresponding to the message clicked by the user, and iteratively update the features based on the user's click rate for different copy styles.

[0253] The Factor Selection Module is one of the core modules of this invention. It is responsible for calculating the weights of various reach channels, reach times, and content packages based on user preferences, selecting the optimal combination and generating a message delivery plan. By comprehensively considering the user's channel preferences, time preferences, algorithm preferences, and copywriting preferences, this module ensures that each message is delivered at the most appropriate time, through the most appropriate channel, and with the content package that best suits the user's interests.

[0254] Channel selection unit:

[0255] Functional Description: The channel optimization unit calculates the weight of each reach channel based on the user's channel preference characteristics and the platform's normalized channel click-through rate. The system analyzes the user's historical click behavior on different channels (such as app push, IM messages, email, etc.) to determine the user's preference for each channel and normalizes it based on the platform's overall channel performance.

[0256] Implementation steps:

[0257] Data collection: The system monitors the user's click behavior on different channels and generates the user's channel preference characteristics based on historical click-through rates.

[0258] Weight calculation: The weight of each channel is calculated based on the user's channel preference characteristics and the overall click-through rate of the platform. For example, if a user prefers to receive messages via App Push, but the click-through rate of IM messages is low, App Push will have a higher weight.

[0259] Iterative update: As more data is collected, the system will continuously update the user's channel preference characteristics and weights to ensure that the push strategy always meets the latest situation.

[0260] Content package selection unit:

[0261] Functional Description: The content package selection unit matches the user's algorithmic and copywriting preferences with the algorithmic and copywriting features in the content package, calculating the weight of each content package. This unit ensures that the content package pushed to the user not only meets their interests but also maximizes click-through rate.

[0262] Implementation steps:

[0263] Algorithm matching: The system first extracts the algorithmic features used in each content package and matches them with the user's algorithmic preference features. For example, if a user has a strong interest in "high-paying job" recommendations, content packages containing such algorithmic features will be given a higher weight.

[0264] Copy matching: The system also analyzes the copy style of each content pack and matches it with the user's copy preference characteristics. For example, if a user is more likely to click on messages with "urgent" copy, content packs with this copy style will be given a higher weight.

[0265] Weight calculation and update: The system assigns corresponding weights to each content package based on the matching results, and continuously updates these weights as more data is accumulated.

[0266] Timing Optimization Unit:

[0267] Functional Description: The Timing Optimization Unit calculates the weights of each reach time period based on the user's time preference characteristics and the platform's time-normalized click-through rate. This unit ensures that push messages are delivered within the most appropriate time period to increase open rates.

[0268] Implementation steps:

[0269] Time period division: The system divides a day into multiple time periods (such as each hour or half hour as a time slot) and counts the user's click behavior in each time period.

[0270] Weight calculation: The system assigns a weight to each time period based on the user's activity level and the overall platform performance during that time period. For example, if a user typically opens messages between 9:00 AM and 11:00 AM, this time period will be given a higher weight.

[0271] Iterative update: As more data is collected, the system will continuously update the user's response in different time periods to ensure accurate push timing.

[0272] Combination unit:

[0273] Functional Description: The combination unit is responsible for generating a complete message delivery plan based on the weights of the three dimensions of reach channel, reach time, and content package, selecting the best combination from high to low. This unit ensures that each push message best meets the needs of the target user.

[0274] Implementation steps:

[0275] Optimal combination generation: The system first selects the highest-weighted combination of reach channel, reach time, and content package to generate the first message.

[0276] Subsequent combination generation: After generating the first message, the system will continue to select the suboptimal combination from the remaining elements to generate the second message, and so on, until the preset number of messages is met.

[0277] Resend mechanism: If a message fails to be delivered or doesn't reach its intended recipient for some reason, the system will re-select and resend it immediately, regardless of time constraints. This resend mechanism ensures that important information is ultimately delivered to its intended recipient.

[0278] Practical application cases

[0279] For example, a recruitment platform wants to push job recommendations to its registered users. Suppose the platform operator configures a smart reach campaign that sends two job recommendation messages to the target audience. The operator provides the following elements:

[0280] Channel selection: APP Push, IM message

[0281] Content package selection: Contains four content packages with different job recommendation algorithms and copywriting styles (such as "High-paying job recommendation" and "Urgent recruitment").

[0282] Time options: 8:00, 9:00, 10:00, 11:00 AM

[0283] User A case:

[0284] User A has a strong preference for app push notifications. He usually opens messages between 9:00 and 11:00 a.m., is particularly interested in recommendations for "high-paying jobs," and tends to click on messages with "urgent" text.

[0285] The system first uses the channel optimization unit to determine that App Push is the most suitable push channel, thus assigning it a higher weight. In the timing optimization unit, since User A is typically active between 9:00 and 11:00, the 9:00 time period is given the highest weight. In the content package optimization unit, since the "high-paying job" recommendation meets the user's algorithm preferences and the "urgent" style copy meets their copywriting preferences, the content package containing these elements receives the highest weight.

[0286] The system finally generates the first message: a message combining "high-paying positions" and "urgent recruitment" will be sent via APP Push at 9:00.

[0287] Next, the system selects the next best combination of remaining elements to generate a second message. Since the 9:00 time slot is already used, the system chooses 10:00 as the next best time, IM as the next best channel, and another content package that is more relevant to the user's interests, but less relevant.

[0288] The second message is: Send another set of job recommendation information via IM at 10:00.

[0289] If the second message fails to be sent successfully due to a network failure, the system will ignore the time factor and reselect the optimal combination for resending, such as immediately sending another set of job recommendation information via IM.

[0290] Results: This intelligent, multi-dimensional factor selection mechanism ensures that each push notification best meets the needs of target users, increasing both open and click-through rates. Furthermore, the automatic resend mechanism further improves the success rate of information delivery and effectively prevents the loss of important information due to technical issues.

[0291] In summary, the factor selection module in this invention provides personalized and precise information push solutions for each target user by comprehensively considering preferences across multiple dimensions (such as channel, timing, and content). This intelligent, multi-dimensional comprehensive evaluation mechanism significantly improves intelligent reach, ensuring that each push maximizes the needs of the target group, thereby increasing overall business conversion rates.

[0292] Among them, continue to refer to Figure 12 The intelligent contact system based on user characteristics also includes a communication module for realizing data interaction between the user portrait module, preference generation module and factor selection module and the central server.

[0293] Reference Figure 13 , the communication module includes:

[0294] A data sending unit is used to send the user contact channel label, contact time label and contact content label generated by the user portrait module, the channel preference feature, time preference feature, algorithm preference feature and copywriting preference feature generated by the preference generation module, and the contact channel weight, contact time weight and content package weight calculated by the factor selection module to the central server;

[0295] A data receiving unit, configured to receive processing instructions and data update information issued by a central server, so that the user profiling module, preference generation module, and factor selection module can process according to the latest instructions and data;

[0296] The communication control unit is used to manage and control the workflow of the data sending unit and the data receiving unit, ensuring the effective transmission and secure communication of data between each module and the central server.

[0297] The communication module plays a key role in this invention, responsible for realizing data interaction between the user profiling module, preference generation module, and factor selection module and the central server. Through this module, the system can effectively transmit user-related data and receive processing instructions and data update information issued by the central server, thereby ensuring the smooth operation of the entire intelligent contact process. Specifically, the communication module includes the following three core units:

[0298] Data sending unit:

[0299] Function description: The data sending unit is responsible for sending the user contact channel labels, contact time labels and contact content labels generated by the user portrait module, the channel preference features, time preference features, algorithm preference features and copywriting preference features generated by the preference generation module, and the contact channel weights, contact time weights and content package weights calculated by the factor selection module to the central server.

[0300] Implementation steps:

[0301] Data packaging: The system first packages the data generated by the above modules, including user portrait information, preference characteristics and factor weights.

[0302] Data transmission: The packaged data is sent to the central server via a secure network protocol (such as HTTPS or WebSocket).

[0303] Transmission confirmation: The system monitors the data transmission process to ensure that the data reaches the central server successfully and retransmits it if necessary.

[0304] Data receiving unit:

[0305] Functional Description: The data receiving unit is used to receive processing instructions and data update information issued by the central server. These instructions and update information will be used to guide the user profiling module, preference generation module, and factor selection module to make processing adjustments.

[0306] Implementation steps:

[0307] Instruction reception: The system receives processing instructions and the latest data update information from the central server through communication protocols.

[0308] Instruction parsing: The received data will be parsed into specific operation instructions (such as updating user portraits, adjusting preference features, etc.).

[0309] Execution feedback: After executing these instructions, the system will feed back the execution results to the central server to ensure the success of the operation and conduct subsequent optimization.

[0310] Communication control unit:

[0311] Function description: The communication control unit is responsible for managing and controlling the workflow of the data sending unit and the data receiving unit, ensuring the effective transmission and secure communication of data between each module and the central server.

[0312] Implementation steps:

[0313] Task scheduling: Arrange data sending and receiving tasks reasonably according to the task priority within the system to avoid conflicts.

[0314] Security assurance: Encryption technology (such as SSL / TLS) is used to ensure that data is not tampered with or leaked during transmission.

[0315] Error handling and retry mechanism: If a network failure or other error occurs during communication, the system will automatically handle the error and try to resend or receive data.

[0316] Practical application cases

[0317] For example, an e-commerce platform wants to push personalized promotional information to its user base. Suppose the platform operator configures a smart reach campaign that sends three promotional messages to the target audience. The operator provides the following elements:

[0318] Channel selection: APP Push, SMS, Email

[0319] Content package selection: Four content packages with different promotion algorithms and copywriting styles (such as "Limited Time Discount", "New Product Recommendation", etc.)

[0320] Time options: 8:00 AM, 10:00 AM, 2:00 PM

[0321] User B case:

[0322] User B has a strong preference for app push notifications. He is usually active between 10:00 AM and 2:00 PM, is particularly interested in "limited-time discount" promotions, and tends to click on messages with "urgent" text.

[0323] Data sending process of the communication module:

[0324] The system first generates the user's reach channel label (APP Push), reach time label (10:00 to 14:00) and reach content label (limited-time discount) through the user portrait module.

[0325] The preference generation module generates user B's channel preference characteristics (APP Push), time preference characteristics (10:00 to 14:00), algorithm preference characteristics (limited-time discounts), and copywriting preference characteristics (urgent style) based on historical click behavior.

[0326] The factor selection module calculates the highest weighted combination of the APP Push channel, the 10:00 time period, and the "limited-time discount + emergency style" content package.

[0327] The system packages these calculation results and sends them to the central server through the data sending unit of the communication module.

[0328] Data receiving process of the communication module:

[0329] The central server analyzes the received data and issues new processing instructions, such as adjusting the push strategy or updating certain content packages.

[0330] The system receives these instructions through the data receiving unit of the communication module and interprets them into specific operations, such as adjusting the push time for user B or changing the push content.

[0331] After the system completes these operations, it feeds back the execution results to the central server.

[0332] Application of the reissue mechanism:

[0333] If a message fails to be successfully sent to User B via App Push at 10:00 as planned due to a network failure, the system will ignore the time factor and immediately send a promotional message via SMS to ensure that important information is not missed.

[0334] Results: This intelligent, multi-dimensional factor selection mechanism, combined with an efficient communication module, ensures that each push notification is tailored to the needs of the target user, increasing both open and click-through rates. Furthermore, the automatic resend mechanism further improves message delivery success rates and effectively prevents the loss of important information due to technical issues. Furthermore, through real-time communication with the central server, the system dynamically adjusts push strategies based on the latest business needs, ensuring the entire intelligent reach process remains optimal.

[0335] In summary, the communication module in this invention provides stable data transmission support for core functions such as user profiling, preference generation, and factor selection through an efficient and secure data exchange mechanism. Practical application cases demonstrate that this communication module not only ensures smooth communication between functional modules but also responds promptly to changing business needs, improving overall message reach through real-time adjustments to push strategies.

[0336] The following reactive power regulation algorithm is used between the communication module and the central server: where r ij (i=1, 2, ..., k; j=1, 2, ..., 5) represents the degree of membership of data transmission between the i-th communication module and the j-th central server.

[0337] The following algorithm formula for the adjustable capacity is used between the communication module and the factor selection module:

[0338]

[0339] in in The adjustable capacity provided for the transmitted data i in time period t, where V i t is the actual transmission speed of the transmitted data i in time period t, where is the upper limit of the regulation of the transmitted data in time period i, where is the lower limit of the data transmitted in time period i, where P it is the actual amount of data bytes transmitted in time period i, where E i is the capacity service provision for data i transmitted, where β is the daily transmission rate of data transmitted.

[0340] In this invention, communication and data transmission between the communication module, the central server, and the factor selection module utilize algorithms for reactive power regulation and adjustable capacity. These algorithms are designed to optimize data transmission efficiency and reliability, ensuring the system can efficiently transmit information under varying network conditions.

[0341] Algorithm formula for reactive power regulation:

[0342] The reactive power regulation algorithm is used to optimize data transmission between communication modules and the central server. The "membership" mentioned here represents the degree of correlation between the data transmission between the i-th communication module and the j-th central server. Reactive power regulation is commonly used in power systems to control reactive power to maintain voltage stability. In this invention, this concept is leveraged to dynamically adjust load distribution during data transmission.

[0343] Membership explained: Membership can be understood as the quality or priority of data transmission between the communication module and the central server. By calculating membership, the system can determine how to allocate resources, ensuring that critical data is transmitted first when the network load is high.

[0344] Practical application: For example, when multiple communication modules simultaneously send data to a central server, the system prioritizes each module based on its degree of affiliation with the server. If a module's data transmission affiliation is high (perhaps because it's processing an important task), the system prioritizes its data request, thereby improving the response time for critical tasks.

[0345] Algorithm formula for adjustable capacity:

[0346] The adjustable capacity algorithm is used to optimize the data transmission between the communication module and the factor selection module. The variables in this formula describe the data transmission in different time periods:

[0347] Practical Application: For example, during peak hours (such as 9:00 AM to 11:00 AM), when network bandwidth may be limited, the system can dynamically adjust the data capacity of each message based on a formula, ensuring that important messages are delivered promptly while less important messages can be sent later or with a reduced capacity. In this way, the system can balance the data load across different time periods and improve overall communication efficiency.

[0348] Practical application cases:

[0349] A large e-commerce platform wanted to push personalized promotional information to its users through an intelligent reach system. Due to its large user base, the platform needed to ensure that important promotional information could be efficiently pushed to its target users during peak periods (such as shopping festivals) while also preventing message delays or loss due to network congestion.

[0350] Reactive power regulation algorithm application:

[0351] Suppose the platform has multiple communication modules responsible for sending key information such as user profiles and preferences to a central server. During a shopping festival, user traffic surges, and each communication module simultaneously sends a large amount of data to the central server. At this time, the reactive power regulation algorithm comes into play:

[0352] - The system calculates the degree of data transmission between each communication module and the central server. If a communication module is processing information for a VIP customer, its degree will be set to a higher value.

[0353] -Based on the degree of affiliation, the system prioritizes data requests related to VIP customers to ensure that these customers can receive promotional information in a timely manner, while data requests from ordinary customers may be processed later.

[0354] Adjustable capacity algorithm application:

[0355] Imagine a platform needs to push different types of promotional information to different user groups. Each message contains a large amount of images and text. During a shopping festival, due to bandwidth constraints, the platform needs to dynamically adjust the data capacity of each message to ensure that key promotional information is delivered in a timely manner.

[0356] The system uses an adjustable capacity formula to calculate the bandwidth available for each message during different time periods. For example, during peak hours between 9:00 AM and 11:00 AM, the system might reduce the data capacity for minor promotional messages (such as general discounts) and allocate more bandwidth to important promotional messages (such as limited-time sales).

[0357] -If a message fails to be sent as planned (for example, due to network congestion), the system will readjust its capacity based on the actual situation and resend it through other channels (such as SMS).

[0358] Results: Through reactive power regulation and an adjustable capacity algorithm, the platform dynamically adjusts resource allocation when network load is high, ensuring that critical tasks are prioritized. Furthermore, by dynamically adjusting the data capacity of each message, the platform effectively utilizes limited bandwidth resources and improves overall communication efficiency. This mechanism not only improves message reach but also avoids the loss or delay of important information due to network congestion, thereby enhancing user experience and business conversion rates.

[0359] The reactive power regulation and adjustable capacity algorithms in this invention provide a flexible and efficient data transmission optimization solution. Through these algorithms, the intelligent reach system can dynamically adjust resource allocation and data transmission strategies based on real-time network conditions, ensuring that important information reaches its intended users in a timely and reliable manner. This mechanism is particularly well-suited for information push in high-concurrency, large-scale user scenarios, effectively improving the overall performance of the intelligent reach system.

[0360] Among them, continue to refer to Figure 12 ,The intelligent reaching system based on user characteristics also includes a central server.

[0361] Reference Figure 16 , the central server includes:

[0362] The data processing unit is used to receive and store data from the communication module, including user portrait tags, user preference characteristics, weights of each contact channel, contact time weights, and content package weights;

[0363] The instruction control unit is used to generate and send processing instructions and data update information to the user profiling module, preference generation module and factor selection module;

[0364] Data analysis unit, used to analyze collected user data and optimize reach strategies and feature tag systems;

[0365] Security management unit, used to ensure the security of data during transmission and processing, and prevent data leakage and illegal access;

[0366] The load balancing unit is used to distribute data transmission tasks among multiple communication modules to improve the stability and efficiency of the system.

[0367] In this intelligent reach system, the central server plays a core role, coordinating data transmission, processing, analysis, and optimization between various modules. Through the management of the central server, the system can efficiently process information such as user profiles, preference characteristics, and factor selection, and adjust push strategies based on real-time data, thereby improving the accuracy and efficiency of message reach. The following are the functions of each unit of the central server and their actual application scenarios:

[0368] Data processing unit

[0369] Functional Description: This unit receives and stores data from the communication module, including user profile tags, user preference characteristics, weights for each reach channel, reach time weights, and content package weights. By storing and managing this data, the system can make more accurate decisions based on historical data in subsequent operations.

[0370] Practical Application:

[0371] Scenario: During promotional activities on an e-commerce platform, the system needs to generate user profiles based on historical user behavior (such as clicks and opens) and store user preferences for different channels, time periods, and content packages. The data processing unit stores this data on a central server for reference during subsequent push notifications.

[0372] Effect: By storing this data, the system can make more accurate decisions based on historical data in future push notifications, thereby improving message reach.

[0373] Command control unit

[0374] Functional Description: The Instruction Control Unit generates and distributes processing instructions and data update information to the User Profiling Module, Preference Generation Module, and Factor Selection Module. It ensures that each module can dynamically adjust based on the latest data and strategies.

[0375] Practical Application:

[0376] Scenario: Assume that the preference characteristics of a certain user group have changed (for example, they start to use AppPush more frequently instead of email). The instruction control unit will send an update instruction to the preference generation module, requiring it to adjust the channel preference of this user group.

[0377] Effect: This dynamic adjustment mechanism ensures that the system can quickly respond to changes based on real-time data, keeping the push strategy in an optimal state.

[0378] Data Analysis Unit

[0379] Functional Description: This unit is responsible for analyzing collected user data to optimize reach strategies and feature tagging systems. By analyzing large amounts of historical data, the system can identify potential trends and adjust push strategies based on these trends.

[0380] Practical Application:

[0381] Scenario: After a major promotional event, the platform wants to understand which push strategies were most effective. The data analytics unit will review and analyze all push behaviors (including open rates, click-through rates, etc.) to optimize reach strategies for future campaigns.

[0382] Effect: Through continuous data analysis, the platform can continuously optimize its push strategy and improve message reach and user interaction rate.

[0383] Security Management Unit

[0384] Functional Description: The security management unit is used to ensure the security of data during transmission and processing, preventing data leakage and illegal access. It ensures that all data involving user privacy is properly protected.

[0385] Practical Application:

[0386] Scenario: An e-commerce platform needs to push personalized promotional information to millions of users. This information contains sensitive user behavior data (such as purchase history and browsing history). The security management unit uses encryption technology to ensure that this data cannot be illegally intercepted or tampered with during transmission.

[0387] Effect: Through strict data security management, the platform can effectively prevent data leakage, enhance user trust, and comply with relevant regulatory requirements (such as GDPR).

[0388] Load balancing unit

[0389] Functional Description: The load balancing unit distributes data transmission tasks among multiple communication modules, improving system stability and efficiency. It ensures that the system can maintain efficient operation even in high-concurrency situations and does not crash or delay due to excessive load.

[0390] Practical Application:

[0391] Scenario: During the Double 11 shopping festival, an e-commerce platform needs to push promotional information to millions of users simultaneously. The load balancing unit distributes tasks to multiple communication modules based on real-time network conditions to avoid overloading any one module.

[0392] Effect: Through the load balancing mechanism, the platform can ensure that personalized messages can be smoothly delivered to each user even during peak hours, thereby improving overall system performance.

[0393] Practical application cases

[0394] Scenario Overview:

[0395] A major e-commerce platform planned to push personalized promotional information to its millions of registered users during the "Double 11" shopping festival. To ensure that every user received promotional content tailored to their interests, the platform implemented the intelligent reach system proposed in this invention, coordinating data exchange and task execution between various modules through a central server.

[0396] Step 1: Data collection and processing

[0397] The user portrait module generates portrait tags for each user, including their most frequently used channels (such as App Push or SMS), most active time periods (such as 9 a.m. to 11 a.m.), and their interest in different types of promotional content (such as "limited-time discounts" or "new product recommendations").

[0398] The data processing unit receives and stores these portrait tags and preference features to provide a basis for subsequent push.

[0399] Step 2: Instruction issuance and strategy adjustment

[0400] Before a campaign begins, the command control unit issues commands based on real-time data. For example, if the App Push channel becomes congested during a certain period, the unit will issue a command to the factor selection module to prioritize SMS push.

[0401] Step 3: Real-time analysis and optimization

[0402] The data analysis unit continuously monitors the open and click-through rates of each message. If a certain type of promotional content is found to be underperforming (e.g., a "new product recommendation" has a low open rate), the system will automatically adjust the subsequent push strategy, shifting more resources to the better-performing "limited-time discount" promotional content.

[0403] Step 4: Security

[0404] Throughout the entire process, the security management unit uses encryption technology to ensure that all data related to user privacy (such as purchase records) will not be illegally intercepted or tampered with, thereby protecting user privacy.

[0405] Step 5: Load balancing and efficient transmission

[0406] During the peak period of Singles' Day (Singles' Day), with millions of messages being sent simultaneously, the load balancing unit distributed tasks across multiple communication modules to avoid network congestion and delays. This ensured that every user received personalized promotional information in a timely manner.

[0407] Through the various functional units within the central server, this invention achieves efficient coordination and optimization of the intelligent message delivery process. Whether it's data collection and storage, real-time command delivery, or security and load balancing, these functional units collaborate to improve the performance and stability of the entire intelligent delivery system. In practical applications, this architecture not only improves message reach but also significantly optimizes the user experience, resulting in higher business conversion rates for enterprises.

[0408] The present invention also relates to an intelligent reaching method based on user characteristics, comprising the following steps:

[0409] S1, user profile generation: obtaining user behavior data, and generating user contact channel tags, contact time tags, and contact content tags based on the behavior data;

[0410] S2, preference feature generation: Based on the user's click behavior on the message, the user's channel preference feature, time preference feature, algorithm preference feature and copy preference feature are generated;

[0411] S3, factor weight calculation:

[0412] Calculate the weight of each reach channel based on the user's channel preference characteristics and the normalized click-through rate of the platform channel;

[0413] Calculate the weight of each touch time based on the user's time preference characteristics and the platform's time-normalized click-through rate;

[0414] Match the user's algorithm preference features and copywriting preference features with the algorithm features and copywriting features of the content package, and calculate the weight of each content package;

[0415] S4, optimal combination selection: Based on the calculated reach channel weight, reach time weight, and content package weight, the optimal combination is selected from high to low weights to generate a message sending plan that meets the preset number of messages;

[0416] S5, message sending: according to the message sending plan, at the corresponding reach time, through the corresponding reach channel, send the corresponding content package to the user;

[0417] S6, resending mechanism: If a message fails to be sent or does not actually reach the user, a new message delivery plan is generated based on the best approach, ignoring the reach time and immediately resending the message;

[0418] S7, data interaction: Data interaction with the central server is realized through the communication module, user portraits, preference characteristics and weight information are sent, and processing instructions and data update information from the central server are received to update and optimize the processing of each module.

[0419] Methods for improving the effective reach of smart messages aim to address the low reach and inaccurate push notifications inherent in existing technologies. With the rapid development of the internet and smart message delivery technologies, the nature of push notifications has dramatically changed. Traditional fixed-channel and fixed-time push notifications are no longer able to meet the personalized needs of users, resulting in a decline in message reach. Existing technologies lack refined control over the smart message delivery process, particularly in scenarios with multiple message pushes, and are unable to automatically select the optimal channel, time, and content combination for users.

[0420] Traditional smart message push methods typically require configuring multiple independent tasks for each campaign. Each task identifies target users based on tags and sends messages to them via fixed channels, content packages, and timeframes. When multiple messages need to be sent to the same target user group, operators must manually create multiple campaigns to determine the delivery of different content via different channels at different times. This approach presents the following issues:

[0421] - Lack of automation: Each push requires human intervention, and push strategies cannot be dynamically adjusted based on users' real-time behavior.

[0422] - Low push efficiency: Multiple messages require the creation of multiple campaigns, increasing operational costs and complexity.

[0423] - Lack of precision: Unable to automatically select the optimal reach channel, time, and content combination based on user preferences, resulting in poor push results.

[0424] This invention introduces intelligent and automated message reach strategies to achieve dynamic management of user profiles, preference characteristics, and factor selection, thereby significantly improving the effective reach of smart messages. Specific innovations are as follows:

[0425] Automated multi-dimensional selection mechanism:

[0426] The system can provide multiple channels, multiple content packages, and multiple time points within a single campaign. It automatically calculates the weight of each factor based on historical user behavior data (such as click-through rate and open rate), and sequentially selects the optimal, suboptimal, and suboptimal combinations to generate a complete messaging plan. Compared to traditional methods, this method eliminates the need to manually create multiple campaigns. Instead, the system automatically selects the most appropriate reach plan for each user.

[0427] Real-time feedback and dynamic adjustment:

[0428] The system dynamically updates user profiles and preferences based on feedback from each push (such as open rates and click-through rates). Through this iterative optimization mechanism, the system continuously improves the accuracy of its push strategies, ensuring that each push is tailored to the user's current needs.

[0429] Reissue mechanism:

[0430] If a message fails to be delivered or reaches the user due to network failure or other reasons, the system will re-generate a new delivery plan, ignoring time factors, and resend it immediately. This resending mechanism ensures that important information will not miss the best delivery opportunity due to unexpected factors.

[0431] By reviewing and summarizing large-scale marketing recommendations in ToC scenarios, the present invention can achieve the following technical effects:

[0432] - Accurate crowd prediction: Based on data analysis of all registered users, the system can automatically predict the optimal crowd and its preferences, thereby achieving more accurate target crowd selection.

[0433] -Intelligent channel and content recommendation: The system can recommend the most appropriate contact channel and content package combination for each target user based on business needs, thereby improving conversion rate.

[0434] -Efficient operation support: Through automation and multi-dimensional optimization mechanism, this invention greatly reduces the workload of operators to manually configure tasks, while improving push efficiency and effectiveness.

[0435] The present invention is implemented in three steps through a refined strategy:

[0436] Layered portrait label generation:

[0437] The system first generates layered profile tags based on user behavior data, including access channel tags (such as App Push, IM message, and email), access time tags (based on historical login habits), and access content tags (based on historical open rates). These tags provide data support for subsequent preference generation and factor selection.

[0438] Preference generation and strategy optimization:

[0439] Based on users' click-through rates on historical messages, the system dynamically generates and updates their channel preference, time preference, algorithm preference, and content preference. By iteratively optimizing these preferences, the system continuously adjusts its push strategy, ensuring that each push is tailored to the user's current needs.

[0440] Factor selection and combination generation:

[0441] Based on user profiles and preferences, the system weights the multiple channels, content packages, and timeframes provided, selecting the optimal combination to generate a complete messaging plan. For example, when sending three promotional messages to a target user group, the system will prioritize the "Limited Time Discount" message sent via app push at 9:00 AM. It will then select the next best combination from the remaining elements to generate the second message, and so on until the preset number of messages is met.

[0442] Reissue mechanism and dynamic adjustment:

[0443] If a message fails to be delivered or does not reach the target user, the system will re-generate a new delivery plan, ignoring time factors, and immediately resend it. For example, if the 9:00 AM push notification fails, the system may choose to send another promotional message via IM at 10:00 AM as a replacement.

[0444] This invention achieves precise control over the intelligent message delivery process by introducing a multi-dimensional intelligent optimization mechanism. Compared to traditional approaches, this not only improves message reach but also reduces the workload of operators for manual task configuration, significantly improving operational efficiency. Furthermore, through real-time feedback and dynamic adjustments, this invention continuously optimizes push strategies, providing enterprises with more efficient and accurate information delivery solutions.

[0445] For the above method, apply the following:

[0446] like Figure 1 As shown in the diagram, the intelligent reach system architecture shows a decision service system based on a rules engine (Drools), designed to automate decision execution and rule management. The system automates business logic by interacting with intelligent services and running the rules engine. The following is a detailed analysis of each module in the architecture diagram:

[0447] Intelligent Services

[0448] Function: The intelligent service module is responsible for receiving external requests or triggering events and passing these requests to the decision service for processing. This can be an event triggered by user behavior or a system scheduled task.

[0449] Function: As the entrance to the entire system, the intelligent service passes the data to be processed to the decision execution module, which processes it according to preset rules.

[0450] Decision Service (Drools)

[0451] Function: The decision service is the core of the entire system, which makes automated decisions based on business logic based on the rule engine (Drools). It includes multiple sub-modules:

[0452] Decision execution module: Receives data from intelligent services and performs corresponding actions based on the output of the rule engine. It is responsible for calling the rule engine to process business logic and returning the results to the intelligent service.

[0453] Rule engine module: This is the core module implemented based on Drools, responsible for reasoning and calculating input data according to predefined business rules to obtain decision results.

[0454] Rule Management Module: This module manages and maintains business rules, supporting dynamic loading, updating, and deleting of rules. It interacts with the database (PgSQL) and cache (Redis) to ensure that the latest business rules take effect promptly.

[0455] Decision service management terminal: provides a management interface for operators or administrators to view, modify and configure business rules.

[0456] Data storage

[0457] Redis: Used to cache business rules and improve query efficiency. In practical applications, Redis can store commonly used or frequently accessed rules to reduce the pressure of direct queries on the database.

[0458] PgSQL: Used to persistently store all business rules and related data. All business rules are stored in PgSQL after creation or update, so that they can be loaded into the rule engine for execution when needed.

[0459] Rule Management

[0460] Function: This module manages all business rules, including creation, modification, and deletion. Operations personnel can maintain these rules through the decision service management terminal. All updated rules are synchronized to the Redis cache to improve system access speed.

[0461] Function: This module enables the system to flexibly adjust business logic without modifying the code. This design greatly improves the flexibility and maintainability of the system.

[0462] Decision execution

[0463] Function: When the intelligent service sends a request, the decision execution module calls the Drools engine to perform judgments and calculations based on the currently effective business rules and returns the results to the intelligent service. The decision execution module also interacts with Redis and PgSQL to ensure that the latest business rules are used.

[0464] Function: This module is the part of the entire system that actually executes the business logic. It combines input data with existing rules to produce corresponding decision results.

[0465] like Figure 2The flowchart shown in Figure 1 illustrates the message push process triggered by behavioral events in the Smart Reach system. Through the collaboration of the decision service and the rules engine, the system automatically selects the optimal activity, time, and content for message push based on user behavior. The following is a detailed analysis of each component of the flowchart: Behavioral Event Service: Function: The Behavioral Event Service module is responsible for receiving user behavioral data in the system and determining whether these behaviors meet the triggering conditions for Smart Reach activities. Process Description: Message Receiving: When a user performs certain actions on the platform (such as browsing, clicking, and purchasing), the system captures this behavioral data and passes it as a message to the Behavioral Event Service. Retrieving Activity ID and Details: The Behavioral Event Service retrieves the corresponding activity ID based on the user's behavior and queries the activity's detailed information. Determining Whether It's a Smart Reach Activity: The system determines whether the action is a Smart Reach activity. If so, the subsequent steps will be executed; if not, the process ends. Smart Reach Service: Function: The Smart Reach Service module is responsible for processing eligible behavioral events and, through the rules engine, selects the optimal push strategy (such as the best activity and time). Process Description: Consuming Topic Messages: When a behavioral event is identified as a Smart Reach activity, the Smart Reach service consumes the corresponding Topic message and enters the Rule Factory class for processing. Rule Factory Processing: The Rule Factory class contains various rule types (such as custom rules, market rules, user preference rules, polling rules, etc.). The system analyzes user behavior based on these rules and determines the optimal push strategy. Invoking the Decision Service: Based on the analysis results of the Rule Factory class, the Smart Reach service invokes the Decision Service to further optimize the push strategy. Decision Service (Rule Engine): Function: The Decision Service module runs predefined business logic based on a rule engine (such as Drools) to ultimately determine the optimal push solution. Process Description: Returning the Optimal Activity ID and Time: The Decision Service uses the rule engine to calculate the activity ID and optimal push time that best suits the current user and returns the results to the Smart Reach Service. Push Execution: Based on the decision result, the system sends the corresponding message to the user, completing the entire push process. Push Execution: Function: After the decision is completed, the system generates a specific message and actually pushes it through the traffic framework or marketing engine. Process Description: Based on the decision results, the system will generate a message containing the optimal activity, time point, and content combination, and send it to the target users through preset channels (such as App Push, SMS, etc.). This architecture achieves efficient and accurate information reach by capturing user behavior and dynamically adjusting the push strategy based on preset rules. It can not only respond quickly based on real-time user behavior, but also supports flexible adjustment of business logic, making the entire system highly automated and flexible. In actual applications, this architecture is suitable for scenarios that require real-time response to user behavior and personalized recommendations or marketing push, such as personalized recommendations on e-commerce platforms and risk warnings on financial platforms.

[0466] like Figure 3 As shown in the figure, the entity relationship diagram of the intelligent reach system shows the relationship between multiple core entities, mainly including content packages, intelligent reach activities, reach content, and reach configuration. The following is a detailed analysis of each entity and its relationship:

[0467] Content Package

[0468] property:

[0469] id: A unique identifier used to distinguish different content packages.

[0470] Name: The name of the content pack, describing the purpose or type of the content pack.

[0471] Activity ID: The activity ID associated with the content package, indicating which smart reach activity the content package belongs to.

[0472] Data source type: describes the data source type of the content package, such as whether it is obtained from an internal database or an external interface.

[0473] Status: The status of the current content pack (such as enabled, disabled, etc.).

[0474] Version number: used to identify the version information of the content package for easy management and updating.

[0475] Creation Time / Update Time: Records the time when the content pack was created and last updated.

[0476] relation:

[0477] There is a one-to-many relationship between Content Packages and Content Package Details. Each Content Package can have multiple details, such as the specific content to be delivered and the type of delivery channel.

[0478] Content Package Details

[0479] property:

[0480] id: A unique identifier used to distinguish different content package details.

[0481] Content pack id: The content pack ID associated with it, indicating which content pack this detail belongs to.

[0482] Send content: The specific message or information to be sent to the user.

[0483] Sending channel type: describes the channel through which the message is sent (such as AppPush, SMS, email, etc.).

[0484] Status: The status of the current detail item (such as enabled or disabled).

[0485] Version number / creation time / update time: used to manage the version and time information of the detail item.

[0486] relation:

[0487] There is a one-to-many relationship between a content pack detail and the content packs it belongs to.

[0488] Smart Reach Activity

[0489] property:

[0490] Activity ID: A unique identifier used to distinguish different smart reach activities.

[0491] Event Name: A descriptive name for the event, usually reflecting the purpose or theme of the event.

[0492] Basic information of the activity: Contains some basic descriptions of the activity, such as the target user group, push strategy, etc.

[0493] relation:

[0494] Smart reach activities are associated with multiple entities under them, including reach content and reach configuration.

[0495] Reach Content

[0496] property:

[0497] Activity ID: Associated with the smart reach activity, indicating which activity the reach content belongs to.

[0498] Activity content: specific information or messages to be pushed to users.

[0499] Content tags / algorithm tags: used to mark the specific tags or algorithm recommendation features contained in the message so that the system can match it according to user preferences.

[0500] relation:

[0501] Each smart reach activity can contain multiple reach contents, and each reach content is associated with the corresponding smart reach activity.

[0502] Reach Configuration

[0503] property:

[0504] Activity ID: Associated with the Smart Reach activity, indicating which activity the configuration belongs to.

[0505] Audience ID: Indicates the target user group for this configuration.

[0506] Data source information / algorithm data source information / frequency configuration information: used to describe information such as the data source, algorithm recommendation rules, and message push frequency involved in the configuration.

[0507] relation:

[0508] Each smart reach campaign can have multiple reach configurations to define how to execute push strategies for different audience groups.

[0509] The process of using this method:

[0510] 1) If Figure 5 As shown, it schematically shows the user group that you want to reach based on user characteristics.

[0511] 2) If Figure 6 As shown, it schematically shows the configuration of the reach strategy, the selection of pre-fetched channel elements, the total number of items, and whether each element is prioritized based on user preferences.

[0512] 3) If Figure 7 The figure below schematically illustrates the provision of multiple content packages, each consisting of an algorithm and content. Each content package should include a set of content for each intended channel. After creation, the content package's content characteristics must be defined.

[0513] 4) If Figure 8 It schematically shows the setting of the contact date and provides multiple contact times for selection.

[0514] The above is only a specific embodiment of the present application, but the scope of protection of the present application is not limited to this. Any person skilled in the art of message push technology can easily conceive of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. An intelligent reaching system based on user characteristics, characterized by: User portrait module, used to generate user contact channel tags, contact time tags, and contact content tags based on user behavior data; The preference generation module is used to generate the user's channel preference characteristics, time preference characteristics, algorithm preference characteristics, and copywriting preference characteristics based on the user's click behavior on the message; The factor selection module is used to calculate the weight of each contact channel, the weight of each contact time, and the weight of each content package based on the user's preference characteristics, select the optimal combination of contact channel, contact time, and content package, and generate a message sending plan, where: The factor selection module includes: The channel optimization unit is used to calculate the weight of each reach channel based on the user's channel preference characteristics and the normalized click-through rate of the platform channel; A content package selection unit is used to match the algorithmic and textual characteristics of the content package with the user's algorithmic and textual characteristics, and calculate the weight of each content package; The timing optimization unit is used to calculate the weight of each touch time based on the user's time preference characteristics and the platform time normalized click rate; A combining unit, configured to generate a message sending plan that satisfies a preset number of messages by combining the reach channel weight, reach time weight, and content package weight in descending order of weight; An execution module is configured to send a corresponding content package to a user at a corresponding reach time and through a corresponding reach channel according to the message sending plan; if the message fails to be sent or does not actually reach the user, a new message sending plan is generated based on the best approach, the reach time is ignored, and the message is resent; Among them, the user portrait module, the preference generation module and the factor selection module are all connected to the central server through a communication module.

2. The user-feature-based intelligent contact system according to claim 1, characterized in that: The communication module is used to implement data interaction between the user portrait module, preference generation module and factor selection module and the central server, and the communication module includes: A data sending unit is used to send the user contact channel label, contact time label and contact content label generated by the user portrait module, the channel preference feature, time preference feature, algorithm preference feature and copywriting preference feature generated by the preference generation module, and the contact channel weight, contact time weight and content package weight calculated by the factor selection module to the central server; A data receiving unit, configured to receive processing instructions and data update information issued by a central server, so that the user profiling module, preference generation module, and factor selection module can process according to the latest instructions and data; The communication control unit is used to manage and control the workflow of the data sending unit and the data receiving unit, ensuring the effective transmission and secure communication of data between each module and the central server.

3. The user-feature-based intelligent contact system according to claim 2, characterized in that: The following reactive power regulation algorithm is used between the communication module and the central server: ,in Indicates the i The communication module and j The degree of data transmission between central servers.

4. The user feature-based intelligent contact system according to claim 3, characterized in that: The following algorithm formula for adjustable capacity is used between the communication module and the factor selection module: ， ， in, For the data transmitted i In the period t Provides adjustable capacity, For the data transmitted i The actual conveying speed in time period t, For data transmitted in time period i The upper limit of adjustment, For data transmitted in time period i The lower limit of adjustment, For data transmitted in time period i The actual amount of data bytes, For the data transmitted i The capacity service supply, β is the daily transmission rate of the transmitted data.

5. The user-feature-based intelligent contact system according to claim 4, characterized in that: The user portrait module includes: Behavior data collection unit, used to collect user behavior data, including receiving, opening, and clicking on messages; A tag generation unit is configured to analyze and generate a user's contact channel tag, contact time tag, and contact content tag based on the behavior data, wherein: The reach channel label indicates the user's preference for different channels; The reach time label indicates the user's activity level in different time periods; The access content tags indicate the user's interest preferences for different types of content.

6. The user feature-based intelligent contact system according to claim 5, characterized in that: The preference generation module includes: A channel preference generation unit is used to generate a user's channel preference feature based on the user's click behavior on messages from different reach channels, and iteratively update the feature based on the user's click rate; A time preference generation unit is used to generate a user's time preference feature based on the user's click behavior on messages with different reach times, and iteratively update the feature based on the user's message click time; An algorithm preference generation unit, configured to generate a user's algorithm preference feature based on the algorithm features in the content package corresponding to the message clicked by the user, and iteratively update the feature based on the user's click-through rate on content recommended by different algorithms; The copy preference generation unit is used to generate the user's copy preference features based on the copy features in the content package corresponding to the message clicked by the user, and iteratively update the features based on the user's click rate for different copy styles.

7. The user feature-based intelligent contact system according to claim 6, characterized in that: The central server includes: The data processing unit is used to receive and store data from the communication module, including user portrait tags, user preference characteristics, weights of each contact channel, contact time weights, and content package weights; The instruction control unit is used to generate and send processing instructions and data update information to the user profiling module, preference generation module and factor selection module; Data analysis unit, used to analyze collected user data and optimize reach strategies and feature tag systems; Security management unit, used to ensure the security of data during transmission and processing, and prevent data leakage and illegal access; The load balancing unit is used to distribute data transmission tasks among multiple communication modules to improve the stability and efficiency of the system.

8. An intelligent reaching method based on user characteristics, characterized in that: The following steps are involved: S1: User profile generation: Obtain user behavior data and generate user contact channel tags, contact time tags, and contact content tags based on the behavior data; S2: Preference feature generation: Based on the user's click behavior on the message, the user's channel preference feature, time preference feature, algorithm preference feature, and copy preference feature are generated; S3: Factor weight calculation: Calculate the weight of each reach channel based on the user's channel preference characteristics and the normalized click-through rate of the platform channel; Calculate the weight of each touch time based on the user's time preference characteristics and the platform's time-normalized click-through rate; Match the user's algorithm preference features and copywriting preference features with the algorithm features and copywriting features of the content package, and calculate the weight of each content package; S4: Optimal combination selection: Based on the calculated reach channel weight, reach time weight, and content package weight, the optimal combination is selected from high to low weights to generate a message sending plan that meets the preset number of messages. S5: Message sending: According to the message sending plan, the corresponding content package is sent to the user at the corresponding reach time through the corresponding reach channel; S6: Resend mechanism: If a message fails to be sent or does not actually reach the user, a new message delivery plan is generated based on the best approach, ignoring the reach time and immediately resending the message; S7: Data interaction: Realize data interaction with the central server through the communication module, send user portraits, preference characteristics and weight information, receive processing instructions and data update information from the central server to update and optimize the processing of each module.

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